Cost-effectiveness of screening type 2 diabetes patients for chronic kidney disease progression with the CKD273 urinary peptide classifier as compared to urinary albumin excretion

Cost-effectiveness of screening type 2 diabetes patients for chronic kidney disease progression... Abstract Background In type 2 diabetes mellitus (T2DM) patients, chronic kidney disease (CKD) progression may occur without detectable changes in urinary albumin excretion (UAE) rate. A new urinary peptide classifier (CKD273) has exhibited greater ability to detect CKD progression, however, its cost-effectiveness remains unknown. This study evaluated the cost-effectiveness of screening for CKD progression with the CKD273 classifier, as compared to UAE, in diabetic patients. Methods A decision-analytic Markov model was developed to estimate costs and health outcomes [including overall survival and quality-adjusted life years (QALYs)] from a health system perspective for adopting a new annual screening strategy based on the CKD273 classifier as compared to annual UAE-based screening in a hypothetical cohort of T2DM patients. High-risk patients were defined as T2DM patients with at least one concomitant risk factor (i.e. patients with background genetic risk for developing the disease, obesity, hypertension and/or smoking history) for developing diabetic nephropathy secondary to cardiovascular disease (CVD)-related complications. Low-risk T2DM patients, were defined as those not having any of the aforementioned concomitant risk factors. Results Over the projected course of a patient’s lifetime, in all T2DM patients annual screening with the CKD273 classifier was more costly, but also more effective, than annual screening with UAE. The incremental costs incurred with screening based on the CKD273 classifier were €3,053 per patient, while patients gained 0.13 QALYs. Hence, in all patients, annual screening with the CKD273 classifier was cost effective [incremental cost-effectiveness ratio (ICER) €23,903/QALY gained], notably below current government thresholds for funding such health care interventions. For patients at high risk of developing diabetic nephropathy secondary to CVD-related complications, screening based on the CKD273 classifier was cost-saving (i.e. dominant, being both more effective and less expensive than UAE-based screening). Finally, in low-risk patients, CKD273 classifier-based screening was not cost effective (ICER €73,140/QALY) given current government willingness-to-pay thresholds. Conclusions In diabetic patients, annual CKD273 classifier-based screening is more costly but also more effective in QALYs gained as compared to UAE. From a health provider perspective, the observed benefits are greatest when such screening is implemented in patients at high risk for diabetes-associated renal or cardiovascular diseases (CVDs). chronic kidney disease, cost-effectiveness, diabetes mellitus type 2, prognostic biomarker, proteomics INTRODUCTION Diabetic nephropathy as well as hypertension constitute the leading causes of chronic kidney disease (CKD), which afflicts approximately one-tenth of the adult population in high-income countries [1, 2], albeit with notable variations in country-specific prevalence rates [3]. CKD may result in end-stage kidney disease (ESKD) with consequent diminution of patients’ quality of life and survival [4]. In 2002 alone, international registries reported 1 million patients with ESKD globally [5]. It is expected that by 2025, type 2 diabetes mellitus (T2DM) will affect >300 million patients worldwide, with CKD-attributable mortality rates increasing by 50% [6] from ∼1.5 million deaths in 2012 [7]. The health care–associated costs of CKD swell geometrically with advancing disease stages. In the USA, total CKD costs incurred by Medicare patients were estimated to be in the range of US$80 billion, including US$30 billion for CKD Stage 5 and US$50 billion for CKD Stages 1–4 [8]. Assuming similar disease distributions and health care–associated costs in the European Union (EU), these findings would infer that the costs of CKD in the EU approximate to €120 billion annually, as also reported in a recent publication wherein the authors estimated that the costs of CKD in Europe are likely to exceed €100 billion [9]. Findings of the AusDiab Study indicate that the cost of CKD increases in parallel with the severity of renal dysfunction [10]. Due to increasing prevalence rates of T2DM and its complications [11], CKD is an emerging concern for clinicians and health care policymakers alike. To improve health outcomes in T2DM patients, while concurrently reducing associated health care costs, investigations are increasingly dedicated to developing novel means of facilitating the timely detection of CKD onset as well as the consequent prevention of CKD progression and disease complications [12, 13]. Patients at elevated risk for CKD progression may receive intensive medical management so as to deter or delay ESKD, the necessity for dialysis and/or death [14]. CKD diagnosis is currently based on the detection of urinary albumin excretion (UAE) rates and/or a reduction in estimated glomerular filtration rate (eGFR). In addition, the combination of UAE and >5% annual reduction in eGFR are indicative of progressive CKD [15]. Gender does not appear to mediate the performance of either approach in relation to CKD onset or ESKD [16]. However, despite the absence of detectable UAE [17, 18] and/or microalbuminuria [19], CKD nevertheless progresses in a notable proportion of T2DM patients. Thus the clinical necessity of an accurate test that complements current medical approaches in predicting CKD progression, and consequently facilitates clinical decision making, remains inadequately addressed to date [18]. Recently, a capillary electrophoresis–mass spectrometry (CE-MS)-based urinary peptide classifier, namely the CKD273 classifier, was developed [20]. The diagnostic usefulness of the CKD273 classifier has been evaluated in multiple independent studies, including case–control [20] and longitudinal [21] investigations, and has since been proposed as a potentially clinically applicable predictive marker of CKD progression [21, 22]. Even so, its potential applicability in clinical practice ought to be considered in light of its cost-effectiveness [23] in relation to both health care costs [18, 24] and patients’ quality of life and health outcomes, including quality-adjusted survival [25]. While proteomics testing is expensive, urinary proteome testing with markers such as the CKD273 classifier is projected to decrease overall lifetime health care costs in T2DM patients [24]. It is hypothesized that, due to its higher sensitivity, the CKD273 classifier will detect diabetic patients at risk for CKD progression with greater accuracy [26] as compared with current standard approaches such as UAE [27]. As a result, screening with the CKD273 classifier may facilitate the pre-emptive initiation of intensified therapy [including additional antihypertensive therapy, angiotensin-converting enzyme (ACE) inhibitor or spironolactone] among T2DM patients at risk for CKD progression and hence deter both disease complications and attributable mortality in a cost-effective manner. However, the cost-effectiveness of screening T2DM patients for CKD progression using the CKD273 classifier as compared to UAE has not been evaluated to date. Therefore, the primary study objective is to evaluate the cost-effectiveness, from a European health care system perspective, of screening T2DM patients for CKD progression using the CKD273 classifier as compared to screening annually with UAE over a 40-year time frame. MATERIALS AND METHODS A Markov model was developed using TreeAge Pro 2008 software (TreeAge Software, Williamstown, MA, USA) to simulate the cost-effectiveness, from a European health care provider perspective, of annual screening for CKD progression using the CKD273 classifier as compared to annual screening with UAE in a hypothetical cohort of prevalent T2DM patients. The hypothetical cohort included patients of both genders, 50 years of age at baseline [28] and residing in Europe. High-risk patients were defined as those patients with at least one concomitant risk factor (i.e. patients with background genetic risk for developing the disease, obesity, hypertension and/or smoking history) for developing diabetic nephropathy secondary to cardiovascular disease (CVD)-related complications. Low-risk patients were defined as those not having any of the aforementioned concomitant risk factors. The current standard strategy for assessing CKD in T2DM patients, namely the UAE test, was considered an appropriate comparator in this model, as it represents standard care in most countries. Screening for CKD with the CKD273 classifier has a sensitivity of 94.6% and specificity of 97.1% [26]. The sensitivity of the UAE test is 70.0% and specificity is 71.0% [27]. In our hypothetical cohort, both tests were applied at baseline and annually thereafter in all patients until either CKD progression (micro- or macroalbuminuria and/or diabetic nephropathy) or any of the health outcomes (ESKD, dialysis, renal transplantation or death) under evaluation occurred. Additionally, simulations were also conducted wherein screening based on the peptide classifier was conducted in patients at either low or high risk for CVD-related complications. Model structure The analysis was conducted and reported according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement [29]. The Markov model was constructed to include all potential consequences of CKD progression, including diabetic nephropathy, kidney transplantation, graft failure, dialysis and death attributable either to CKD or CVDs (Supplementary data, Figure S1). Based on screening test results, diabetic patients either received modified intensified therapy and/or advanced through the health states under evaluation (namely, further disease progression, kidney transplantation, graft failure, dialysis and/or death). Intensified therapy, as compared with standard therapy, included additional antihypertensive therapy, ACE inhibitors or spironolactone. Specifically, the hypothetical cohort of normoalbuminuric T2DM patients was screened at baseline with both the CKD273 classifier and the UAE. Positive screening findings from either approach entailed normoalbuminuric patients receiving standard antihypertensive therapy transitioning to intensified therapy so as to deter the onset and/or progression of CKD. The efficacy of standard antihypertensive therapy is assumed to be 88.0% [30]; additionally, efficacy based on empirical reports with modified therapy is approximated at 68.0%. Following baseline screening, both CKD273 classifier and UAE screening were conducted annually until any of the health outcomes of interest occurred. The possible model pathways included the following: (i) At baseline, normoalbuminuric patients, receiving standard antihypertensive treatment and having negative screening results either remained event free or, despite screening results, transitioned to more advanced stages of CKD. Normoalbuminuric patients with positive screening results transitioned to receiving intensified therapy and either remained event free or transitioned to more advanced stages of CKD, despite early intervention. Both patient groups could die due to any cause or CVD. Surviving normoalbuminuric patients without CKD progression were retested annually thereafter with either the CKD273 classifier or the UAE until the aforementioned health events or death occurred. For surviving patients, a utility quality-adjusted life year (QALY) for no ESKD was determined for each year of survival {utility QALY 0.82, [95% confidence interval (CI) 0.74–0.90]} [31]. (ii) Microalbuminuric patients with negative screening results continued standard therapy and either remained event free or transitioned to macroalbuminuria (diabetic nephropathy). Microalbuminuric patients with positive screening results were administered intensified therapy and either remained event free or, despite treatment, transitioned to macroalbuminuria (diabetic nephropathy). The likelihood to progress between these latter two states was mediated by the effectiveness of intensified treatment. Alternatively, both patient groups could die from CVD or other causes. (iii) Patients with diabetic nephropathy either remained event free or died due to CVD or other causes. Surviving diabetic nephropathy patients either remained event free or transitioned to ESKD and/or, pending the efficacy of therapy, dialysis. Patients with ESKD and on dialysis could either die due to CVD or other causes or alternatively could transition to kidney transplantation. Patients could either stay on maintenance dialysis or die on dialysis. Finally, in order to assess differential impacts of utilizing annual CKD273-based screening in selected target population groups, the aforementioned simulations were replicated separately in low- and high-risk patients for diabetes-associated renal and cardiovascular complications. As detailed in Table 1, the risk of events, transition probabilities and outcomes were primarily based on the United Kingdom Prospective Diabetes Study (UKPDS 64) findings [32] and published reports [11, 33], given a CKD prevalence rate of 11% [34]. A time horizon of 40 years (maximum expected patient lifetime after developing T2DM) was applied for the comprehensive consideration of extended effects of identifying patients at risk for CKD progression upon further disease progression, kidney transplantation, graft failure, dialysis and death attributable either to CKD or CVD [35, 36]. Outcomes were expressed in terms of overall survival [life years (LYs)] and quality-adjusted survival (QALYs). Table 1 Annual probability of transitions, outcomes and utilities applied in the cost-effectiveness analysis, according to diabetic patients’ risk for developing complications   Probabilities and utilities     All patients  Patients at low risk for complications  Patients at high risk for complications  Transition variables   Baseline progression from normo- to microalbuminuria  0.05  0.033  0.075   Annual transition from normo- to microalbuminuria  0.020 [32]  0.013  0.030   Annual transition from micro- to macroalbuminuria  0.03 [32]  0.020  0.045   Annual transition from diabetic nephropathy to end-stage renal disease (dialysis)  0.040 [33]  0.040  0.040   Projected (2012–25) annual increase in the prevalence of CKD Stage 5  0.032 [11]  0.021  0.048  Outcome variables   No CKD progression    CV mortality in normoalbuminuric patients  0.007 [32]  0.005  0.010    Non-CV mortality in normoalbuminuric patients  0.007 [32]  0.007  0.007   CKD progression    CV mortality in microalbuminuric patients  0.020 [32]  0.013  0.030    Non-CV mortality in microalbuminuric patients  0.010 [32]  0.010  0.010    CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.035 [32]  0.035  0.035    Non-CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.011 [32]  0.011  0.011   Dialysis    CV mortality  0.121 [32]  0.121  0.121    Non-CV mortality  0.071 [32]  0.071  0.071  QALY parameters         Utility following renal transplantation  0.76 [39, 40]  0.76  0.76   Utility following dialysis  0.48 [40]  0.48  0.48   Utility no ESKD  0.88 [40]  0.88  0.88    Probabilities and utilities     All patients  Patients at low risk for complications  Patients at high risk for complications  Transition variables   Baseline progression from normo- to microalbuminuria  0.05  0.033  0.075   Annual transition from normo- to microalbuminuria  0.020 [32]  0.013  0.030   Annual transition from micro- to macroalbuminuria  0.03 [32]  0.020  0.045   Annual transition from diabetic nephropathy to end-stage renal disease (dialysis)  0.040 [33]  0.040  0.040   Projected (2012–25) annual increase in the prevalence of CKD Stage 5  0.032 [11]  0.021  0.048  Outcome variables   No CKD progression    CV mortality in normoalbuminuric patients  0.007 [32]  0.005  0.010    Non-CV mortality in normoalbuminuric patients  0.007 [32]  0.007  0.007   CKD progression    CV mortality in microalbuminuric patients  0.020 [32]  0.013  0.030    Non-CV mortality in microalbuminuric patients  0.010 [32]  0.010  0.010    CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.035 [32]  0.035  0.035    Non-CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.011 [32]  0.011  0.011   Dialysis    CV mortality  0.121 [32]  0.121  0.121    Non-CV mortality  0.071 [32]  0.071  0.071  QALY parameters         Utility following renal transplantation  0.76 [39, 40]  0.76  0.76   Utility following dialysis  0.48 [40]  0.48  0.48   Utility no ESKD  0.88 [40]  0.88  0.88  Health outcomes The health states used in the Markov model included (i) T2DM (without CKD), (ii) T2DM with progressive stages of CKD, (iii) ESKD, (iv) dialysis, (v) renal transplantation and (vi) death. Recent meta-regression analyses indicate that each 10% incremental reduction of UAE is associated with a reduction in CVD-attributable events [37]. Likelihood rates of the health states assessed were retrieved from the UKPDS 64 report [32]. Based on the most conservative estimates arising from the UKPDS 74 [38], we assumed a 33% reduction in the risk of the investigated health outcomes in the low-risk group and a 1.5-fold increase of the corresponding values in the high-risk group (Table 1). Quality of life Decrements in quality of life were associated with diabetic complications, progression of CKD to ESKD and initiation of dialysis. Utility estimates for the QALY calculation were obtained from literature reports [39, 40] (Table 1). These values represent preference-based quality of life on an interval scale, where 0 represents death and 1 represents perfect health [41]. Resource use and costs All costs were valued in 2015 euros (€) or UK pounds (£; to which 2015-based euro conversions were applied) based on either recommended retail prices or reports of health care associated costs from a health system perspective [11, 33, 42] (Table 2). All costs include expenses incurred per annual frequency of testing and/or treatment, as well as physician and hospitalization costs. Costs of intensified therapy included drug costs and physician visits. Incremental cost-effectiveness ratios (ICERs) were calculated. Based on recent reports, governments of high-income countries are more likely to fund health care interventions with an ICER of less than approximately €45,000 (US$50,000) per QALY gained [43, 44], while UK National Health Service (NHS) thresholds are set at the lowest level approximating €25,600 (£20,000) per QALY gained [45]. Table 2 Cost estimations applied in the cost-effectiveness analysis of screening type 2 diabetic patients with the CKD273 peptide classifier as compared to UAE Description  References  Cost incurreda (euros, 2015)  Annual UAE testing  [Recommended retail price]  36  Annual CKD273 peptide classifier testing  [Recommended retail price]  866  Annual treatment costs for diabetic nephropathy  [11]  5987  Annual costs for dialysis  [42]  52 968  Annual cost of standard therapy (additional antihypertensive therapy in patients with known CKD)  [33]  857  Annual cost of modified therapy (ACE inhibitors), without standard therapy, in patients with known CKD  [33]  378  Annual cost of intensive therapy (ACE inhibitors and spironolactone), without standard therapy, in patients with known CKD  [33]  420  Description  References  Cost incurreda (euros, 2015)  Annual UAE testing  [Recommended retail price]  36  Annual CKD273 peptide classifier testing  [Recommended retail price]  866  Annual treatment costs for diabetic nephropathy  [11]  5987  Annual costs for dialysis  [42]  52 968  Annual cost of standard therapy (additional antihypertensive therapy in patients with known CKD)  [33]  857  Annual cost of modified therapy (ACE inhibitors), without standard therapy, in patients with known CKD  [33]  378  Annual cost of intensive therapy (ACE inhibitors and spironolactone), without standard therapy, in patients with known CKD  [33]  420  a Costs incurred include physician visits and hospitalization costs. Allowance for uncertainty All variables in the model were evaluated with one-way sensitivity analyses using an upper and lower bound for each cost and effect parameter, including the probability of kidney disease progression. Deterministic one-way sensitivity analysis findings were used to identify the variables that influenced the cost-effectiveness results. Finally, probabilistic sensitivity analysis was conducted based on Monte Carlo simulations with 1000 iterations, to assess the effects of uncertainty upon the overall confidence of the base case model findings. RESULTS Screening based on the CKD273 classifier compared to UAE Overall in T2DM patients, annual screening for CKD progression with the CKD273 classifier compared with UAE was more costly but also more effective in relation to QALYs gained (Table 3). Specifically, the total costs per patient incurred by annual screening with the CKD273 classifier was €57,083, as compared to €54,030 resulting from annual screening with the UAE over the course of 40 years. The incremental costs incurred by screening with the CKD273 classifier were €3,053 per patient. Additionally, patients screened with the CKD273 classifier had better survival and greater QALYs than those undergoing UAE-based screening. In particular, patients undergoing screening based on the CKD273 classifier gained 24.26 QALYs as compared to 24.13 QALYs for those undergoing UAE-based screening. Hence, patients undergoing CKD273 classifier-based screening gained 0.13 QALYs, predominantly due to the slowing of CKD progression and diabetic complications, as well as better quality of life. The ICER of screening based on the CKD273 classifier as compared to UAE was €23,903 per QALY gained, which is lower than the UK NHS threshold costs per QALY gained. Table 3 Cost-effectiveness analysis from a European health system perspective of annually screening type 2 diabetic patients with the CKD273 peptide classifier as compared to UAE over a 40-year time period, according to patient risk of developing diabetic nephropathy secondary to CVD-related complications Target population for screening strategy  Total cost per patient over 40 years (mean €, 2015)  Incremental cost (€, 2015)  Total effectiveness per patient over 40 years (mean QALY)  Incremental effectiveness (QALY)  ICER (€/QALY gained)  All patients             UAE  54 030  0  24.13  0  0   CKD273 peptide classifier  57 083  3053  24.26  0.13  23 903  Patients at low risk for CVD-related complications             UAE  48 159  0  26.27  0  0   CKD273 peptide classifier  54 305  6146  26.36  0.08  73 140  Patients at high risk for CVD-related complications             UAE  61 649  0  21.00  0  0   CKD273 peptide classifier  60 949  −700  21.17  0.17  Dominant  Target population for screening strategy  Total cost per patient over 40 years (mean €, 2015)  Incremental cost (€, 2015)  Total effectiveness per patient over 40 years (mean QALY)  Incremental effectiveness (QALY)  ICER (€/QALY gained)  All patients             UAE  54 030  0  24.13  0  0   CKD273 peptide classifier  57 083  3053  24.26  0.13  23 903  Patients at low risk for CVD-related complications             UAE  48 159  0  26.27  0  0   CKD273 peptide classifier  54 305  6146  26.36  0.08  73 140  Patients at high risk for CVD-related complications             UAE  61 649  0  21.00  0  0   CKD273 peptide classifier  60 949  −700  21.17  0.17  Dominant  The risk of developing diabetic nephropathy and associated complications can be predicted to some extent by certain clinical risk factors. In order to assess differential impacts of utilizing annual CKD273-based screening in selective population groups, simulations were also conducted in low- and high-risk patients, as defined above. As shown in Table 3, in low-risk patients, annual screening for CKD progression with the CKD273 classifier as compared with UAE was more costly but more effective in terms of QALYs gained. The ICER in this patient group was €73,140 per QALY gained, notably exceeding National Institute for Health and Care Excellence thresholds [45]. In contrast, in high-risk patients, annual screening for CKD progression with the CKD273 classifier was cost-saving, i.e. less costly and more effective (known as ‘dominant’) in relation to QALYs gained. Specifically, CKD273-based screening reduced cost by €700 per patient, while concomitantly contributing to the attainment of 0.17 QALYs. Sensitivity analyses In order to explore which variables had the greatest impact on expected costs, ICER tornado diagrams were constructed (Figure 1). The assumed willingness to pay (WTP) for the cost of the test was set at ∼10% greater than current UAE costs (€40). The ICER tornado diagram in high-risk populations (Figure 1) indicated that the factors that most profoundly affected the calculated expected value of the cost-effectiveness analysis included the annual probability of disease progression, efficacy of modified therapy for CKD management, mortality attributable to dialysis and annual expected increase in the prevalence of Stage 5 CKD. Deterministic one-way sensitivity analysis indicated that with increasing probability of transition from micro- to macroalbuminuria (i.e. CKD progression), the CKD273 classifier-based screening contributed to a greater gain in life years in T2DM patients as compared to UAE-based screening (Figure 2). Finally, probabilistic sensitivity analysis suggested that, assuming a WTP threshold of €45,000 (Supplementary data, Figure S2, Panel A), annual screening with the CKD273 classifier in T2DM patients is more cost effective than annual screening with UAE in 80.9% of the simulated events. When additionally the application of a more conservative WTP threshold of €25,600 was assumed (Supplementary data, Figure S2, Panel B), annual screening with the CKD273 classifier remained the most cost-effective approach in 52.2% of simulated events. FIGURE 1 View largeDownload slide ICER tornado diagram for the cost-effectiveness analysis from a European health provider perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening with UAE, over a 40-year time frame. The ICER tornado diagram indicates that the factors that most profoundly affect the calculated expected value (EV) of the cost-effectiveness analysis include the annual probability of disease progression, the efficacy of modified therapy for CKD management, the mortality attributable to dialysis and the annual expected increase in the prevalence of Stage 5 CKD. FIGURE 1 View largeDownload slide ICER tornado diagram for the cost-effectiveness analysis from a European health provider perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening with UAE, over a 40-year time frame. The ICER tornado diagram indicates that the factors that most profoundly affect the calculated expected value (EV) of the cost-effectiveness analysis include the annual probability of disease progression, the efficacy of modified therapy for CKD management, the mortality attributable to dialysis and the annual expected increase in the prevalence of Stage 5 CKD. FIGURE 2 View largeDownload slide One-way sensitivity analyses for the cost-effectiveness from a European perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening annually with UAE over a 40-year time frame. The analysis indicates that with increasing probability of annual CKD progression, the CKD273 classifier–based screening (as compared to UAE-based screening) was associated with a greater gain in QALYs in diabetic patients. FIGURE 2 View largeDownload slide One-way sensitivity analyses for the cost-effectiveness from a European perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening annually with UAE over a 40-year time frame. The analysis indicates that with increasing probability of annual CKD progression, the CKD273 classifier–based screening (as compared to UAE-based screening) was associated with a greater gain in QALYs in diabetic patients. Model validation Both internal and external validation of the constructed models was conducted according to the International Society for Pharmacoeconomics and Outcomes Research–Society for Medical Decision Making Modeling Good Research Practices report [46]. Internal validation was conducted independently by two individual researchers (R.M. and E.C.) in relation to model pathways, verification of individual equations and parameters and accurate implementation of the model assumptions. Due to limited data availability regarding CKD273 classifier use at the population level, external validation of the control arm of the analysis (namely, use of UAE in T2DM patients for CKD progression screening) was conducted in relation to survival. Data and modelling procedures from the UKPDS were used to study this effect [47]. The third-order validation conducted [48] revealed that the present model predicted a life expectancy within the age range of that reported by the UKPDS-OM2 simulation [remaining life expectancy of T2DM patients 50–54 years of age at base case: 25.1 years (95% CI 24.5–25.7)] [47]. DISCUSSION The present study findings indicate that annual screening of all T2DM patients for CKD progression with the CKD273 urinary peptide classifier was more costly but also more effective in terms of QALYs gained than annual screening with UAE. Specifically, in all T2DM patients, annual screening with the CKD273 classifier as compared to annual screening with UAE was cost effective (ICER €23,903/QALY gained) over the projected course of a patient’s lifetime. This is notably below current government thresholds for funding such health care interventions in most high-income countries, including Europe and the USA [43, 44]. The observed benefits from a European health provider perspective of utilizing CKD273-based screening was most profound when such a screening strategy was implemented in patients at high risk for developing diabetic nephropathy secondary to CVD-related complications. In particular, in high-risk patients, screening based on the CKD273 classifier was cost-saving (i.e. dominant), being both more effective and less expensive than UAE-based screening. In contrast, in low-risk patients, screening based on the CKD273 classifier was not cost effective (ICER €73,140/QALY) given current government WTP thresholds. Therefore, screening particularly high-risk T2DM patients with the CKD273 classifier appears to be a preferable cost-saving approach as compared to UAE. Particularly in high-income countries, the continuously increasing prevalence rates of both T2DM and CKD [11] mandate the early detection of patients likely to develop CKD progression and diabetic complications [12, 13]. It is already established that in patients at elevated risk for disease progression, the timely administration of modified intensified therapy can deter the necessity for long-term dialysis, ESKD and death, enhancing patients’ longevity and quality of life [4]. Moreover, from a European health provider perspective, recent findings indicate that diabetic patients who experience CKD progression incur 85% higher health care costs than their non-CKD counterparts [10]. Recent reports corroborate that the timely detection of diabetic patients who are most likely to develop CKD progression is of pivotal importance for initiating modified intervention therapy and deterring both CKD progression and diabetic complications, including ESKD [12, 49]. While current standard medical approaches utilize the combination of UAE and >5% annual reduction in eGFR as indicative of CKD progression [15], initial investigations have indicated that their combined performance is inferior to that of the recently developed CKD273 classifier [17, 18, 26, 27]. In particular, while recent meta-regression analyses indicate that incremental reductions of UAE are significantly associated with a reduction in CVD-attributable events [including myocardial infarction (−13%) and stroke (−29%), as well as overall composite CVD-attributable outcome, including death, myocardial infarction and stroke (−14%)], it is not associated with overall and CVD-attributable mortality [37]. However, the relationship of albumin:creatinine ratio to the relative risk of all-cause mortality and cardiovascular mortality displays log hazard ratios increasing linearly with increasing log albumin:creatinine ratio [50, 51]. In contrast, based on the Oxford Evidence-Based Levels, high evidence levels exist [22] that the CKD273 classifier is useful for predicting the occurrence of micro- or macroalbuminuria in T2DM patients [52, 53] and that it improves CKD risk prediction beyond eGFR and albuminuria [16]. A recent systematic review of investigations evaluating the cost-effectiveness of proteinuria-based screening in diabetic patients revealed that the ICERs ranged from US$5,298 to US$54,943 per QALY. Moreover, the most prominent determinants of cost-effectiveness of such a screening strategy were the incidence of CKD, rate of disease progression and effectiveness of treatment [54]. While not directly comparable due to the differences in the projected time horizons applied for the analyses, the present study findings indicate that in T2DM patients, the CKD273 classifier incurs higher incremental costs than UAE. However, patients gained 0.13 more QALYs than those under undergoing UAE-based screening. In absolute terms and from a national health provider standpoint [18, 24], the CKD273 classifier-based screening strategy is cost effective [23]. However, cost-effectiveness could not be demonstrated in the low-risk population, while screening based on the CKD273 classifier has a notably favourable value for cost, particularly in patients at highest risk for diabetes-related complications, demonstrating not only cost-effectiveness, but even savings. Furthermore, the notable increase in life years gained support from the consideration of its use for augmenting high-risk diabetic patients’ quality of life [25]. Finally, since patients with even mild stages of CKD are more likely to succumb to causes other than diabetic complications [55], the use of an accurate predictor of CKD progression at the earliest stage of disease is mandated. Therefore, as compared to annual screening with UAE, the adoption of screening high-risk patients for CKD progression with the CKD273 classifier appears to be a favourable approach for both health care costs and diabetic patients’ quality of life. Recent reports propose that UAE screening conducted at longer time intervals may be more cost effective, particularly in population groups at high risk for CKD progression [28, 56]. We explored the cost-effectiveness of implementing screening with the CKD273 classifier at longer time intervals, including every second, third and fifth year. Screening with the CKD273 classifier every second, third and fifth year was less expensive, but also less effective, than annual screening with UAE (Supplementary data, Table S1). In particular, the efficacy of screening was observed to decrease with increasing screening time intervals. It is posited that the diminished efficacy of infrequent screening strategies may be potentially attributed to the costs incurred by delayed diagnosis and timely referral to specialist services for deterring further progression to ESKD [57]. Hence, to secure the timely detection and referral of patients at risk for CKD progression and consequent initiation of modified therapy, it appears preferable, from both a patient and health care provider perspective, to conduct screening with the CKD273 classifier on an annual basis. Furthermore, we hypothesized that once the CKD273 classifier was widely adopted as a screening method, a notable reduction in its retail cost could be anticipated. Based on the model assumptions and sensitivity analysis, we assumed that following widespread use the CKD273 classifier would decrease in cost from €650 to €450 (Supplementary data, Table S1). In particular, in the latter hypothetical scenario, screening T2DM patients for CKD progression with the CKD273 classifier was dominant (i.e. less costly and more effective) than screening with UAE. Specifically, the incremental costs incurred by screening with UAE (as compared to the CKD273 classifier) were €2,177. The effectiveness of the CKD273 classifier was superior to that of UAE (CKD273 classifier versus UAE: 24.26 versus 24.13 QALYs gained) and patients had better survival and QALYs, gaining 0.13 QALYs. Screening with the CKD273 classifier had a markedly favourable value for cost as the ICER of UAE was −€17,043 per life year gained. Therefore, the cost-effectiveness of utilizing the CKD273 classifier in CKD progression screening is anticipated to be even greater once it is widely adopted in clinical practice and, by extent, its retail cost is notably reduced. Finally, although T2DM patients account for approximately one-third of patients requiring chronic renal replacement therapy, CKD is common to both type 1 and type 2 diabetes [58]. In particular, while the prevalence rate of diabetic nephropathy from type 1 diabetes has fallen during recent decades [59], findings from the recent UK National Diabetes Audit indicate that only 57.7% and 67.6% of type 1 and type 2 diabetic patients, respectively, are normoalbuminuric [60]. Furthermore, following adjustment for confounding effects, the prevalence of CKD in type 1 diabetics is similar to that of their type 2 counterparts [61]. Hence, the prevention of CKD remains a public health concern in type 1 and type 2 diabetic patients alike. The renoprotective effects particularly of ACE inhibition and angiotensin II receptor blockers in microalbuminuric patients for delaying progression to diabetic nephropathy has been documented in both type 1 and type 2 diabetic patients [62]. Therefore, it is anticipated that, based on the present study findings, screening of type 1 diabetic patients with the CKD273 classifier could facilitate the timely initiation of intensified therapy and consequent determent of diabetic nephropathy. Strengths and limitations The present analysis is the first of its kind to simulate the incremental costs and incremental benefits of screening diabetic patients for CKD progression with the CKD273 classifier as compared to UAE. Additionally, the simulation has utilized the highest quality of evidence to retrieve values of transition, outcomes and costs. Furthermore, the simulation was both internally and externally validated. However, it should be noted that randomized trials for estimating survival rates are not available to date. Finally, the cost-effectiveness analysis included an extended time horizon of 40 years. In normoalbuminuric patients, diabetic nephropathy may develop following a median period of 19 years, while ESKD may develop after a median of ∼9 years thereafter. Hence, the median period of normoalbuminuric patients to develop ESKD is ∼30 years [32]. Therefore, the time horizon of 40 years utilized for the present study simulation seems to be appropriate. Even so, the study findings should be considered with caution, as they arise from simulations in a hypothetical cohort of prevalent T2DM patients. Also, background rates have been derived from the UKPDS 64 and thus may vary from current prevalence rates [63]. However, since the applied rates remain notably lower than those of other developed countries such as the USA [64], any variations in rates introduced would likely only bias the study findings towards the null hypothesis. In addition, costs are observed from a European health and social care perspective. Specifically, risk of events/outcomes are based on the UKPDS 64 (with conversion to euros applied for the purposes of the present analysis), while costs incurred for events/outcomes were taken from the most recent published peer-reviewed scientific literature. Therefore, estimations are optimally calculated and extrapolated to the UK NHS system. Despite the external validations conducted to verify the model simulation, extrapolation of findings may be compromised for other national health care settings. Implications for future research The simulation conducted indicated that annually screening patients with T2DM for CKD progression with the CKD273 urinary peptide classifier was more costly but also more effective for QALYs gained than annual screening with UAE. The observed benefits from a European health provider perspective of utilizing CKD273-based screening were most profound when such screening was implemented in patients at high risk for developing diabetes-associated complications. Observational studies are necessary to validate the present study findings and to evaluate the potential impact of such screening practices on minimizing health care costs attributable to other comorbidities, including CVD, in high-risk patient groups. GLOSSARY Dominant: The intervention under evaluation incurs less costs and is at least as effective if not more effective than the comparator. Incremental cost-effectiveness ratio (ICER): The ratio of the difference in costs and the difference in health outcomes (effects). Markov model: A type of decision analytic model that allows for the transfer between mutually exclusive health states over an extended time period. Monte Carlo simulation: The random selection of values from a given distribution of model parameters, used in a probabilistic approach to calculate the 95% confidence range around the ICER. Sensitivity analysis: Approach for evaluating the robustness of an economic model by evaluating the changes in results obtained when key parameters or scenarios are varied over a specified range. Utility: A quality of life weight on a 0–1 scale (death to full health) used in the calculation of quality-adjusted life years (QALYs). SUPPLEMENTARY DATA Supplementary data are available online at http://ndt.oxfordjournals.org. ACKNOWLEDGEMENTS The authors would like to thank Ferdinand Bahlman, MD and Leif Fluehe, MSc for their contributions in retrieving and compiling data regarding the use of the CKD273 urinary peptide classifier at the population level. V.S.S. contributed to the compilation of the manuscript on behalf of the ERA-EDTA Registry, which is an official body of the ERA-EDTA (European Renal Association–European Dialysis and Transplant Association). FUNDING This work was supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreements 279277 (PRIORITY), grant agreement 601933 (TransBioBC) and grant agreement 608332 (iMODE-CKD). CONFLICT OF INTEREST STATEMENT None declared. The results presented in this paper have not been published previously in whole or part, except in abstract format. REFERENCES 1 Jha V, Garcia-Garcia G, Iseki K et al.   Chronic kidney disease: global dimension and perspectives. Lancet  2013; 382: 260– 272 Google Scholar CrossRef Search ADS PubMed  2 Alebiosu CO, Ayodele OE. The global burden of chronic kidney disease and the way forward. Ethn Dis  2005; 15: 418– 423 Google Scholar PubMed  3 Bruck K, Stel VS, Gambaro G et al.   CKD prevalence varies across the European general population. J Am Soc Nephrol  2016; 27: 2135– 2147 Google Scholar CrossRef Search ADS PubMed  4 Tonelli M, Wiebe N, Culleton B et al.   Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol  2006; 17: 2034– 2047 Google Scholar CrossRef Search ADS PubMed  5 Lysaght MJ. Maintenance dialysis population dynamics: current trends and long-term implications. J Am Soc Nephrol  2002; 13(Suppl 1): S37– S40 Google Scholar PubMed  6 World Health Organization. Diabetes. Fact sheet 312. World Health Organization, 2008. 7 World Health Organization. Global Health Estimates: Deaths by Cause, Age, Sex and Country, 2000-2012 . Geneva: World Health Organization, 2014. 8 Honeycutt AA, Segel JE, Zhuo X et al.   Medical costs of CKD in the Medicare population. J Am Soc Nephrol  2013; 24: 1478– 1483 Google Scholar CrossRef Search ADS PubMed  9 Nkuipou-Kenfack E, Zurbig P, Mischak H. The long path towards implementation of clinical proteomics: exemplified based on CKD273. Proteomics Clin Appl  2017; 11: 5– 6 10 Wyld ML, Lee CM, Zhuo X et al.   Cost to government and society of chronic kidney disease stage 1-5: a national cohort study. Intern Med J  2015; 45: 741– 747 Google Scholar CrossRef Search ADS PubMed  11 Kainz A, Hronsky M, Stel VS et al.   Prediction of prevalence of chronic kidney disease in diabetic patients in countries of the European Union up to 2025. Nephrol Dial Transplant  2015; 30(Suppl 4): iv113– iv118 Google Scholar CrossRef Search ADS PubMed  12 Levey AS, Coresh J. Chronic kidney disease. Lancet  2012; 379: 165– 180 Google Scholar CrossRef Search ADS PubMed  13 Levey AS, Atkins R, Coresh J et al.   Chronic kidney disease as a global public health problem: approaches and initiatives – a position statement from Kidney Disease Improving Global Outcomes. Kidney Int  2007; 72: 247– 259 Google Scholar CrossRef Search ADS PubMed  14 Wouters OJ, O'Donoghue DJ, Ritchie J et al.   Early chronic kidney disease: diagnosis, management and models of care. Nat Rev Nephrol  2015; 11: 491– 502 Google Scholar CrossRef Search ADS PubMed  15 Levey AS, Eckardt KU, Tsukamoto Y et al.   Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int  2005; 67: 2089– 2100 Google Scholar CrossRef Search ADS PubMed  16 Nitsch D, Grams M, Sang Y et al.   Associations of estimated glomerular filtration rate and albuminuria with mortality and renal failure by sex: a meta-analysis. BMJ  2013; 346: f324 Google Scholar CrossRef Search ADS PubMed  17 Miller WG, Bruns DE, Hortin GL et al.   Current issues in measurement and reporting of urinary albumin excretion. Clin Chem  2009; 55: 24– 38 Google Scholar CrossRef Search ADS PubMed  18 Mullen W, Delles C, Mischak H. Urinary proteomics in the assessment of chronic kidney disease. Curr Opin Nephrol Hypertens  2011; 20: 654– 661 Google Scholar CrossRef Search ADS PubMed  19 Krolewski AS. Progressive renal decline: the new paradigm of diabetic nephropathy in type 1 diabetes. Diabetes Care  2015; 38: 954– 962 Google Scholar CrossRef Search ADS PubMed  20 Good DM, Zurbig P, Argiles A et al.   Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics  2010; 9: 2424– 2437 Google Scholar CrossRef Search ADS PubMed  21 Siwy J, Schanstra JP, Argiles A et al.   Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy. Nephrol Dial Transplant  2014; 29: 1563– 1570 Google Scholar CrossRef Search ADS PubMed  22 Critselis E, Lambers Heerspink H. Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression. Nephrol Dial Transplant  2016; 31: 249– 254 Google Scholar PubMed  23 Horvath AR, Lord SJ, St John A et al.   From biomarkers to medical tests: the changing landscape of test evaluation. Clin Chim Acta  2014; 427: 49– 57 Google Scholar CrossRef Search ADS PubMed  24 Mischak H, Delles C, Klein J et al.   Urinary proteomics based on capillary electrophoresis-coupled mass spectrometry in kidney disease: discovery and validation of biomarkers, and clinical application. Adv Chronic Kidney Dis  2010; 17: 493– 506 Google Scholar CrossRef Search ADS PubMed  25 Ioannidis JP, Khoury MJ. Improving validation practices in "omics" research. Science  2011; 334: 1230– 1232 Google Scholar CrossRef Search ADS PubMed  26 Molin L, Seraglia R, Lapolla A et al.   A comparison between MALDI-MS and CE-MS data for biomarker assessment in chronic kidney diseases. J Proteomics  2012; 75: 5888– 5897 Google Scholar CrossRef Search ADS PubMed  27 Mogensen CE. How to protect the kidney in diabetic patients: with special reference to IDDM. Diabetes  1997; 46(Suppl 2): S104– S111 Google Scholar CrossRef Search ADS PubMed  28 Hoerger TJ, Wittenborn JS, Segel JE et al.   A health policy model of CKD: 2. The cost-effectiveness of microalbuminuria screening. Am J Kidney Dis  2010; 55: 463– 473 Google Scholar CrossRef Search ADS PubMed  29 Husereau D, Drummond M, Petrou S et al.   Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. BMJ  2013; 346: f1049 Google Scholar CrossRef Search ADS PubMed  30 Farmer AJ, Stevens R, Hirst J et al.   Optimal strategies for identifying kidney disease in diabetes: properties of screening tests, progression of renal dysfunction and impact of treatment – systematic review and modelling of progression and cost-effectiveness. Health Technol Assess  2014; 18: 1– 128 Google Scholar CrossRef Search ADS PubMed  31 Wyld M, Morton RL, Hayen A et al.   A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. PLoS Med  2012; 9: e1001307 Google Scholar CrossRef Search ADS PubMed  32 Adler AI, Stevens RJ, Manley SE et al.   Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney Int  2003; 63: 225– 232 Google Scholar CrossRef Search ADS PubMed  33 Manns B, Hemmelgarn B, Tonelli M et al.   Population based screening for chronic kidney disease: cost effectiveness study. BMJ  2010; 341: c5869 Google Scholar CrossRef Search ADS PubMed  34 Jha V, Wang AY, Wang H. The impact of CKD identification in large countries: the burden of illness. Nephrol Dial Transplant  2012; 27(Suppl 3): iii32– iii38 Google Scholar PubMed  35 Pippias M, Jager KJ, Kramer A et al.   The changing trends and outcomes in renal replacement therapy: data from the ERA-EDTA Registry. Nephrol Dial Transplant  2015; 36 Gonzalez-Espinoza L, Ortiz A. 2012 ERA-EDTA Registry Annual Report: cautious optimism on outcomes, concern about persistent inequalities and data black-outs. Clin Kidney J  2015; 8: 243– 247 Google Scholar CrossRef Search ADS PubMed  37 Savarese G, Dei Cas A, Rosano G et al.   Reduction of albumin urinary excretion is associated with reduced cardiovascular events in hypertensive and/or diabetic patients. A meta-regression analysis of 32 randomized trials. Int J Cardiol  2014; 172: 403– 410 Google Scholar CrossRef Search ADS PubMed  38 Retnakaran R, Cull CA, Thorne KI et al.   Risk factors for renal dysfunction in type 2 diabetes: U.K. Prospective Diabetes Study 74. Diabetes  2006; 55: 1832– 1839. Google Scholar CrossRef Search ADS PubMed  39 Kennedy-Martin T, Paczkowski R, Rayner S. Utility values in diabetic kidney disease: a literature review. Curr Med Res Opin  2015; 31: 1271– 1282 Google Scholar CrossRef Search ADS PubMed  40 Lung TW, Hayes AJ, Hayen A et al.   A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation. Qual Life Res  2011; 20: 1669– 1678 Google Scholar CrossRef Search ADS PubMed  41 Torrance GW. Measurement of health state utilities for economic appraisal. J Health Econ  1986; 5: 1– 30 Google Scholar CrossRef Search ADS PubMed  42 Roggeri DP, Salomone M. Chronic kidney disease: evolution of healthcare costs and resource consumption from predialysis to dialysis in Piedmont region, Italy. Adv Nephrol  2014; 2014: 1– 6 Google Scholar CrossRef Search ADS   43 Pearson SD, Rawlins MD. Quality, innovation, and value for money: NICE and the British National Health Service. JAMA  2005; 294: 2618– 2622 Google Scholar CrossRef Search ADS PubMed  44 Morton RL, Howard K, Webster AC et al.   The cost-effectiveness of induction immunosuppression in kidney transplantation. Nephrol Dial Transplant  2009; 24: 2258– 2269 Google Scholar CrossRef Search ADS PubMed  45 National Institute for Health and Care Excellence. The Guidelines Manual . London: NICE, 2012. 46 Eddy DM, Hollingworth W, Caro JJ et al.   Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Value Health  2012; 15: 843– 850 Google Scholar CrossRef Search ADS PubMed  47 Hayes AJ, Leal J, Gray AM et al.   UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia  2013; 56: 1925– 1933 Google Scholar CrossRef Search ADS PubMed  48 McCabe C, Dixon S. Testing the validity of cost-effectiveness models. Pharmacoeconomics  2000; 17: 501– 513 Google Scholar CrossRef Search ADS PubMed  49 Schievink B, Kropelin T, Mulder S et al.   Early renin-angiotensin system intervention is more beneficial than late intervention in delaying end-stage renal disease in patients with type 2 diabetes. Diabetes Obes Metab  2016; 18: 64– 71 Google Scholar CrossRef Search ADS PubMed  50 van der Velde M, Matsushita K, Coresh J et al.   Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int  2011; 79: 1341– 1352 Google Scholar CrossRef Search ADS PubMed  51 Astor BC, Matsushita K, Gansevoort RT et al.   Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int  2011; 79: 1331– 1340 Google Scholar CrossRef Search ADS PubMed  52 Zurbig P, Jerums G, Hovind P et al.   Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes  2012; 61: 3304– 3313 Google Scholar CrossRef Search ADS PubMed  53 Roscioni SS, de Zeeuw D, Hellemons ME et al.   A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus. Diabetologia  2013; 56: 259– 267 Google Scholar CrossRef Search ADS PubMed  54 Komenda P, Ferguson TW, Macdonald K et al.   Cost-effectiveness of primary screening for CKD: a systematic review. Am J Kidney Dis  2014; 63: 789– 797 Google Scholar CrossRef Search ADS PubMed  55 Dalrymple LS, Katz R, Kestenbaum B et al.   Chronic kidney disease and the risk of end-stage renal disease versus death. J Gen Intern Med  2011; 26: 379– 385 Google Scholar CrossRef Search ADS PubMed  56 Hoerger TJ, Wittenborn JS, Zhuo X et al.   Cost-effectiveness of screening for microalbuminuria among African Americans. J Am Soc Nephrol  2012; 23: 2035– 2041 Google Scholar CrossRef Search ADS PubMed  57 Smart NA, Dieberg G, Ladhani M et al.   Early referral to specialist nephrology services for preventing the progression to end-stage kidney disease. Cochrane Database Syst Rev  2014; 6: CD007333 58 Ruggenenti P, Remuzzi G. Nephropathy of type 1 and type 2 diabetes: diverse pathophysiology, same treatment? Nephrol Dial Transplant  2000; 15: 1900– 1902 Google Scholar CrossRef Search ADS PubMed  59 Satirapoj B, Adler SG. Prevalence and management of diabetic nephropathy in western countries. Kidney Dis (Basel)  2015; 1: 61– 70 Google Scholar CrossRef Search ADS PubMed  60 Hill CJ, Cardwell CR, Patterson CC et al.   Chronic kidney disease and diabetes in the national health service: a cross-sectional survey of the U.K. national diabetes audit. Diabet Med  2014; 31: 448– 454 Google Scholar CrossRef Search ADS PubMed  61 Ohta M, Babazono T, Uchigata Y et al.   Comparison of the prevalence of chronic kidney disease in Japanese patients with type 1 and type 2 diabetes. Diabet Med  2010; 27: 1017– 1023 Google Scholar CrossRef Search ADS PubMed  62 Parving HH, Hovind P. Microalbuminuria in type 1 and type 2 diabetes mellitus: evidence with angiotensin converting enzyme inhibitors and angiotensin II receptor blockers for treating early and preventing clinical nephropathy. Curr Hypertens Rep  2002; 4: 387– 393 Google Scholar CrossRef Search ADS PubMed  63 Aitken GR, Roderick PJ, Fraser S et al.   Change in prevalence of chronic kidney disease in England over time: comparison of nationally representative cross-sectional surveys from 2003 to 2010. BMJ Open  2014; 4: e005480 Google Scholar CrossRef Search ADS PubMed  64 Saran R, Li Y, Robinson B et al.   US Renal Data System 2014 Annual Data Report: epidemiology of kidney disease in the United States. Am J Kidney Dis  2015; 66(1 Suppl 1): Svii, S1– 305 © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nephrology Dialysis Transplantation Oxford University Press

Cost-effectiveness of screening type 2 diabetes patients for chronic kidney disease progression with the CKD273 urinary peptide classifier as compared to urinary albumin excretion

Loading next page...
 
/lp/ou_press/cost-effectiveness-of-screening-type-2-diabetes-patients-for-chronic-0SVsYQes07
Publisher
Oxford University Press
Copyright
© The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
ISSN
0931-0509
eISSN
1460-2385
D.O.I.
10.1093/ndt/gfx068
Publisher site
See Article on Publisher Site

Abstract

Abstract Background In type 2 diabetes mellitus (T2DM) patients, chronic kidney disease (CKD) progression may occur without detectable changes in urinary albumin excretion (UAE) rate. A new urinary peptide classifier (CKD273) has exhibited greater ability to detect CKD progression, however, its cost-effectiveness remains unknown. This study evaluated the cost-effectiveness of screening for CKD progression with the CKD273 classifier, as compared to UAE, in diabetic patients. Methods A decision-analytic Markov model was developed to estimate costs and health outcomes [including overall survival and quality-adjusted life years (QALYs)] from a health system perspective for adopting a new annual screening strategy based on the CKD273 classifier as compared to annual UAE-based screening in a hypothetical cohort of T2DM patients. High-risk patients were defined as T2DM patients with at least one concomitant risk factor (i.e. patients with background genetic risk for developing the disease, obesity, hypertension and/or smoking history) for developing diabetic nephropathy secondary to cardiovascular disease (CVD)-related complications. Low-risk T2DM patients, were defined as those not having any of the aforementioned concomitant risk factors. Results Over the projected course of a patient’s lifetime, in all T2DM patients annual screening with the CKD273 classifier was more costly, but also more effective, than annual screening with UAE. The incremental costs incurred with screening based on the CKD273 classifier were €3,053 per patient, while patients gained 0.13 QALYs. Hence, in all patients, annual screening with the CKD273 classifier was cost effective [incremental cost-effectiveness ratio (ICER) €23,903/QALY gained], notably below current government thresholds for funding such health care interventions. For patients at high risk of developing diabetic nephropathy secondary to CVD-related complications, screening based on the CKD273 classifier was cost-saving (i.e. dominant, being both more effective and less expensive than UAE-based screening). Finally, in low-risk patients, CKD273 classifier-based screening was not cost effective (ICER €73,140/QALY) given current government willingness-to-pay thresholds. Conclusions In diabetic patients, annual CKD273 classifier-based screening is more costly but also more effective in QALYs gained as compared to UAE. From a health provider perspective, the observed benefits are greatest when such screening is implemented in patients at high risk for diabetes-associated renal or cardiovascular diseases (CVDs). chronic kidney disease, cost-effectiveness, diabetes mellitus type 2, prognostic biomarker, proteomics INTRODUCTION Diabetic nephropathy as well as hypertension constitute the leading causes of chronic kidney disease (CKD), which afflicts approximately one-tenth of the adult population in high-income countries [1, 2], albeit with notable variations in country-specific prevalence rates [3]. CKD may result in end-stage kidney disease (ESKD) with consequent diminution of patients’ quality of life and survival [4]. In 2002 alone, international registries reported 1 million patients with ESKD globally [5]. It is expected that by 2025, type 2 diabetes mellitus (T2DM) will affect >300 million patients worldwide, with CKD-attributable mortality rates increasing by 50% [6] from ∼1.5 million deaths in 2012 [7]. The health care–associated costs of CKD swell geometrically with advancing disease stages. In the USA, total CKD costs incurred by Medicare patients were estimated to be in the range of US$80 billion, including US$30 billion for CKD Stage 5 and US$50 billion for CKD Stages 1–4 [8]. Assuming similar disease distributions and health care–associated costs in the European Union (EU), these findings would infer that the costs of CKD in the EU approximate to €120 billion annually, as also reported in a recent publication wherein the authors estimated that the costs of CKD in Europe are likely to exceed €100 billion [9]. Findings of the AusDiab Study indicate that the cost of CKD increases in parallel with the severity of renal dysfunction [10]. Due to increasing prevalence rates of T2DM and its complications [11], CKD is an emerging concern for clinicians and health care policymakers alike. To improve health outcomes in T2DM patients, while concurrently reducing associated health care costs, investigations are increasingly dedicated to developing novel means of facilitating the timely detection of CKD onset as well as the consequent prevention of CKD progression and disease complications [12, 13]. Patients at elevated risk for CKD progression may receive intensive medical management so as to deter or delay ESKD, the necessity for dialysis and/or death [14]. CKD diagnosis is currently based on the detection of urinary albumin excretion (UAE) rates and/or a reduction in estimated glomerular filtration rate (eGFR). In addition, the combination of UAE and >5% annual reduction in eGFR are indicative of progressive CKD [15]. Gender does not appear to mediate the performance of either approach in relation to CKD onset or ESKD [16]. However, despite the absence of detectable UAE [17, 18] and/or microalbuminuria [19], CKD nevertheless progresses in a notable proportion of T2DM patients. Thus the clinical necessity of an accurate test that complements current medical approaches in predicting CKD progression, and consequently facilitates clinical decision making, remains inadequately addressed to date [18]. Recently, a capillary electrophoresis–mass spectrometry (CE-MS)-based urinary peptide classifier, namely the CKD273 classifier, was developed [20]. The diagnostic usefulness of the CKD273 classifier has been evaluated in multiple independent studies, including case–control [20] and longitudinal [21] investigations, and has since been proposed as a potentially clinically applicable predictive marker of CKD progression [21, 22]. Even so, its potential applicability in clinical practice ought to be considered in light of its cost-effectiveness [23] in relation to both health care costs [18, 24] and patients’ quality of life and health outcomes, including quality-adjusted survival [25]. While proteomics testing is expensive, urinary proteome testing with markers such as the CKD273 classifier is projected to decrease overall lifetime health care costs in T2DM patients [24]. It is hypothesized that, due to its higher sensitivity, the CKD273 classifier will detect diabetic patients at risk for CKD progression with greater accuracy [26] as compared with current standard approaches such as UAE [27]. As a result, screening with the CKD273 classifier may facilitate the pre-emptive initiation of intensified therapy [including additional antihypertensive therapy, angiotensin-converting enzyme (ACE) inhibitor or spironolactone] among T2DM patients at risk for CKD progression and hence deter both disease complications and attributable mortality in a cost-effective manner. However, the cost-effectiveness of screening T2DM patients for CKD progression using the CKD273 classifier as compared to UAE has not been evaluated to date. Therefore, the primary study objective is to evaluate the cost-effectiveness, from a European health care system perspective, of screening T2DM patients for CKD progression using the CKD273 classifier as compared to screening annually with UAE over a 40-year time frame. MATERIALS AND METHODS A Markov model was developed using TreeAge Pro 2008 software (TreeAge Software, Williamstown, MA, USA) to simulate the cost-effectiveness, from a European health care provider perspective, of annual screening for CKD progression using the CKD273 classifier as compared to annual screening with UAE in a hypothetical cohort of prevalent T2DM patients. The hypothetical cohort included patients of both genders, 50 years of age at baseline [28] and residing in Europe. High-risk patients were defined as those patients with at least one concomitant risk factor (i.e. patients with background genetic risk for developing the disease, obesity, hypertension and/or smoking history) for developing diabetic nephropathy secondary to cardiovascular disease (CVD)-related complications. Low-risk patients were defined as those not having any of the aforementioned concomitant risk factors. The current standard strategy for assessing CKD in T2DM patients, namely the UAE test, was considered an appropriate comparator in this model, as it represents standard care in most countries. Screening for CKD with the CKD273 classifier has a sensitivity of 94.6% and specificity of 97.1% [26]. The sensitivity of the UAE test is 70.0% and specificity is 71.0% [27]. In our hypothetical cohort, both tests were applied at baseline and annually thereafter in all patients until either CKD progression (micro- or macroalbuminuria and/or diabetic nephropathy) or any of the health outcomes (ESKD, dialysis, renal transplantation or death) under evaluation occurred. Additionally, simulations were also conducted wherein screening based on the peptide classifier was conducted in patients at either low or high risk for CVD-related complications. Model structure The analysis was conducted and reported according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement [29]. The Markov model was constructed to include all potential consequences of CKD progression, including diabetic nephropathy, kidney transplantation, graft failure, dialysis and death attributable either to CKD or CVDs (Supplementary data, Figure S1). Based on screening test results, diabetic patients either received modified intensified therapy and/or advanced through the health states under evaluation (namely, further disease progression, kidney transplantation, graft failure, dialysis and/or death). Intensified therapy, as compared with standard therapy, included additional antihypertensive therapy, ACE inhibitors or spironolactone. Specifically, the hypothetical cohort of normoalbuminuric T2DM patients was screened at baseline with both the CKD273 classifier and the UAE. Positive screening findings from either approach entailed normoalbuminuric patients receiving standard antihypertensive therapy transitioning to intensified therapy so as to deter the onset and/or progression of CKD. The efficacy of standard antihypertensive therapy is assumed to be 88.0% [30]; additionally, efficacy based on empirical reports with modified therapy is approximated at 68.0%. Following baseline screening, both CKD273 classifier and UAE screening were conducted annually until any of the health outcomes of interest occurred. The possible model pathways included the following: (i) At baseline, normoalbuminuric patients, receiving standard antihypertensive treatment and having negative screening results either remained event free or, despite screening results, transitioned to more advanced stages of CKD. Normoalbuminuric patients with positive screening results transitioned to receiving intensified therapy and either remained event free or transitioned to more advanced stages of CKD, despite early intervention. Both patient groups could die due to any cause or CVD. Surviving normoalbuminuric patients without CKD progression were retested annually thereafter with either the CKD273 classifier or the UAE until the aforementioned health events or death occurred. For surviving patients, a utility quality-adjusted life year (QALY) for no ESKD was determined for each year of survival {utility QALY 0.82, [95% confidence interval (CI) 0.74–0.90]} [31]. (ii) Microalbuminuric patients with negative screening results continued standard therapy and either remained event free or transitioned to macroalbuminuria (diabetic nephropathy). Microalbuminuric patients with positive screening results were administered intensified therapy and either remained event free or, despite treatment, transitioned to macroalbuminuria (diabetic nephropathy). The likelihood to progress between these latter two states was mediated by the effectiveness of intensified treatment. Alternatively, both patient groups could die from CVD or other causes. (iii) Patients with diabetic nephropathy either remained event free or died due to CVD or other causes. Surviving diabetic nephropathy patients either remained event free or transitioned to ESKD and/or, pending the efficacy of therapy, dialysis. Patients with ESKD and on dialysis could either die due to CVD or other causes or alternatively could transition to kidney transplantation. Patients could either stay on maintenance dialysis or die on dialysis. Finally, in order to assess differential impacts of utilizing annual CKD273-based screening in selected target population groups, the aforementioned simulations were replicated separately in low- and high-risk patients for diabetes-associated renal and cardiovascular complications. As detailed in Table 1, the risk of events, transition probabilities and outcomes were primarily based on the United Kingdom Prospective Diabetes Study (UKPDS 64) findings [32] and published reports [11, 33], given a CKD prevalence rate of 11% [34]. A time horizon of 40 years (maximum expected patient lifetime after developing T2DM) was applied for the comprehensive consideration of extended effects of identifying patients at risk for CKD progression upon further disease progression, kidney transplantation, graft failure, dialysis and death attributable either to CKD or CVD [35, 36]. Outcomes were expressed in terms of overall survival [life years (LYs)] and quality-adjusted survival (QALYs). Table 1 Annual probability of transitions, outcomes and utilities applied in the cost-effectiveness analysis, according to diabetic patients’ risk for developing complications   Probabilities and utilities     All patients  Patients at low risk for complications  Patients at high risk for complications  Transition variables   Baseline progression from normo- to microalbuminuria  0.05  0.033  0.075   Annual transition from normo- to microalbuminuria  0.020 [32]  0.013  0.030   Annual transition from micro- to macroalbuminuria  0.03 [32]  0.020  0.045   Annual transition from diabetic nephropathy to end-stage renal disease (dialysis)  0.040 [33]  0.040  0.040   Projected (2012–25) annual increase in the prevalence of CKD Stage 5  0.032 [11]  0.021  0.048  Outcome variables   No CKD progression    CV mortality in normoalbuminuric patients  0.007 [32]  0.005  0.010    Non-CV mortality in normoalbuminuric patients  0.007 [32]  0.007  0.007   CKD progression    CV mortality in microalbuminuric patients  0.020 [32]  0.013  0.030    Non-CV mortality in microalbuminuric patients  0.010 [32]  0.010  0.010    CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.035 [32]  0.035  0.035    Non-CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.011 [32]  0.011  0.011   Dialysis    CV mortality  0.121 [32]  0.121  0.121    Non-CV mortality  0.071 [32]  0.071  0.071  QALY parameters         Utility following renal transplantation  0.76 [39, 40]  0.76  0.76   Utility following dialysis  0.48 [40]  0.48  0.48   Utility no ESKD  0.88 [40]  0.88  0.88    Probabilities and utilities     All patients  Patients at low risk for complications  Patients at high risk for complications  Transition variables   Baseline progression from normo- to microalbuminuria  0.05  0.033  0.075   Annual transition from normo- to microalbuminuria  0.020 [32]  0.013  0.030   Annual transition from micro- to macroalbuminuria  0.03 [32]  0.020  0.045   Annual transition from diabetic nephropathy to end-stage renal disease (dialysis)  0.040 [33]  0.040  0.040   Projected (2012–25) annual increase in the prevalence of CKD Stage 5  0.032 [11]  0.021  0.048  Outcome variables   No CKD progression    CV mortality in normoalbuminuric patients  0.007 [32]  0.005  0.010    Non-CV mortality in normoalbuminuric patients  0.007 [32]  0.007  0.007   CKD progression    CV mortality in microalbuminuric patients  0.020 [32]  0.013  0.030    Non-CV mortality in microalbuminuric patients  0.010 [32]  0.010  0.010    CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.035 [32]  0.035  0.035    Non-CV mortality in macroalbuminuric patients (diabetic nephropathy)  0.011 [32]  0.011  0.011   Dialysis    CV mortality  0.121 [32]  0.121  0.121    Non-CV mortality  0.071 [32]  0.071  0.071  QALY parameters         Utility following renal transplantation  0.76 [39, 40]  0.76  0.76   Utility following dialysis  0.48 [40]  0.48  0.48   Utility no ESKD  0.88 [40]  0.88  0.88  Health outcomes The health states used in the Markov model included (i) T2DM (without CKD), (ii) T2DM with progressive stages of CKD, (iii) ESKD, (iv) dialysis, (v) renal transplantation and (vi) death. Recent meta-regression analyses indicate that each 10% incremental reduction of UAE is associated with a reduction in CVD-attributable events [37]. Likelihood rates of the health states assessed were retrieved from the UKPDS 64 report [32]. Based on the most conservative estimates arising from the UKPDS 74 [38], we assumed a 33% reduction in the risk of the investigated health outcomes in the low-risk group and a 1.5-fold increase of the corresponding values in the high-risk group (Table 1). Quality of life Decrements in quality of life were associated with diabetic complications, progression of CKD to ESKD and initiation of dialysis. Utility estimates for the QALY calculation were obtained from literature reports [39, 40] (Table 1). These values represent preference-based quality of life on an interval scale, where 0 represents death and 1 represents perfect health [41]. Resource use and costs All costs were valued in 2015 euros (€) or UK pounds (£; to which 2015-based euro conversions were applied) based on either recommended retail prices or reports of health care associated costs from a health system perspective [11, 33, 42] (Table 2). All costs include expenses incurred per annual frequency of testing and/or treatment, as well as physician and hospitalization costs. Costs of intensified therapy included drug costs and physician visits. Incremental cost-effectiveness ratios (ICERs) were calculated. Based on recent reports, governments of high-income countries are more likely to fund health care interventions with an ICER of less than approximately €45,000 (US$50,000) per QALY gained [43, 44], while UK National Health Service (NHS) thresholds are set at the lowest level approximating €25,600 (£20,000) per QALY gained [45]. Table 2 Cost estimations applied in the cost-effectiveness analysis of screening type 2 diabetic patients with the CKD273 peptide classifier as compared to UAE Description  References  Cost incurreda (euros, 2015)  Annual UAE testing  [Recommended retail price]  36  Annual CKD273 peptide classifier testing  [Recommended retail price]  866  Annual treatment costs for diabetic nephropathy  [11]  5987  Annual costs for dialysis  [42]  52 968  Annual cost of standard therapy (additional antihypertensive therapy in patients with known CKD)  [33]  857  Annual cost of modified therapy (ACE inhibitors), without standard therapy, in patients with known CKD  [33]  378  Annual cost of intensive therapy (ACE inhibitors and spironolactone), without standard therapy, in patients with known CKD  [33]  420  Description  References  Cost incurreda (euros, 2015)  Annual UAE testing  [Recommended retail price]  36  Annual CKD273 peptide classifier testing  [Recommended retail price]  866  Annual treatment costs for diabetic nephropathy  [11]  5987  Annual costs for dialysis  [42]  52 968  Annual cost of standard therapy (additional antihypertensive therapy in patients with known CKD)  [33]  857  Annual cost of modified therapy (ACE inhibitors), without standard therapy, in patients with known CKD  [33]  378  Annual cost of intensive therapy (ACE inhibitors and spironolactone), without standard therapy, in patients with known CKD  [33]  420  a Costs incurred include physician visits and hospitalization costs. Allowance for uncertainty All variables in the model were evaluated with one-way sensitivity analyses using an upper and lower bound for each cost and effect parameter, including the probability of kidney disease progression. Deterministic one-way sensitivity analysis findings were used to identify the variables that influenced the cost-effectiveness results. Finally, probabilistic sensitivity analysis was conducted based on Monte Carlo simulations with 1000 iterations, to assess the effects of uncertainty upon the overall confidence of the base case model findings. RESULTS Screening based on the CKD273 classifier compared to UAE Overall in T2DM patients, annual screening for CKD progression with the CKD273 classifier compared with UAE was more costly but also more effective in relation to QALYs gained (Table 3). Specifically, the total costs per patient incurred by annual screening with the CKD273 classifier was €57,083, as compared to €54,030 resulting from annual screening with the UAE over the course of 40 years. The incremental costs incurred by screening with the CKD273 classifier were €3,053 per patient. Additionally, patients screened with the CKD273 classifier had better survival and greater QALYs than those undergoing UAE-based screening. In particular, patients undergoing screening based on the CKD273 classifier gained 24.26 QALYs as compared to 24.13 QALYs for those undergoing UAE-based screening. Hence, patients undergoing CKD273 classifier-based screening gained 0.13 QALYs, predominantly due to the slowing of CKD progression and diabetic complications, as well as better quality of life. The ICER of screening based on the CKD273 classifier as compared to UAE was €23,903 per QALY gained, which is lower than the UK NHS threshold costs per QALY gained. Table 3 Cost-effectiveness analysis from a European health system perspective of annually screening type 2 diabetic patients with the CKD273 peptide classifier as compared to UAE over a 40-year time period, according to patient risk of developing diabetic nephropathy secondary to CVD-related complications Target population for screening strategy  Total cost per patient over 40 years (mean €, 2015)  Incremental cost (€, 2015)  Total effectiveness per patient over 40 years (mean QALY)  Incremental effectiveness (QALY)  ICER (€/QALY gained)  All patients             UAE  54 030  0  24.13  0  0   CKD273 peptide classifier  57 083  3053  24.26  0.13  23 903  Patients at low risk for CVD-related complications             UAE  48 159  0  26.27  0  0   CKD273 peptide classifier  54 305  6146  26.36  0.08  73 140  Patients at high risk for CVD-related complications             UAE  61 649  0  21.00  0  0   CKD273 peptide classifier  60 949  −700  21.17  0.17  Dominant  Target population for screening strategy  Total cost per patient over 40 years (mean €, 2015)  Incremental cost (€, 2015)  Total effectiveness per patient over 40 years (mean QALY)  Incremental effectiveness (QALY)  ICER (€/QALY gained)  All patients             UAE  54 030  0  24.13  0  0   CKD273 peptide classifier  57 083  3053  24.26  0.13  23 903  Patients at low risk for CVD-related complications             UAE  48 159  0  26.27  0  0   CKD273 peptide classifier  54 305  6146  26.36  0.08  73 140  Patients at high risk for CVD-related complications             UAE  61 649  0  21.00  0  0   CKD273 peptide classifier  60 949  −700  21.17  0.17  Dominant  The risk of developing diabetic nephropathy and associated complications can be predicted to some extent by certain clinical risk factors. In order to assess differential impacts of utilizing annual CKD273-based screening in selective population groups, simulations were also conducted in low- and high-risk patients, as defined above. As shown in Table 3, in low-risk patients, annual screening for CKD progression with the CKD273 classifier as compared with UAE was more costly but more effective in terms of QALYs gained. The ICER in this patient group was €73,140 per QALY gained, notably exceeding National Institute for Health and Care Excellence thresholds [45]. In contrast, in high-risk patients, annual screening for CKD progression with the CKD273 classifier was cost-saving, i.e. less costly and more effective (known as ‘dominant’) in relation to QALYs gained. Specifically, CKD273-based screening reduced cost by €700 per patient, while concomitantly contributing to the attainment of 0.17 QALYs. Sensitivity analyses In order to explore which variables had the greatest impact on expected costs, ICER tornado diagrams were constructed (Figure 1). The assumed willingness to pay (WTP) for the cost of the test was set at ∼10% greater than current UAE costs (€40). The ICER tornado diagram in high-risk populations (Figure 1) indicated that the factors that most profoundly affected the calculated expected value of the cost-effectiveness analysis included the annual probability of disease progression, efficacy of modified therapy for CKD management, mortality attributable to dialysis and annual expected increase in the prevalence of Stage 5 CKD. Deterministic one-way sensitivity analysis indicated that with increasing probability of transition from micro- to macroalbuminuria (i.e. CKD progression), the CKD273 classifier-based screening contributed to a greater gain in life years in T2DM patients as compared to UAE-based screening (Figure 2). Finally, probabilistic sensitivity analysis suggested that, assuming a WTP threshold of €45,000 (Supplementary data, Figure S2, Panel A), annual screening with the CKD273 classifier in T2DM patients is more cost effective than annual screening with UAE in 80.9% of the simulated events. When additionally the application of a more conservative WTP threshold of €25,600 was assumed (Supplementary data, Figure S2, Panel B), annual screening with the CKD273 classifier remained the most cost-effective approach in 52.2% of simulated events. FIGURE 1 View largeDownload slide ICER tornado diagram for the cost-effectiveness analysis from a European health provider perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening with UAE, over a 40-year time frame. The ICER tornado diagram indicates that the factors that most profoundly affect the calculated expected value (EV) of the cost-effectiveness analysis include the annual probability of disease progression, the efficacy of modified therapy for CKD management, the mortality attributable to dialysis and the annual expected increase in the prevalence of Stage 5 CKD. FIGURE 1 View largeDownload slide ICER tornado diagram for the cost-effectiveness analysis from a European health provider perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening with UAE, over a 40-year time frame. The ICER tornado diagram indicates that the factors that most profoundly affect the calculated expected value (EV) of the cost-effectiveness analysis include the annual probability of disease progression, the efficacy of modified therapy for CKD management, the mortality attributable to dialysis and the annual expected increase in the prevalence of Stage 5 CKD. FIGURE 2 View largeDownload slide One-way sensitivity analyses for the cost-effectiveness from a European perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening annually with UAE over a 40-year time frame. The analysis indicates that with increasing probability of annual CKD progression, the CKD273 classifier–based screening (as compared to UAE-based screening) was associated with a greater gain in QALYs in diabetic patients. FIGURE 2 View largeDownload slide One-way sensitivity analyses for the cost-effectiveness from a European perspective of screening type 2 diabetic patients using the CKD273 peptide classifier as compared to screening annually with UAE over a 40-year time frame. The analysis indicates that with increasing probability of annual CKD progression, the CKD273 classifier–based screening (as compared to UAE-based screening) was associated with a greater gain in QALYs in diabetic patients. Model validation Both internal and external validation of the constructed models was conducted according to the International Society for Pharmacoeconomics and Outcomes Research–Society for Medical Decision Making Modeling Good Research Practices report [46]. Internal validation was conducted independently by two individual researchers (R.M. and E.C.) in relation to model pathways, verification of individual equations and parameters and accurate implementation of the model assumptions. Due to limited data availability regarding CKD273 classifier use at the population level, external validation of the control arm of the analysis (namely, use of UAE in T2DM patients for CKD progression screening) was conducted in relation to survival. Data and modelling procedures from the UKPDS were used to study this effect [47]. The third-order validation conducted [48] revealed that the present model predicted a life expectancy within the age range of that reported by the UKPDS-OM2 simulation [remaining life expectancy of T2DM patients 50–54 years of age at base case: 25.1 years (95% CI 24.5–25.7)] [47]. DISCUSSION The present study findings indicate that annual screening of all T2DM patients for CKD progression with the CKD273 urinary peptide classifier was more costly but also more effective in terms of QALYs gained than annual screening with UAE. Specifically, in all T2DM patients, annual screening with the CKD273 classifier as compared to annual screening with UAE was cost effective (ICER €23,903/QALY gained) over the projected course of a patient’s lifetime. This is notably below current government thresholds for funding such health care interventions in most high-income countries, including Europe and the USA [43, 44]. The observed benefits from a European health provider perspective of utilizing CKD273-based screening was most profound when such a screening strategy was implemented in patients at high risk for developing diabetic nephropathy secondary to CVD-related complications. In particular, in high-risk patients, screening based on the CKD273 classifier was cost-saving (i.e. dominant), being both more effective and less expensive than UAE-based screening. In contrast, in low-risk patients, screening based on the CKD273 classifier was not cost effective (ICER €73,140/QALY) given current government WTP thresholds. Therefore, screening particularly high-risk T2DM patients with the CKD273 classifier appears to be a preferable cost-saving approach as compared to UAE. Particularly in high-income countries, the continuously increasing prevalence rates of both T2DM and CKD [11] mandate the early detection of patients likely to develop CKD progression and diabetic complications [12, 13]. It is already established that in patients at elevated risk for disease progression, the timely administration of modified intensified therapy can deter the necessity for long-term dialysis, ESKD and death, enhancing patients’ longevity and quality of life [4]. Moreover, from a European health provider perspective, recent findings indicate that diabetic patients who experience CKD progression incur 85% higher health care costs than their non-CKD counterparts [10]. Recent reports corroborate that the timely detection of diabetic patients who are most likely to develop CKD progression is of pivotal importance for initiating modified intervention therapy and deterring both CKD progression and diabetic complications, including ESKD [12, 49]. While current standard medical approaches utilize the combination of UAE and >5% annual reduction in eGFR as indicative of CKD progression [15], initial investigations have indicated that their combined performance is inferior to that of the recently developed CKD273 classifier [17, 18, 26, 27]. In particular, while recent meta-regression analyses indicate that incremental reductions of UAE are significantly associated with a reduction in CVD-attributable events [including myocardial infarction (−13%) and stroke (−29%), as well as overall composite CVD-attributable outcome, including death, myocardial infarction and stroke (−14%)], it is not associated with overall and CVD-attributable mortality [37]. However, the relationship of albumin:creatinine ratio to the relative risk of all-cause mortality and cardiovascular mortality displays log hazard ratios increasing linearly with increasing log albumin:creatinine ratio [50, 51]. In contrast, based on the Oxford Evidence-Based Levels, high evidence levels exist [22] that the CKD273 classifier is useful for predicting the occurrence of micro- or macroalbuminuria in T2DM patients [52, 53] and that it improves CKD risk prediction beyond eGFR and albuminuria [16]. A recent systematic review of investigations evaluating the cost-effectiveness of proteinuria-based screening in diabetic patients revealed that the ICERs ranged from US$5,298 to US$54,943 per QALY. Moreover, the most prominent determinants of cost-effectiveness of such a screening strategy were the incidence of CKD, rate of disease progression and effectiveness of treatment [54]. While not directly comparable due to the differences in the projected time horizons applied for the analyses, the present study findings indicate that in T2DM patients, the CKD273 classifier incurs higher incremental costs than UAE. However, patients gained 0.13 more QALYs than those under undergoing UAE-based screening. In absolute terms and from a national health provider standpoint [18, 24], the CKD273 classifier-based screening strategy is cost effective [23]. However, cost-effectiveness could not be demonstrated in the low-risk population, while screening based on the CKD273 classifier has a notably favourable value for cost, particularly in patients at highest risk for diabetes-related complications, demonstrating not only cost-effectiveness, but even savings. Furthermore, the notable increase in life years gained support from the consideration of its use for augmenting high-risk diabetic patients’ quality of life [25]. Finally, since patients with even mild stages of CKD are more likely to succumb to causes other than diabetic complications [55], the use of an accurate predictor of CKD progression at the earliest stage of disease is mandated. Therefore, as compared to annual screening with UAE, the adoption of screening high-risk patients for CKD progression with the CKD273 classifier appears to be a favourable approach for both health care costs and diabetic patients’ quality of life. Recent reports propose that UAE screening conducted at longer time intervals may be more cost effective, particularly in population groups at high risk for CKD progression [28, 56]. We explored the cost-effectiveness of implementing screening with the CKD273 classifier at longer time intervals, including every second, third and fifth year. Screening with the CKD273 classifier every second, third and fifth year was less expensive, but also less effective, than annual screening with UAE (Supplementary data, Table S1). In particular, the efficacy of screening was observed to decrease with increasing screening time intervals. It is posited that the diminished efficacy of infrequent screening strategies may be potentially attributed to the costs incurred by delayed diagnosis and timely referral to specialist services for deterring further progression to ESKD [57]. Hence, to secure the timely detection and referral of patients at risk for CKD progression and consequent initiation of modified therapy, it appears preferable, from both a patient and health care provider perspective, to conduct screening with the CKD273 classifier on an annual basis. Furthermore, we hypothesized that once the CKD273 classifier was widely adopted as a screening method, a notable reduction in its retail cost could be anticipated. Based on the model assumptions and sensitivity analysis, we assumed that following widespread use the CKD273 classifier would decrease in cost from €650 to €450 (Supplementary data, Table S1). In particular, in the latter hypothetical scenario, screening T2DM patients for CKD progression with the CKD273 classifier was dominant (i.e. less costly and more effective) than screening with UAE. Specifically, the incremental costs incurred by screening with UAE (as compared to the CKD273 classifier) were €2,177. The effectiveness of the CKD273 classifier was superior to that of UAE (CKD273 classifier versus UAE: 24.26 versus 24.13 QALYs gained) and patients had better survival and QALYs, gaining 0.13 QALYs. Screening with the CKD273 classifier had a markedly favourable value for cost as the ICER of UAE was −€17,043 per life year gained. Therefore, the cost-effectiveness of utilizing the CKD273 classifier in CKD progression screening is anticipated to be even greater once it is widely adopted in clinical practice and, by extent, its retail cost is notably reduced. Finally, although T2DM patients account for approximately one-third of patients requiring chronic renal replacement therapy, CKD is common to both type 1 and type 2 diabetes [58]. In particular, while the prevalence rate of diabetic nephropathy from type 1 diabetes has fallen during recent decades [59], findings from the recent UK National Diabetes Audit indicate that only 57.7% and 67.6% of type 1 and type 2 diabetic patients, respectively, are normoalbuminuric [60]. Furthermore, following adjustment for confounding effects, the prevalence of CKD in type 1 diabetics is similar to that of their type 2 counterparts [61]. Hence, the prevention of CKD remains a public health concern in type 1 and type 2 diabetic patients alike. The renoprotective effects particularly of ACE inhibition and angiotensin II receptor blockers in microalbuminuric patients for delaying progression to diabetic nephropathy has been documented in both type 1 and type 2 diabetic patients [62]. Therefore, it is anticipated that, based on the present study findings, screening of type 1 diabetic patients with the CKD273 classifier could facilitate the timely initiation of intensified therapy and consequent determent of diabetic nephropathy. Strengths and limitations The present analysis is the first of its kind to simulate the incremental costs and incremental benefits of screening diabetic patients for CKD progression with the CKD273 classifier as compared to UAE. Additionally, the simulation has utilized the highest quality of evidence to retrieve values of transition, outcomes and costs. Furthermore, the simulation was both internally and externally validated. However, it should be noted that randomized trials for estimating survival rates are not available to date. Finally, the cost-effectiveness analysis included an extended time horizon of 40 years. In normoalbuminuric patients, diabetic nephropathy may develop following a median period of 19 years, while ESKD may develop after a median of ∼9 years thereafter. Hence, the median period of normoalbuminuric patients to develop ESKD is ∼30 years [32]. Therefore, the time horizon of 40 years utilized for the present study simulation seems to be appropriate. Even so, the study findings should be considered with caution, as they arise from simulations in a hypothetical cohort of prevalent T2DM patients. Also, background rates have been derived from the UKPDS 64 and thus may vary from current prevalence rates [63]. However, since the applied rates remain notably lower than those of other developed countries such as the USA [64], any variations in rates introduced would likely only bias the study findings towards the null hypothesis. In addition, costs are observed from a European health and social care perspective. Specifically, risk of events/outcomes are based on the UKPDS 64 (with conversion to euros applied for the purposes of the present analysis), while costs incurred for events/outcomes were taken from the most recent published peer-reviewed scientific literature. Therefore, estimations are optimally calculated and extrapolated to the UK NHS system. Despite the external validations conducted to verify the model simulation, extrapolation of findings may be compromised for other national health care settings. Implications for future research The simulation conducted indicated that annually screening patients with T2DM for CKD progression with the CKD273 urinary peptide classifier was more costly but also more effective for QALYs gained than annual screening with UAE. The observed benefits from a European health provider perspective of utilizing CKD273-based screening were most profound when such screening was implemented in patients at high risk for developing diabetes-associated complications. Observational studies are necessary to validate the present study findings and to evaluate the potential impact of such screening practices on minimizing health care costs attributable to other comorbidities, including CVD, in high-risk patient groups. GLOSSARY Dominant: The intervention under evaluation incurs less costs and is at least as effective if not more effective than the comparator. Incremental cost-effectiveness ratio (ICER): The ratio of the difference in costs and the difference in health outcomes (effects). Markov model: A type of decision analytic model that allows for the transfer between mutually exclusive health states over an extended time period. Monte Carlo simulation: The random selection of values from a given distribution of model parameters, used in a probabilistic approach to calculate the 95% confidence range around the ICER. Sensitivity analysis: Approach for evaluating the robustness of an economic model by evaluating the changes in results obtained when key parameters or scenarios are varied over a specified range. Utility: A quality of life weight on a 0–1 scale (death to full health) used in the calculation of quality-adjusted life years (QALYs). SUPPLEMENTARY DATA Supplementary data are available online at http://ndt.oxfordjournals.org. ACKNOWLEDGEMENTS The authors would like to thank Ferdinand Bahlman, MD and Leif Fluehe, MSc for their contributions in retrieving and compiling data regarding the use of the CKD273 urinary peptide classifier at the population level. V.S.S. contributed to the compilation of the manuscript on behalf of the ERA-EDTA Registry, which is an official body of the ERA-EDTA (European Renal Association–European Dialysis and Transplant Association). FUNDING This work was supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreements 279277 (PRIORITY), grant agreement 601933 (TransBioBC) and grant agreement 608332 (iMODE-CKD). CONFLICT OF INTEREST STATEMENT None declared. The results presented in this paper have not been published previously in whole or part, except in abstract format. REFERENCES 1 Jha V, Garcia-Garcia G, Iseki K et al.   Chronic kidney disease: global dimension and perspectives. Lancet  2013; 382: 260– 272 Google Scholar CrossRef Search ADS PubMed  2 Alebiosu CO, Ayodele OE. The global burden of chronic kidney disease and the way forward. Ethn Dis  2005; 15: 418– 423 Google Scholar PubMed  3 Bruck K, Stel VS, Gambaro G et al.   CKD prevalence varies across the European general population. J Am Soc Nephrol  2016; 27: 2135– 2147 Google Scholar CrossRef Search ADS PubMed  4 Tonelli M, Wiebe N, Culleton B et al.   Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol  2006; 17: 2034– 2047 Google Scholar CrossRef Search ADS PubMed  5 Lysaght MJ. Maintenance dialysis population dynamics: current trends and long-term implications. J Am Soc Nephrol  2002; 13(Suppl 1): S37– S40 Google Scholar PubMed  6 World Health Organization. Diabetes. Fact sheet 312. World Health Organization, 2008. 7 World Health Organization. Global Health Estimates: Deaths by Cause, Age, Sex and Country, 2000-2012 . Geneva: World Health Organization, 2014. 8 Honeycutt AA, Segel JE, Zhuo X et al.   Medical costs of CKD in the Medicare population. J Am Soc Nephrol  2013; 24: 1478– 1483 Google Scholar CrossRef Search ADS PubMed  9 Nkuipou-Kenfack E, Zurbig P, Mischak H. The long path towards implementation of clinical proteomics: exemplified based on CKD273. Proteomics Clin Appl  2017; 11: 5– 6 10 Wyld ML, Lee CM, Zhuo X et al.   Cost to government and society of chronic kidney disease stage 1-5: a national cohort study. Intern Med J  2015; 45: 741– 747 Google Scholar CrossRef Search ADS PubMed  11 Kainz A, Hronsky M, Stel VS et al.   Prediction of prevalence of chronic kidney disease in diabetic patients in countries of the European Union up to 2025. Nephrol Dial Transplant  2015; 30(Suppl 4): iv113– iv118 Google Scholar CrossRef Search ADS PubMed  12 Levey AS, Coresh J. Chronic kidney disease. Lancet  2012; 379: 165– 180 Google Scholar CrossRef Search ADS PubMed  13 Levey AS, Atkins R, Coresh J et al.   Chronic kidney disease as a global public health problem: approaches and initiatives – a position statement from Kidney Disease Improving Global Outcomes. Kidney Int  2007; 72: 247– 259 Google Scholar CrossRef Search ADS PubMed  14 Wouters OJ, O'Donoghue DJ, Ritchie J et al.   Early chronic kidney disease: diagnosis, management and models of care. Nat Rev Nephrol  2015; 11: 491– 502 Google Scholar CrossRef Search ADS PubMed  15 Levey AS, Eckardt KU, Tsukamoto Y et al.   Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int  2005; 67: 2089– 2100 Google Scholar CrossRef Search ADS PubMed  16 Nitsch D, Grams M, Sang Y et al.   Associations of estimated glomerular filtration rate and albuminuria with mortality and renal failure by sex: a meta-analysis. BMJ  2013; 346: f324 Google Scholar CrossRef Search ADS PubMed  17 Miller WG, Bruns DE, Hortin GL et al.   Current issues in measurement and reporting of urinary albumin excretion. Clin Chem  2009; 55: 24– 38 Google Scholar CrossRef Search ADS PubMed  18 Mullen W, Delles C, Mischak H. Urinary proteomics in the assessment of chronic kidney disease. Curr Opin Nephrol Hypertens  2011; 20: 654– 661 Google Scholar CrossRef Search ADS PubMed  19 Krolewski AS. Progressive renal decline: the new paradigm of diabetic nephropathy in type 1 diabetes. Diabetes Care  2015; 38: 954– 962 Google Scholar CrossRef Search ADS PubMed  20 Good DM, Zurbig P, Argiles A et al.   Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics  2010; 9: 2424– 2437 Google Scholar CrossRef Search ADS PubMed  21 Siwy J, Schanstra JP, Argiles A et al.   Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy. Nephrol Dial Transplant  2014; 29: 1563– 1570 Google Scholar CrossRef Search ADS PubMed  22 Critselis E, Lambers Heerspink H. Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression. Nephrol Dial Transplant  2016; 31: 249– 254 Google Scholar PubMed  23 Horvath AR, Lord SJ, St John A et al.   From biomarkers to medical tests: the changing landscape of test evaluation. Clin Chim Acta  2014; 427: 49– 57 Google Scholar CrossRef Search ADS PubMed  24 Mischak H, Delles C, Klein J et al.   Urinary proteomics based on capillary electrophoresis-coupled mass spectrometry in kidney disease: discovery and validation of biomarkers, and clinical application. Adv Chronic Kidney Dis  2010; 17: 493– 506 Google Scholar CrossRef Search ADS PubMed  25 Ioannidis JP, Khoury MJ. Improving validation practices in "omics" research. Science  2011; 334: 1230– 1232 Google Scholar CrossRef Search ADS PubMed  26 Molin L, Seraglia R, Lapolla A et al.   A comparison between MALDI-MS and CE-MS data for biomarker assessment in chronic kidney diseases. J Proteomics  2012; 75: 5888– 5897 Google Scholar CrossRef Search ADS PubMed  27 Mogensen CE. How to protect the kidney in diabetic patients: with special reference to IDDM. Diabetes  1997; 46(Suppl 2): S104– S111 Google Scholar CrossRef Search ADS PubMed  28 Hoerger TJ, Wittenborn JS, Segel JE et al.   A health policy model of CKD: 2. The cost-effectiveness of microalbuminuria screening. Am J Kidney Dis  2010; 55: 463– 473 Google Scholar CrossRef Search ADS PubMed  29 Husereau D, Drummond M, Petrou S et al.   Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. BMJ  2013; 346: f1049 Google Scholar CrossRef Search ADS PubMed  30 Farmer AJ, Stevens R, Hirst J et al.   Optimal strategies for identifying kidney disease in diabetes: properties of screening tests, progression of renal dysfunction and impact of treatment – systematic review and modelling of progression and cost-effectiveness. Health Technol Assess  2014; 18: 1– 128 Google Scholar CrossRef Search ADS PubMed  31 Wyld M, Morton RL, Hayen A et al.   A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. PLoS Med  2012; 9: e1001307 Google Scholar CrossRef Search ADS PubMed  32 Adler AI, Stevens RJ, Manley SE et al.   Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney Int  2003; 63: 225– 232 Google Scholar CrossRef Search ADS PubMed  33 Manns B, Hemmelgarn B, Tonelli M et al.   Population based screening for chronic kidney disease: cost effectiveness study. BMJ  2010; 341: c5869 Google Scholar CrossRef Search ADS PubMed  34 Jha V, Wang AY, Wang H. The impact of CKD identification in large countries: the burden of illness. Nephrol Dial Transplant  2012; 27(Suppl 3): iii32– iii38 Google Scholar PubMed  35 Pippias M, Jager KJ, Kramer A et al.   The changing trends and outcomes in renal replacement therapy: data from the ERA-EDTA Registry. Nephrol Dial Transplant  2015; 36 Gonzalez-Espinoza L, Ortiz A. 2012 ERA-EDTA Registry Annual Report: cautious optimism on outcomes, concern about persistent inequalities and data black-outs. Clin Kidney J  2015; 8: 243– 247 Google Scholar CrossRef Search ADS PubMed  37 Savarese G, Dei Cas A, Rosano G et al.   Reduction of albumin urinary excretion is associated with reduced cardiovascular events in hypertensive and/or diabetic patients. A meta-regression analysis of 32 randomized trials. Int J Cardiol  2014; 172: 403– 410 Google Scholar CrossRef Search ADS PubMed  38 Retnakaran R, Cull CA, Thorne KI et al.   Risk factors for renal dysfunction in type 2 diabetes: U.K. Prospective Diabetes Study 74. Diabetes  2006; 55: 1832– 1839. Google Scholar CrossRef Search ADS PubMed  39 Kennedy-Martin T, Paczkowski R, Rayner S. Utility values in diabetic kidney disease: a literature review. Curr Med Res Opin  2015; 31: 1271– 1282 Google Scholar CrossRef Search ADS PubMed  40 Lung TW, Hayes AJ, Hayen A et al.   A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation. Qual Life Res  2011; 20: 1669– 1678 Google Scholar CrossRef Search ADS PubMed  41 Torrance GW. Measurement of health state utilities for economic appraisal. J Health Econ  1986; 5: 1– 30 Google Scholar CrossRef Search ADS PubMed  42 Roggeri DP, Salomone M. Chronic kidney disease: evolution of healthcare costs and resource consumption from predialysis to dialysis in Piedmont region, Italy. Adv Nephrol  2014; 2014: 1– 6 Google Scholar CrossRef Search ADS   43 Pearson SD, Rawlins MD. Quality, innovation, and value for money: NICE and the British National Health Service. JAMA  2005; 294: 2618– 2622 Google Scholar CrossRef Search ADS PubMed  44 Morton RL, Howard K, Webster AC et al.   The cost-effectiveness of induction immunosuppression in kidney transplantation. Nephrol Dial Transplant  2009; 24: 2258– 2269 Google Scholar CrossRef Search ADS PubMed  45 National Institute for Health and Care Excellence. The Guidelines Manual . London: NICE, 2012. 46 Eddy DM, Hollingworth W, Caro JJ et al.   Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Value Health  2012; 15: 843– 850 Google Scholar CrossRef Search ADS PubMed  47 Hayes AJ, Leal J, Gray AM et al.   UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia  2013; 56: 1925– 1933 Google Scholar CrossRef Search ADS PubMed  48 McCabe C, Dixon S. Testing the validity of cost-effectiveness models. Pharmacoeconomics  2000; 17: 501– 513 Google Scholar CrossRef Search ADS PubMed  49 Schievink B, Kropelin T, Mulder S et al.   Early renin-angiotensin system intervention is more beneficial than late intervention in delaying end-stage renal disease in patients with type 2 diabetes. Diabetes Obes Metab  2016; 18: 64– 71 Google Scholar CrossRef Search ADS PubMed  50 van der Velde M, Matsushita K, Coresh J et al.   Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int  2011; 79: 1341– 1352 Google Scholar CrossRef Search ADS PubMed  51 Astor BC, Matsushita K, Gansevoort RT et al.   Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int  2011; 79: 1331– 1340 Google Scholar CrossRef Search ADS PubMed  52 Zurbig P, Jerums G, Hovind P et al.   Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes  2012; 61: 3304– 3313 Google Scholar CrossRef Search ADS PubMed  53 Roscioni SS, de Zeeuw D, Hellemons ME et al.   A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus. Diabetologia  2013; 56: 259– 267 Google Scholar CrossRef Search ADS PubMed  54 Komenda P, Ferguson TW, Macdonald K et al.   Cost-effectiveness of primary screening for CKD: a systematic review. Am J Kidney Dis  2014; 63: 789– 797 Google Scholar CrossRef Search ADS PubMed  55 Dalrymple LS, Katz R, Kestenbaum B et al.   Chronic kidney disease and the risk of end-stage renal disease versus death. J Gen Intern Med  2011; 26: 379– 385 Google Scholar CrossRef Search ADS PubMed  56 Hoerger TJ, Wittenborn JS, Zhuo X et al.   Cost-effectiveness of screening for microalbuminuria among African Americans. J Am Soc Nephrol  2012; 23: 2035– 2041 Google Scholar CrossRef Search ADS PubMed  57 Smart NA, Dieberg G, Ladhani M et al.   Early referral to specialist nephrology services for preventing the progression to end-stage kidney disease. Cochrane Database Syst Rev  2014; 6: CD007333 58 Ruggenenti P, Remuzzi G. Nephropathy of type 1 and type 2 diabetes: diverse pathophysiology, same treatment? Nephrol Dial Transplant  2000; 15: 1900– 1902 Google Scholar CrossRef Search ADS PubMed  59 Satirapoj B, Adler SG. Prevalence and management of diabetic nephropathy in western countries. Kidney Dis (Basel)  2015; 1: 61– 70 Google Scholar CrossRef Search ADS PubMed  60 Hill CJ, Cardwell CR, Patterson CC et al.   Chronic kidney disease and diabetes in the national health service: a cross-sectional survey of the U.K. national diabetes audit. Diabet Med  2014; 31: 448– 454 Google Scholar CrossRef Search ADS PubMed  61 Ohta M, Babazono T, Uchigata Y et al.   Comparison of the prevalence of chronic kidney disease in Japanese patients with type 1 and type 2 diabetes. Diabet Med  2010; 27: 1017– 1023 Google Scholar CrossRef Search ADS PubMed  62 Parving HH, Hovind P. Microalbuminuria in type 1 and type 2 diabetes mellitus: evidence with angiotensin converting enzyme inhibitors and angiotensin II receptor blockers for treating early and preventing clinical nephropathy. Curr Hypertens Rep  2002; 4: 387– 393 Google Scholar CrossRef Search ADS PubMed  63 Aitken GR, Roderick PJ, Fraser S et al.   Change in prevalence of chronic kidney disease in England over time: comparison of nationally representative cross-sectional surveys from 2003 to 2010. BMJ Open  2014; 4: e005480 Google Scholar CrossRef Search ADS PubMed  64 Saran R, Li Y, Robinson B et al.   US Renal Data System 2014 Annual Data Report: epidemiology of kidney disease in the United States. Am J Kidney Dis  2015; 66(1 Suppl 1): Svii, S1– 305 © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Journal

Nephrology Dialysis TransplantationOxford University Press

Published: Mar 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial