Medication discrepancies in late-stage chronic kidney disease

Medication discrepancies in late-stage chronic kidney disease Background: Late-stage chronic kidney disease (LS-CKD) can be defined by glomerular filtration rate (GFR) 0–30 mL/min. It is a period of risk for medication discrepancies because of frequent hospitalizations, fragmented medical care, inadequate communication and polypharmacy. In this study, we sought to characterize medication discrepancies in LS-CKD. Methods: We analyzed all patients enrolled in Northwell Health’s Healthy Transitions in LS-CKD program. All patients had estimated GFR 0–30 mL/min, not on dialysis. Medications were reviewed by a nurse at a home visit. Patients’ medication usage and practice were compared with nephrologists’ medication lists, and discrepancies were characterized. Patients were categorized as having either no discrepancies or one or more. Associations between patient characteristics and number of medication discrepancies were evaluated by chi-square or Fisher’s exact test for categorical variables, and two-sample t-test or Wilcoxon text for continuous variables. Results: Seven hundred and thirteen patients with a median age of 70 (interquartile range 58–79) years were studied. There were 392 patients (55.0% of the study population) with at least one medication discrepancy. The therapeutic classes of medications with most frequently occurring medication discrepancies were cardiovascular, vitamins, bone and mineral disease agents, diuretics, analgesics and diabetes medications. In multivariable analysis, factors associated with higher risk of discrepancies were congestive heart failure [odds ratio (OR) 2.13; 95% confidence interval (CI) 1.44–3.16; P¼ 0.0002] and number of medications (OR 1.29; 95% CI 1.21–1.37; P< 0.0001). Conclusions: Medication discrepancies are common in LS-CKD, affect the majority of patients and include high-risk medication classes. Congestive heart failure and total number of medications are independently associated with greater risk for multiple drug discrepancies. The frequency of medication discrepancies indicates a need for great care in medication management of these patients. Key words: chronic renal failure, chronic renal insufficiency, CKD, diuretics, medications Received: July 21, 2017. Editorial decision: October 23, 2017 V C The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 508 | J. Ibrahim et al. care management services to patients with LS-CKD, in an effort Introduction to facilitate care processes and improve preparation for end- Medication discrepancies can be defined as a mismatch stage kidney disease (all such patients were eligible for partici- between a treating physician’s understanding of patients’ cur- pation in the program). The program includes patients with rent medications (including dose and frequency) and the actual CKD Stages 4–5 defined as estimated glomerular filtration rate medications that the patient takes. Discrepancies have the (eGFR) 0–30 mL/min, and excludes patients (i) on dialysis and potential to impair the effectiveness and safety of medication (ii) with significant cognitive impairment, defined as need for treatment. Ideally, the physician conducts a medication recon- assistance with activities of daily living. The program began as ciliation with the patient and educates patients about medica- a pilot, entered a full implementation phase and subsequently tions. In addition, the physician should maintain good had an imbedded randomized controlled trial (RCT) [7] and a communication with other medical providers regarding medi- 3-year period of funding by Centers for Medicare and Medicaid cation changes. In actual practice, however, medication discrep- Innovation program Health Care Innovations Award. ancies are frequent, occurring in 34–95% of patients at the All patients enrolled throughout all program phases from time of admission for acute hospitalizations or psychiatric clin- October 2012 to December 2016 were included in the current ics [1, 2]. When discrepancies occur there may be failure to rec- analysis except for patients from the RCT control group. The ognize symptoms and signs as medication side effects and justification for inclusion of all of these program phase patients increased risk for adverse events [3]. A systematic review of is that all had exactly the same initial home visit with medica- acute hospitalizations found that 11–59% of medication discrep- tion review conducted prior to any other interventions. The pro- ancies were clinically important and 39% had potential to cause gram functions and patients were enrolled, from Nassau, moderate-to-severe patient harm [1]. Suffolk, Queens, Kings and New York counties of New York To date there have been no studies of medication discrepan- State. There have been seven different participating nephrology cies in late-stage chronic kidney disease (LS-CKD). LS-CKD can groups. Three of the offices were private practices, four were be defined as CKD Stages 4–5, prior to the initiation of renal academic nephrology practices. There was no standardized replacement therapy. There are several reasons to suspect that method for medication history-taking among the nephrologists. medication discrepancies may be common in LS-CKD. Patients often take multiple medications, reaching a mean of 11.8 by the Medication review time of dialysis need [4]. Polypharmacy and conflicting dosing schedules are never easy for patients to manage. This is prob- Medication discrepancies were defined from the perspective of ably more true in LS-CKD, where patients are usually older and the nephrologist. Most patients had a recent visit with the have comorbidities including cognitive decline. In addition, nephrologist at the time of program enrollment. After enroll- patients with LS-CKD often have multiple different physicians ment, a home visit was conducted by a program nurse. The involved in their care. The result can sometimes be care frag- nephrologist’s medication list was printed from the electronic mentation with confusion created by numerous drug and dos- health record (EHR) prior to the home visit. All program neph- ing changes. Frequent hospitalizations among patients with LS- rologists’ EHRs prompt for medication reconciliation at all CKD result in medication and dose changes, often without patient visits. adequate reconciliation and communication among providers Medication reviews were all conducted by one of the pro- at hospital discharge. In general, communication problems are gram’s registered nurses. All took place during an initial home a recurring theme throughout the care system, contributing to visit that occurred prior to any other program interventions. medication risk. The purpose of the home visit is to begin to build a trusting rela- Since 2012, we have operated the Healthy Transitions care tionship, to assess the patient’s knowledge regarding their kid- management program for patients with LS-CKD. Nephrologist ney disease, to initiate education regarding modality options for treatment is supplemented by nursing care management serv- renal replacement therapy and diet and most relevant to the ices and advanced informatics. All program patients have an current analysis, to conduct a complete medication review. initial home visit, which offers a unique opportunity for medi- The medication portion of the visit begins with the nurse cation review. The visit occurs as the initial program contact, so asking the patient and key care giver where in the home medi- the medication reviews are free from any interventional effect. cations are kept, and how the patient carries medications if At the visit, the patient and key caregivers show the bottles of needed during the day. Next, the patient was asked to remove medications being used and explain how many pills are taken their currently used medication jars from their medicine cabi- and how frequently. Because this occurs in the patient’s home, net or other storage area. In many cases, the nurse helped the with the actual drugs consumed, it is a rigorous form of medica- patient with this task. Each medication jar was reviewed indi- tion review. In this analysis, we sought to utilize this uniquely vidually with attention to accuracy. The patient was questioned clear view of medication use to better understand and charac- on (i) if the medication was currently being taken, (ii) how many terize the frequency, type and risk factors for medication dis- pills, (iii) how many times a day and (iv) were there any other crepancies in LS-CKD. We are aware of no previous studies of medications, including over-the-counter agents, herbals or vita- this subject in CKD and studies in other populations have often mins, that the patient took. The patient was questioned on relied on health record reviews rather than direct patient inter- other medications that were on the nephrologist’s medication views [5, 6]. list but that the patient was not taking at that time. For any dif- ference between the nephrologist’s list of medications and the patient’s actual medication consumption, a discrepancy and its Materials and methods characteristics were recorded. Although all patients had subse- Patient population quent medication reviews as part of the program, only the base- The Healthy Transitions in Late Stage Kidney Disease program line review was included in the current analysis to avoid began operation in October 2012. The program provides nursing potential bias introduced by program interventions. Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Medication discrepancies in LS-CKD | 509 Table 1. Patient characteristics Table 2. Frequency distribution of number of medication discrepan- cies per patient Characteristic n (% or SD) Medication discrepancies per patient Number Percentage Mean age (years) 67.5 (15.9) Male 403 (56.5) 0 321 45.0 Race 1 125 17.5 White 434 (60.9) 2 83 11.6 Black 174 (24.4) 3 57 8.0 Other or multiracial 67 (9.4) 4 41 5.8 Asian 38 (5.3) 5 19 2.7 Hispanic ethnicity 64 (9.0) 6 24 3.4 Hypertension 662 (92.8) 7 14 2.0 Diabetes 364 (51.1) 8 4 0.6 Congestive heart failure 181 (25.3) 9 5 0.7 Primary insurance type 10 8 1.1 Medicare 400 (56.1) 11 3 0.4 Private 239 (33.5) 12 3 0.4 Medicaid 67 (9.4) 13 1 0.1 No insurance 7 (1) 14 2 0.3 Active smoker 15 1 0.1 Yes 41 (5.8) 16 1 0.1 No 666 (93.4) 17 0 0.0 Not specified 6 (0.8) 18 1 0.1 Mean eGFR (mL/min) 18.66 6.4 eGFR category (mL/min) <10 52 (7.3) Results 10–15 188 (26.4) 15.1–20 178 (25) A total of 713 patients were reviewed who were enrolled from >20 295 (41.4) October 2012 to December 2016. In the entire cohort, the median Mean number of medications 8.1 (3.4) age was 70 [interquartile range (IQR) 58–79] years, 56.5% were men, 24.4% were black, 60.9% were white, 9% were Hispanic, 51.1% were diabetic and 92.8% had hypertension. Patient char- Statistical analyses acteristics are displayed in Table 1. The distribution of eGFR was <10 mL/min, 7.3%; 10–15 mL/min, 26.3%; 15.1–20 mL/min, Baseline patient demographics (age, gender, race, ethnicity, 25%; > 20 mL/min, 41.4%. Six patients had previously undergone hypertension, diabetes, congestive heart failure, insurance type, kidney transplantation. The median time from last nephrologist smoking status and eGFR) were described using categorical or medication reconciliation to nurse visit was 3 (IQR 1–6) days. continuous variables as appropriate. In the entire patient There were 392 patients (55.0% of the study population) with cohort, the frequency, proportion and type of medication dis- at least one medication discrepancy. The total number of dis- crepancy was described. Categorization of medication discrep- crepancies was 1280, and the median number of discrepancies ancy type was determined a priori, and included therapeutic per patient (in patients with at least 1 discrepancy) was 2 (IQR class, and any difference between the nephrologist’s list of 1–4). The distribution of discrepancies per patient is displayed medications and the patient’s actual medication consumption in Table 2. There were 321 patients with zero discrepancies (i.e. patient taking medication not on nephrologist’s list, patient (45%), 125 patients with one discrepancy and 83 patients with not taking medication on nephrologist’s list, different dose and/ two discrepancies. There were 12.1% of patients with five or or different frequency, drug discontinued and patient taking, more discrepancies. The most common type of discrepancies drug discontinued and still on nephrologist’s list or patient not were patients taking a medication not on nephrologist’s list taking medication as instructed). (49.1% of discrepancies), patients not taking a medication on To evaluate patient characteristics associated with risk for the nephrologist’s list (22.7%), different dose (18.4%) and differ- multiple medication discrepancies, we first divided patients ent frequency (5%) (Figure 1). The therapeutic medication into two categories, one with no medication discrepancies and classes with the greatest number of discrepancies were cardio- one with one or more discrepancies. We decided on categoriza- vascular (15.5% of discrepancies), vitamins (11.0%), bone and tion prior to any inspection of results by category. The variables mineral disease agents (8.9%), diuretics (7.0%), analgesics (6.7%) chosen for study were based on availability and then on those and diabetes medications (5.0%) (Figure 2). The types of discrep- with potential causal pathways. In an initial univariable analy- ancies were similar for different drug classes. Among cardiovas- sis, we used chi-square or Fisher’s exact test as appropriate for cular drugs, the subcategories with the greatest number of categorical variables, and two-sample t-test or Wilcoxon text as discrepancies were renin angiotensin aldosterone system inhib- appropriate for continuous variables, to evaluate the associa- tion of each patient characteristic with the outcome of number itors (66% of cardiovascular discrepancies) and beta blockers of medication discrepancies (no discrepancies versus one or (24%). When assessing the outcome of multiple medication dis- more). In subsequent multivariable logistic regression analysis, factors that were found statistically significant in the univari- crepancies, there were 321 patients with no discrepancies, and able analysis plus age and gender were taken as the candidate 392 patients with one or more discrepancies. In a univariable variables for the final model. We conducted all analyses using analysis, the following patient characteristics were found to be SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). associated with medication discrepancies (P< 0.05): congestive Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 510 | J. Ibrahim et al. Paent taking Paent not Different dose Different Different dose Drug Drug Paent not medicaon taking a frequency and frequency disconnued, disconnued taking not on medicaon on paent sll but sll on medicaon as nephrologist's nephrologist's taking nephrologist's instructed list list list Fig. 1. Frequency distribution of types of medication discrepancy, from most common to least common. Fig. 2. Frequency of medication discrepancies by therapeutic class. heart failure, white race, hypertension and number of medica- taken that were not on the nephrologist’s list, medications on tions taken (Table 3). In subsequent multivariable logistic the nephrologist’s list that were not being taken, and dose and regression analysis, factors that were found statistically signifi- frequency differences. cant in the univariable analysis plus age and gender were taken Effective medication management is achieved by optimizing as the candidate variables for the final model. After backward the benefits and reducing risks of treatment. Ongoing medica- variable selection, factors in the final model independently tion review and reconciliation by the provider is essential for longitudinal maintenance of medication safety. When discrep- associated with higher risk of one or more medication discrep- ancies develop between the medications that the provider ancies compared with zero discrepancy were congestive heart failure [odds ratio (OR) 2.13; 95% confidence interval (CI) 1.44– intends for the patient to be taking and those actually being 3.16; P ¼ 0.0002] and number of medications (OR 1.29; 95% CI used, there is increased risk for loss of efficacy or adverse events. Discrepancies can be highly relevant, with a recent 1.21–1.37; P< 0.0001) (Table 4). study finding that in intensive care units, 17.1% of discrepancies were serious or potentially life-threatening [8]. Discussion In the treatment of patients with LS-CKD, the medication We found that more than half of patients with LS-CKD had at management process is fraught with polypharmacy, extensive least one discrepancy between the nephrologist’s medication comorbidity and altered medication pharmacology due to record and the medications actually being consumed. reduced renal function, fragmented medical care and frequent Approximately one-third of patients had two or more discrep- hospitalizations. The nephrologist plays a key and often princi- ancies. Most discrepancies were related to medications being pal role in maintenance of medication safety in these patients. Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Number of DIscrepancies Number of Discrepancies Medication discrepancies in LS-CKD | 511 Table 3. Univariate association of patient characteristics and num- Table 4. Multivariable analysis for medication discrepancy (logistic ber of medication discrepancies regression) Number of medication Multivariable analysis for medication discrepancy discrepancies Odds ratio Zero Variables (95% confidence interval) P-value discrepancies 1 discrepancy CHF (yes versus no) 2.13 (1.44–3.16) 0.0002 Patient characteristics (n¼ 321) (n¼ 392) P-value Number of medications 1.29 (1.21–1.37) <0.0001 Mean number of 0 3.3 (2.8) NA CHF, congestive heart failure. discrepancies (SD) Mean (6SD) age (years) 68.06 15.8 67.36 16.1 0.40 complex, chaotic setting is fertile ground for medication dis- Gender, n (%) 0.15 Male 172 (42.7) 231 (57.3) crepancies and complications. Female 149 (48.1) 161 (51.9) We also found that the number of medications that patients Race, n (%) 0.03 were taking was associated with greater risk for discrepancies. White 177 (40.8) 257 (59.2) This is an intuitive finding and consistent with existing knowl- Black 89 (51.2) 85 (48.9) edge [9]. In LS-CKD, patients take a large number of medications Other or multiracial 33 (49.3) 34 (50.8) and it is wise to review ongoing need for all chronic medications. Asian 22 (57.9) 16 (42.1) Reduction in medication discrepancies in LS-CKD requires a Hispanic ethnicity, n (%) 0.27 more intensive approach to medication reconciliation. Sharing of Yes 33 (51.6) 31 (48.4) medication information between healthcare providers and accu- No 288 (44.4) 361 (55.6) rate is a prerequisite. Incorporation of pharmacy information on Hypertension, n (%) 0.01 medication fill histories into EHRs holds great promise [10]. Ideally, Yes 289 (43.7) 373 (56.3) the physician’s medication record should reflect the time of the No 32 (62.8) 19 (37.2) last patient visit or other communication, but annotated with sub- Diabetes mellitus, n (%) 0.90 sequent activity from other providers and pharmacies. Improved Yes 163 (44.8) 201 (55.2) patient education is important as well, as it is probable that better No 158 (45.3) 191 (54.7) understanding of medications and their purpose would enhance Congestive heart 0.0004 failure, n (%) patient adherence and limit errors. Polypharmacy is certainly an Yes 61 (33.7) 120 (66.3) issue as well. The mean number of medications taken by LS-CKD No 260 (48.9) 272 (51.1) patients is substantial. Elimination of unnecessary medications Current smoker, n (%) 0.82 should be an ongoing effort to improve patient safety. Yes 20 (48.8) 21 (51.2) Strengths of our study include the large numbers of patients No 298 (44.7) 368 (55.3) in different types of practice settings. In addition the determina- Not specified 3 (50.0) 3 (50.0) tion of medication discrepancies was by a highly rigorous Insurance status, n (%) 0.23 approach with home visits and review of actual medication bot- Medicaid 37 (55.2) 30 (44.8) tles and usage descriptions by the patient and key caregivers. Medicare 180 (45.0) 220 (55.0) Limitations include the geographically limited region covered, No insurance 4 (57.1) 3 (42.9) the metropolitan New York counties of Nassau, Suffolk, Private 100 (41.8) 139 (58.2) Queens, Kings and Manhattan. In addition, there was underre- Mean (6SD) eGFR (mL/min) 19.16 6.6 18.36 6.2 0.08 presentation of individuals with Hispanic ethnicity. Hispanic Mean (6SD) number of 6.86 2.3 9.26 3.8 <0.0001 patients made up only 9% of the study population, compared medications with 17% of the US population. The dataset had a low propor- tion of missing data in that 15% of discrepancies were accom- panied by insufficient documentation of the specific involved medication. In addition, we were unable to determine which Our finding of a high rate of medication discrepancies signifies discrepancies might create a higher level of risk to patients as an important gap in performance and opportunity for improve- no available classification scheme is reliable. Additionally, we ment in LS-CKD. The fact that more than one-third of patients were not able to identify potential intentional discrepancies, for had two or more discrepancies is a strong indicator of the mag- example, if a second physician had changed a medication. nitude of the problem. Finally, our dataset also did not allow for determination of the It is concerning that cardiovascular medications were the underlying reasons for unintentional medication discrepancies. most commonly discrepant class of medication and diuretics In conclusion, we found a high rate of medication discrepan- the fourth most common, because of the hemodynamic and cies in LS-CKD. There is a need to better understand the subject metabolic effects of these agents. This aligns with the finding to help improve medication management and reduce risk for that congestive heart failure as comorbidity was a key inde- medication errors. Future research efforts should focus on pendent predictor of multiple discrepancies. It is likely that con- whether discrepancies relate to patient outcomes and on causa- cordant cardiac disease creates important vulnerabilities in tive factors for medication discrepancies. medication management in LS-CKD. The diseases complicate and confound treatment of each other. Medications such as renin angiotensin aldosterone system inhibitors and diuretics Funding are adjusted frequently in response to cardiac, renal or electro- Part of this work was funded by the Centers for Medicare lyte disturbances. When the medications are changed, commu- nication between physicians may often be suboptimal. This and Medicaid Innovation Program. Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 512 | J. Ibrahim et al. 5. Patel CH, Zimmerman KM, Fonda JR et al. Medication com- Conflict of interest statement plexity, medication number, and their relationships to med- None declared. ication discrepancies. Ann Pharmacother 2016; 50: 534–540 6. Hale J, Neal EB, Myers A et al. Medication discrepancies and References associated risk factors identified in home health patients. Home Healthcare Now 2015; 33: 493–499 1. Tam VC, Knowles SR, Cornish PL et al. Frequency, type and 7. Fishbane S, Agoritsas S, Bellucci A et al. Augmented nurse clinical importance of medication history errors at admis- care management in CKD stages 4 to 5: a randomized trial. sion to hospital: a systematic review. CMAJ 2005; 173: Am J Kidney Dis 2017; 70: 498–505 510–515 8. Wills BM, Darko W, Seabury R et al. Pharmacy impact on 2. Prins MC, Drenth-van Maanen AC, Kok RM et al. Use of a medication reconciliation in the medical intensive care unit. structured medication history to establish medication use at J Res Pharm Pract 2016; 5: 142–145 admission to an old age psychiatric clinic: a prospective 9. Hias J, Van der Linden L, Spriet I et al. Predictors for uninten- observational study. CNS Drugs 2013; 27: 963–969 tional medication reconciliation discrepancies in preadmis- 3. Simoons M, Mulder H, Risselada AJ et al. Medication discrep- sion medication: a systematic review. Eur J Clin Pharmacol ancies at outpatient departments for mood and anxiety dis- 2017; 73: 1355–1377 orders in the Netherlands: risks and clinical relevance. J Clin 10. Pevnick JM, Palmer KA, Shane R et al. Potential benefit of Psychiatry 2016; 77: 1511–1518 electronic pharmacy claims data to prevent medication his- 4. Manley HJ, Cannella CA, Bailie GR et al. Medication-related problems in ambulatory hemodialysis patients: a pooled tory errors and resultant inpatient order errors. J Am Med analysis. Am J Kidney Dis 2005; 46: 669–680 Inform Assoc 2016; 23: 942–950 Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical Kidney Journal Oxford University Press

Medication discrepancies in late-stage chronic kidney disease

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Abstract

Background: Late-stage chronic kidney disease (LS-CKD) can be defined by glomerular filtration rate (GFR) 0–30 mL/min. It is a period of risk for medication discrepancies because of frequent hospitalizations, fragmented medical care, inadequate communication and polypharmacy. In this study, we sought to characterize medication discrepancies in LS-CKD. Methods: We analyzed all patients enrolled in Northwell Health’s Healthy Transitions in LS-CKD program. All patients had estimated GFR 0–30 mL/min, not on dialysis. Medications were reviewed by a nurse at a home visit. Patients’ medication usage and practice were compared with nephrologists’ medication lists, and discrepancies were characterized. Patients were categorized as having either no discrepancies or one or more. Associations between patient characteristics and number of medication discrepancies were evaluated by chi-square or Fisher’s exact test for categorical variables, and two-sample t-test or Wilcoxon text for continuous variables. Results: Seven hundred and thirteen patients with a median age of 70 (interquartile range 58–79) years were studied. There were 392 patients (55.0% of the study population) with at least one medication discrepancy. The therapeutic classes of medications with most frequently occurring medication discrepancies were cardiovascular, vitamins, bone and mineral disease agents, diuretics, analgesics and diabetes medications. In multivariable analysis, factors associated with higher risk of discrepancies were congestive heart failure [odds ratio (OR) 2.13; 95% confidence interval (CI) 1.44–3.16; P¼ 0.0002] and number of medications (OR 1.29; 95% CI 1.21–1.37; P< 0.0001). Conclusions: Medication discrepancies are common in LS-CKD, affect the majority of patients and include high-risk medication classes. Congestive heart failure and total number of medications are independently associated with greater risk for multiple drug discrepancies. The frequency of medication discrepancies indicates a need for great care in medication management of these patients. Key words: chronic renal failure, chronic renal insufficiency, CKD, diuretics, medications Received: July 21, 2017. Editorial decision: October 23, 2017 V C The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 508 | J. Ibrahim et al. care management services to patients with LS-CKD, in an effort Introduction to facilitate care processes and improve preparation for end- Medication discrepancies can be defined as a mismatch stage kidney disease (all such patients were eligible for partici- between a treating physician’s understanding of patients’ cur- pation in the program). The program includes patients with rent medications (including dose and frequency) and the actual CKD Stages 4–5 defined as estimated glomerular filtration rate medications that the patient takes. Discrepancies have the (eGFR) 0–30 mL/min, and excludes patients (i) on dialysis and potential to impair the effectiveness and safety of medication (ii) with significant cognitive impairment, defined as need for treatment. Ideally, the physician conducts a medication recon- assistance with activities of daily living. The program began as ciliation with the patient and educates patients about medica- a pilot, entered a full implementation phase and subsequently tions. In addition, the physician should maintain good had an imbedded randomized controlled trial (RCT) [7] and a communication with other medical providers regarding medi- 3-year period of funding by Centers for Medicare and Medicaid cation changes. In actual practice, however, medication discrep- Innovation program Health Care Innovations Award. ancies are frequent, occurring in 34–95% of patients at the All patients enrolled throughout all program phases from time of admission for acute hospitalizations or psychiatric clin- October 2012 to December 2016 were included in the current ics [1, 2]. When discrepancies occur there may be failure to rec- analysis except for patients from the RCT control group. The ognize symptoms and signs as medication side effects and justification for inclusion of all of these program phase patients increased risk for adverse events [3]. A systematic review of is that all had exactly the same initial home visit with medica- acute hospitalizations found that 11–59% of medication discrep- tion review conducted prior to any other interventions. The pro- ancies were clinically important and 39% had potential to cause gram functions and patients were enrolled, from Nassau, moderate-to-severe patient harm [1]. Suffolk, Queens, Kings and New York counties of New York To date there have been no studies of medication discrepan- State. There have been seven different participating nephrology cies in late-stage chronic kidney disease (LS-CKD). LS-CKD can groups. Three of the offices were private practices, four were be defined as CKD Stages 4–5, prior to the initiation of renal academic nephrology practices. There was no standardized replacement therapy. There are several reasons to suspect that method for medication history-taking among the nephrologists. medication discrepancies may be common in LS-CKD. Patients often take multiple medications, reaching a mean of 11.8 by the Medication review time of dialysis need [4]. Polypharmacy and conflicting dosing schedules are never easy for patients to manage. This is prob- Medication discrepancies were defined from the perspective of ably more true in LS-CKD, where patients are usually older and the nephrologist. Most patients had a recent visit with the have comorbidities including cognitive decline. In addition, nephrologist at the time of program enrollment. After enroll- patients with LS-CKD often have multiple different physicians ment, a home visit was conducted by a program nurse. The involved in their care. The result can sometimes be care frag- nephrologist’s medication list was printed from the electronic mentation with confusion created by numerous drug and dos- health record (EHR) prior to the home visit. All program neph- ing changes. Frequent hospitalizations among patients with LS- rologists’ EHRs prompt for medication reconciliation at all CKD result in medication and dose changes, often without patient visits. adequate reconciliation and communication among providers Medication reviews were all conducted by one of the pro- at hospital discharge. In general, communication problems are gram’s registered nurses. All took place during an initial home a recurring theme throughout the care system, contributing to visit that occurred prior to any other program interventions. medication risk. The purpose of the home visit is to begin to build a trusting rela- Since 2012, we have operated the Healthy Transitions care tionship, to assess the patient’s knowledge regarding their kid- management program for patients with LS-CKD. Nephrologist ney disease, to initiate education regarding modality options for treatment is supplemented by nursing care management serv- renal replacement therapy and diet and most relevant to the ices and advanced informatics. All program patients have an current analysis, to conduct a complete medication review. initial home visit, which offers a unique opportunity for medi- The medication portion of the visit begins with the nurse cation review. The visit occurs as the initial program contact, so asking the patient and key care giver where in the home medi- the medication reviews are free from any interventional effect. cations are kept, and how the patient carries medications if At the visit, the patient and key caregivers show the bottles of needed during the day. Next, the patient was asked to remove medications being used and explain how many pills are taken their currently used medication jars from their medicine cabi- and how frequently. Because this occurs in the patient’s home, net or other storage area. In many cases, the nurse helped the with the actual drugs consumed, it is a rigorous form of medica- patient with this task. Each medication jar was reviewed indi- tion review. In this analysis, we sought to utilize this uniquely vidually with attention to accuracy. The patient was questioned clear view of medication use to better understand and charac- on (i) if the medication was currently being taken, (ii) how many terize the frequency, type and risk factors for medication dis- pills, (iii) how many times a day and (iv) were there any other crepancies in LS-CKD. We are aware of no previous studies of medications, including over-the-counter agents, herbals or vita- this subject in CKD and studies in other populations have often mins, that the patient took. The patient was questioned on relied on health record reviews rather than direct patient inter- other medications that were on the nephrologist’s medication views [5, 6]. list but that the patient was not taking at that time. For any dif- ference between the nephrologist’s list of medications and the patient’s actual medication consumption, a discrepancy and its Materials and methods characteristics were recorded. Although all patients had subse- Patient population quent medication reviews as part of the program, only the base- The Healthy Transitions in Late Stage Kidney Disease program line review was included in the current analysis to avoid began operation in October 2012. The program provides nursing potential bias introduced by program interventions. Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Medication discrepancies in LS-CKD | 509 Table 1. Patient characteristics Table 2. Frequency distribution of number of medication discrepan- cies per patient Characteristic n (% or SD) Medication discrepancies per patient Number Percentage Mean age (years) 67.5 (15.9) Male 403 (56.5) 0 321 45.0 Race 1 125 17.5 White 434 (60.9) 2 83 11.6 Black 174 (24.4) 3 57 8.0 Other or multiracial 67 (9.4) 4 41 5.8 Asian 38 (5.3) 5 19 2.7 Hispanic ethnicity 64 (9.0) 6 24 3.4 Hypertension 662 (92.8) 7 14 2.0 Diabetes 364 (51.1) 8 4 0.6 Congestive heart failure 181 (25.3) 9 5 0.7 Primary insurance type 10 8 1.1 Medicare 400 (56.1) 11 3 0.4 Private 239 (33.5) 12 3 0.4 Medicaid 67 (9.4) 13 1 0.1 No insurance 7 (1) 14 2 0.3 Active smoker 15 1 0.1 Yes 41 (5.8) 16 1 0.1 No 666 (93.4) 17 0 0.0 Not specified 6 (0.8) 18 1 0.1 Mean eGFR (mL/min) 18.66 6.4 eGFR category (mL/min) <10 52 (7.3) Results 10–15 188 (26.4) 15.1–20 178 (25) A total of 713 patients were reviewed who were enrolled from >20 295 (41.4) October 2012 to December 2016. In the entire cohort, the median Mean number of medications 8.1 (3.4) age was 70 [interquartile range (IQR) 58–79] years, 56.5% were men, 24.4% were black, 60.9% were white, 9% were Hispanic, 51.1% were diabetic and 92.8% had hypertension. Patient char- Statistical analyses acteristics are displayed in Table 1. The distribution of eGFR was <10 mL/min, 7.3%; 10–15 mL/min, 26.3%; 15.1–20 mL/min, Baseline patient demographics (age, gender, race, ethnicity, 25%; > 20 mL/min, 41.4%. Six patients had previously undergone hypertension, diabetes, congestive heart failure, insurance type, kidney transplantation. The median time from last nephrologist smoking status and eGFR) were described using categorical or medication reconciliation to nurse visit was 3 (IQR 1–6) days. continuous variables as appropriate. In the entire patient There were 392 patients (55.0% of the study population) with cohort, the frequency, proportion and type of medication dis- at least one medication discrepancy. The total number of dis- crepancy was described. Categorization of medication discrep- crepancies was 1280, and the median number of discrepancies ancy type was determined a priori, and included therapeutic per patient (in patients with at least 1 discrepancy) was 2 (IQR class, and any difference between the nephrologist’s list of 1–4). The distribution of discrepancies per patient is displayed medications and the patient’s actual medication consumption in Table 2. There were 321 patients with zero discrepancies (i.e. patient taking medication not on nephrologist’s list, patient (45%), 125 patients with one discrepancy and 83 patients with not taking medication on nephrologist’s list, different dose and/ two discrepancies. There were 12.1% of patients with five or or different frequency, drug discontinued and patient taking, more discrepancies. The most common type of discrepancies drug discontinued and still on nephrologist’s list or patient not were patients taking a medication not on nephrologist’s list taking medication as instructed). (49.1% of discrepancies), patients not taking a medication on To evaluate patient characteristics associated with risk for the nephrologist’s list (22.7%), different dose (18.4%) and differ- multiple medication discrepancies, we first divided patients ent frequency (5%) (Figure 1). The therapeutic medication into two categories, one with no medication discrepancies and classes with the greatest number of discrepancies were cardio- one with one or more discrepancies. We decided on categoriza- vascular (15.5% of discrepancies), vitamins (11.0%), bone and tion prior to any inspection of results by category. The variables mineral disease agents (8.9%), diuretics (7.0%), analgesics (6.7%) chosen for study were based on availability and then on those and diabetes medications (5.0%) (Figure 2). The types of discrep- with potential causal pathways. In an initial univariable analy- ancies were similar for different drug classes. Among cardiovas- sis, we used chi-square or Fisher’s exact test as appropriate for cular drugs, the subcategories with the greatest number of categorical variables, and two-sample t-test or Wilcoxon text as discrepancies were renin angiotensin aldosterone system inhib- appropriate for continuous variables, to evaluate the associa- tion of each patient characteristic with the outcome of number itors (66% of cardiovascular discrepancies) and beta blockers of medication discrepancies (no discrepancies versus one or (24%). When assessing the outcome of multiple medication dis- more). In subsequent multivariable logistic regression analysis, factors that were found statistically significant in the univari- crepancies, there were 321 patients with no discrepancies, and able analysis plus age and gender were taken as the candidate 392 patients with one or more discrepancies. In a univariable variables for the final model. We conducted all analyses using analysis, the following patient characteristics were found to be SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). associated with medication discrepancies (P< 0.05): congestive Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 510 | J. Ibrahim et al. Paent taking Paent not Different dose Different Different dose Drug Drug Paent not medicaon taking a frequency and frequency disconnued, disconnued taking not on medicaon on paent sll but sll on medicaon as nephrologist's nephrologist's taking nephrologist's instructed list list list Fig. 1. Frequency distribution of types of medication discrepancy, from most common to least common. Fig. 2. Frequency of medication discrepancies by therapeutic class. heart failure, white race, hypertension and number of medica- taken that were not on the nephrologist’s list, medications on tions taken (Table 3). In subsequent multivariable logistic the nephrologist’s list that were not being taken, and dose and regression analysis, factors that were found statistically signifi- frequency differences. cant in the univariable analysis plus age and gender were taken Effective medication management is achieved by optimizing as the candidate variables for the final model. After backward the benefits and reducing risks of treatment. Ongoing medica- variable selection, factors in the final model independently tion review and reconciliation by the provider is essential for longitudinal maintenance of medication safety. When discrep- associated with higher risk of one or more medication discrep- ancies develop between the medications that the provider ancies compared with zero discrepancy were congestive heart failure [odds ratio (OR) 2.13; 95% confidence interval (CI) 1.44– intends for the patient to be taking and those actually being 3.16; P ¼ 0.0002] and number of medications (OR 1.29; 95% CI used, there is increased risk for loss of efficacy or adverse events. Discrepancies can be highly relevant, with a recent 1.21–1.37; P< 0.0001) (Table 4). study finding that in intensive care units, 17.1% of discrepancies were serious or potentially life-threatening [8]. Discussion In the treatment of patients with LS-CKD, the medication We found that more than half of patients with LS-CKD had at management process is fraught with polypharmacy, extensive least one discrepancy between the nephrologist’s medication comorbidity and altered medication pharmacology due to record and the medications actually being consumed. reduced renal function, fragmented medical care and frequent Approximately one-third of patients had two or more discrep- hospitalizations. The nephrologist plays a key and often princi- ancies. Most discrepancies were related to medications being pal role in maintenance of medication safety in these patients. Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 Number of DIscrepancies Number of Discrepancies Medication discrepancies in LS-CKD | 511 Table 3. Univariate association of patient characteristics and num- Table 4. Multivariable analysis for medication discrepancy (logistic ber of medication discrepancies regression) Number of medication Multivariable analysis for medication discrepancy discrepancies Odds ratio Zero Variables (95% confidence interval) P-value discrepancies 1 discrepancy CHF (yes versus no) 2.13 (1.44–3.16) 0.0002 Patient characteristics (n¼ 321) (n¼ 392) P-value Number of medications 1.29 (1.21–1.37) <0.0001 Mean number of 0 3.3 (2.8) NA CHF, congestive heart failure. discrepancies (SD) Mean (6SD) age (years) 68.06 15.8 67.36 16.1 0.40 complex, chaotic setting is fertile ground for medication dis- Gender, n (%) 0.15 Male 172 (42.7) 231 (57.3) crepancies and complications. Female 149 (48.1) 161 (51.9) We also found that the number of medications that patients Race, n (%) 0.03 were taking was associated with greater risk for discrepancies. White 177 (40.8) 257 (59.2) This is an intuitive finding and consistent with existing knowl- Black 89 (51.2) 85 (48.9) edge [9]. In LS-CKD, patients take a large number of medications Other or multiracial 33 (49.3) 34 (50.8) and it is wise to review ongoing need for all chronic medications. Asian 22 (57.9) 16 (42.1) Reduction in medication discrepancies in LS-CKD requires a Hispanic ethnicity, n (%) 0.27 more intensive approach to medication reconciliation. Sharing of Yes 33 (51.6) 31 (48.4) medication information between healthcare providers and accu- No 288 (44.4) 361 (55.6) rate is a prerequisite. Incorporation of pharmacy information on Hypertension, n (%) 0.01 medication fill histories into EHRs holds great promise [10]. Ideally, Yes 289 (43.7) 373 (56.3) the physician’s medication record should reflect the time of the No 32 (62.8) 19 (37.2) last patient visit or other communication, but annotated with sub- Diabetes mellitus, n (%) 0.90 sequent activity from other providers and pharmacies. Improved Yes 163 (44.8) 201 (55.2) patient education is important as well, as it is probable that better No 158 (45.3) 191 (54.7) understanding of medications and their purpose would enhance Congestive heart 0.0004 failure, n (%) patient adherence and limit errors. Polypharmacy is certainly an Yes 61 (33.7) 120 (66.3) issue as well. The mean number of medications taken by LS-CKD No 260 (48.9) 272 (51.1) patients is substantial. Elimination of unnecessary medications Current smoker, n (%) 0.82 should be an ongoing effort to improve patient safety. Yes 20 (48.8) 21 (51.2) Strengths of our study include the large numbers of patients No 298 (44.7) 368 (55.3) in different types of practice settings. In addition the determina- Not specified 3 (50.0) 3 (50.0) tion of medication discrepancies was by a highly rigorous Insurance status, n (%) 0.23 approach with home visits and review of actual medication bot- Medicaid 37 (55.2) 30 (44.8) tles and usage descriptions by the patient and key caregivers. Medicare 180 (45.0) 220 (55.0) Limitations include the geographically limited region covered, No insurance 4 (57.1) 3 (42.9) the metropolitan New York counties of Nassau, Suffolk, Private 100 (41.8) 139 (58.2) Queens, Kings and Manhattan. In addition, there was underre- Mean (6SD) eGFR (mL/min) 19.16 6.6 18.36 6.2 0.08 presentation of individuals with Hispanic ethnicity. Hispanic Mean (6SD) number of 6.86 2.3 9.26 3.8 <0.0001 patients made up only 9% of the study population, compared medications with 17% of the US population. The dataset had a low propor- tion of missing data in that 15% of discrepancies were accom- panied by insufficient documentation of the specific involved medication. In addition, we were unable to determine which Our finding of a high rate of medication discrepancies signifies discrepancies might create a higher level of risk to patients as an important gap in performance and opportunity for improve- no available classification scheme is reliable. Additionally, we ment in LS-CKD. The fact that more than one-third of patients were not able to identify potential intentional discrepancies, for had two or more discrepancies is a strong indicator of the mag- example, if a second physician had changed a medication. nitude of the problem. Finally, our dataset also did not allow for determination of the It is concerning that cardiovascular medications were the underlying reasons for unintentional medication discrepancies. most commonly discrepant class of medication and diuretics In conclusion, we found a high rate of medication discrepan- the fourth most common, because of the hemodynamic and cies in LS-CKD. There is a need to better understand the subject metabolic effects of these agents. This aligns with the finding to help improve medication management and reduce risk for that congestive heart failure as comorbidity was a key inde- medication errors. Future research efforts should focus on pendent predictor of multiple discrepancies. It is likely that con- whether discrepancies relate to patient outcomes and on causa- cordant cardiac disease creates important vulnerabilities in tive factors for medication discrepancies. medication management in LS-CKD. The diseases complicate and confound treatment of each other. Medications such as renin angiotensin aldosterone system inhibitors and diuretics Funding are adjusted frequently in response to cardiac, renal or electro- Part of this work was funded by the Centers for Medicare lyte disturbances. When the medications are changed, commu- nication between physicians may often be suboptimal. This and Medicaid Innovation Program. Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018 512 | J. Ibrahim et al. 5. Patel CH, Zimmerman KM, Fonda JR et al. Medication com- Conflict of interest statement plexity, medication number, and their relationships to med- None declared. ication discrepancies. Ann Pharmacother 2016; 50: 534–540 6. Hale J, Neal EB, Myers A et al. Medication discrepancies and References associated risk factors identified in home health patients. Home Healthcare Now 2015; 33: 493–499 1. Tam VC, Knowles SR, Cornish PL et al. Frequency, type and 7. Fishbane S, Agoritsas S, Bellucci A et al. Augmented nurse clinical importance of medication history errors at admis- care management in CKD stages 4 to 5: a randomized trial. sion to hospital: a systematic review. CMAJ 2005; 173: Am J Kidney Dis 2017; 70: 498–505 510–515 8. Wills BM, Darko W, Seabury R et al. Pharmacy impact on 2. Prins MC, Drenth-van Maanen AC, Kok RM et al. 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J Am Med analysis. Am J Kidney Dis 2005; 46: 669–680 Inform Assoc 2016; 23: 942–950 Downloaded from https://academic.oup.com/ckj/article-abstract/11/4/507/4654836 by Ed 'DeepDyve' Gillespie user on 07 August 2018

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Clinical Kidney JournalOxford University Press

Published: Aug 1, 2018

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