Delirium, Frailty, and Mortality: Interactions in a Prospective Study of Hospitalized Older People

Delirium, Frailty, and Mortality: Interactions in a Prospective Study of Hospitalized Older People Abstract Background It is unknown whether the association between delirium and mortality is consistent for individuals across the whole range of health states. A bimodal relationship has been proposed, where delirium is particularly adverse for those with underlying frailty, but may have a smaller effect (perhaps even protective) if it is an early indicator of acute illness in fitter people. We investigated the impact of delirium on mortality in a cohort simultaneously evaluated for frailty. Methods We undertook an exploratory analysis of a cohort of consecutive acute medical admissions aged ≥70. Delirium on admission was ascertained by psychiatrists. A frailty index (FI) was derived according to a standard approach. Deaths were notified from linked national mortality statistics. Cox regression was used to estimate associations between delirium, frailty, and their interactions on mortality. Results The sample consisted of 710 individuals. Both delirium and frailty were independently associated with increased mortality rates (delirium: HR 2.4, 95% CI 1.8–3.3, p < .01; frailty (per SD): HR 3.5, 95% CI 1.2–9.9, p = .02). Estimating the effect of delirium in tertiles of FI, mortality was greatest in the lowest tertile: tertile 1 HR 3.4 (95% CI 2.1–5.6); tertile 2 HR 2.7 (95% CI 1.5–4.6); tertile 3 HR 1.9 (95% CI 1.2–3.0). Conclusion Although delirium and frailty contribute to mortality, the overall impact of delirium on admission appears to be greater at lower levels of frailty. In contrast to the hypothesis that there is a bimodal distribution for mortality, delirium appears to be particularly adverse when precipitated in fitter individuals. Outcomes, Longevity, Risk factors Delirium, characterized by a fluctuating disturbance in arousal, attention, and cognition secondary to an acute medical condition, is common, affecting 18%–35% of general medical inpatients, 8%–17% of older patients attending emergency departments, and 51% of patients in postacute care (1–3). It is associated with increased length of stay, institutionalization, and progression of dementia (4–7). Delirium is widely understood to be associated with mortality, with an overall HR = 1.9 consistent across a number of studies identified in a systematic review (4). This meta-analysis included observational studies adjusting for chronic comorbidity or acute illness, though none accounted for both. Therefore, an important unanswered question is whether the association remains independent of acute and chronic health factors that might otherwise drive mortality (8). Indeed, a more nuanced understanding of delirium and mortality is relevant given the proposal that the relationship may be bimodal (9). That is, although delirium may have catastrophic outcomes in some patients, for others, it may be an early indicator of acute illness leading to earlier recognition and treatment, perhaps even being protective. Accounting for underlying frailty may provide an insight into the relationship between delirium and mortality because frailty itself is so closely related to risk of both mortality and delirium. One view of frailty describes the gradual accumulation of deficits as individuals’ age, which results in loss of physiological reserve (physical, mental, and functional) and increased vulnerability to insults (10). Taking baseline functional status and chronic comorbidity into account can resolve the issue of “unmeasured heterogeneity”—the factors that increase risk despite the same level of acute illness (11). Our aim was to investigate the effect of delirium and frailty on mortality in a large cohort of acutely unwell adults, setting out to answer the following questions (i): Is delirium associated with mortality, even after adjusting for underlying frailty? (ii) Is there an interaction between delirium and frailty? (iii) Is the relationship with frailty and mortality linear across the range of frailty states and does this change according to the presence of delirium? Methods We undertook an exploratory analysis of a cohort prospectively ascertaining outcomes from acutely hospitalized elders. Participants were recruited as previously described (12). Briefly, all patients aged ≥70 years consecutively admitted to the acute medical unit between June 2007 and December 2007 were screened for inclusion. Exclusion criteria were (i) admission length of under 48 hours and (ii) insufficient English to be assessed for cognitive and mental status. The Royal Free Hospitals NHS Trust Ethics Committee gave ethical approval (06/Q0501/31). Outcomes The study was notified of all deaths through linkage with the UK Office for National Statistics for up to 3 years following the index admission. Exposures Delirium All participants were evaluated on admission by trained psychiatrists. Formal cognitive testing included the Mini-Mental State Examination (13) and delirium was defined using the Confusion Assessment Method algorithm (14). Key symptoms such as inattention were identified through the “serial 7s” or “W-O-R-L-D backwards” tasks. Other items such as acute onset, disorganized thinking and altered level of consciousness, and degree of fluctuation of these symptoms were ascertained through clinical assessment which included information from ward staff and the medical chart. Frailty A 31-item frailty index (FI) was constructed according to a standardized procedure (15), where the following variables were included: comorbidity (13 variables), examination findings (5 variables), laboratory findings (9 variables), and functional status (4 variables) (Table 1). These variables were selected to encompass the full range of acute and chronic health factors that could account for any observed association with mortality. All items were given a binary score (0 = no deficit, 1 = deficit present). For each participant, an FI score was calculated by dividing the number of deficits present by the denominator of 31 maximum deficits, resulting in a score between 0 and 1. For example, for an individual with 10 deficits present out of 31, their FI score would be 10/31 = 0.32. Across several iterations of FI in several hundred datasets, the usual upper limit of frailty observed asymptotically approaches 0.70. In our dataset, data were not missing for more than 6% in each variable. Table 1. Frailty Index Variables   Frequency (%)  Comorbidities   Congestive heart failure  21   Dementia  24   Diabetes  20   COPD  17   Metastatic disease  6   Peripheral vascular disease  7   Severe liver disease  Any prior tumor  12 16   Peptic ulcer disease  9   History of MI  28   History of CVA  27   Psychiatric history  21   Alcohol consumption > 1 unit/wk  27  Examination findings   Heart rate (bpm) <70 or >109  34   Respiratory rate (breaths/min) <12 or >24  15   Temperature (oC) <36 or >38.4  10   Glasgow coma score ≤13  13   MAP (mmHg) <70 or >109  28  Laboratory findings   Packed cell volume (%) <30 or >45.9  18   Potassium (mMol/L) <3.5 or >5.4  13   Creatinine (μmol/L) <60 or >140  37   Sodium (mmol/L) <130 or >149  13   White cells (×109/L) <3 or >14.9  21   Platelets (×109/L) <150 or >400  21   Urea (mmol/L) <2.5 or >7  62   CRP (mg/dL) >5  73   Albumin (g/L) <35 or >55  19  Functional status   Care home resident  28   Urinary and/or faecal incontinence  23   Pressure sores present  9   Polypharmacy (>4 medications)  52    Frequency (%)  Comorbidities   Congestive heart failure  21   Dementia  24   Diabetes  20   COPD  17   Metastatic disease  6   Peripheral vascular disease  7   Severe liver disease  Any prior tumor  12 16   Peptic ulcer disease  9   History of MI  28   History of CVA  27   Psychiatric history  21   Alcohol consumption > 1 unit/wk  27  Examination findings   Heart rate (bpm) <70 or >109  34   Respiratory rate (breaths/min) <12 or >24  15   Temperature (oC) <36 or >38.4  10   Glasgow coma score ≤13  13   MAP (mmHg) <70 or >109  28  Laboratory findings   Packed cell volume (%) <30 or >45.9  18   Potassium (mMol/L) <3.5 or >5.4  13   Creatinine (μmol/L) <60 or >140  37   Sodium (mmol/L) <130 or >149  13   White cells (×109/L) <3 or >14.9  21   Platelets (×109/L) <150 or >400  21   Urea (mmol/L) <2.5 or >7  62   CRP (mg/dL) >5  73   Albumin (g/L) <35 or >55  19  Functional status   Care home resident  28   Urinary and/or faecal incontinence  23   Pressure sores present  9   Polypharmacy (>4 medications)  52  View Large Statistical Analysis Proportional hazards for mortality were assessed in a series of Kaplan–Meier plots and Cox regression models, where outcome was date of death. Postestimation procedures included Schoenfeld residuals for checking assumptions of proportionality. Multiplicative interactions between delirium and FI score were assessed in order to estimate the association of delirium and mortality with respect to underlying frailty, using α = .1 as a threshold for type 1 errors. This approach has been justified by quantifying the gains in power using a less stringent α for samples of this size where interactions between dichotomous (delirium status) and continuous (FI) variables are being considered (16). Linearity of any association with mortality across the distribution of frailty, restricted cubic splines were fitted (four knots), plotting log-hazard ratio for mortality against FI score, stratified by delirium status. Stata version 14.1 was used for all statistical procedures. Results The sample contained 710 individuals, with a mean age of 83.1 years (standard deviation 7.41), and 59% were female (Supplementary Table 1). A diagnosis of dementia was present in 42%. At the end of the 3-year follow-up, 340 individuals remained alive and 370 had died (median follow-up 5 months, IQR 1 to 17 months). The prevalence of delirium on admission was 10.3% (n = 73). The prevalence of deficits on index items ranged from 6% (metastatic disease) to 73% (CRP > 5) (Table 1). The mean FI score was 0.23 (SD 0.096, upper limit 0.55), with a broadly normal distribution (Supplementary Figure 1). Delirium, Frailty, and Mortality Delirium was strongly associated with mortality, with 59 (81%) dying within the next 3 years, compared with 311 (49%) without delirium. Tertiles of FI score (first 0–0.19; second 0.20–0.26; third 0.27–0.55) also showed increasing associations with mortality (first 48%; second 51%; third 60%). Both delirium and frailty were crudely associated with increased hazard for death (delirium: 2.4, 95% CI 1.8–3.2; FI [per SD]; HR 5.9 [95% CI 2.1–16]). This remained the case with a multivariable model including both terms adjusted by age and sex (delirium: HR 2.4, 95% CI 1.8–3.3, p < .01; frailty [per SD]: HR 3.5, 95% CI 1.2–9.9, p = .02; Table 2). Kaplan–Meier survival curves (adjusted by age and sex), according to delirium status, demonstrated worse survival for those with delirium (Figure 1). Table 2. Survival Analysis Showing the Associations Between Delirium, Frailty, and Mortality   Univariable  Multivariable  Hazard ratio  95% CI  p  Hazard ratio  95% CI  p  Delirium  2.44  1.85  3.23  <.01  2.37  1.78  3.15  <.01  Frailty index  5.90  2.14  16.2  <.01  3.37  1.18  9.60  .02    Univariable  Multivariable  Hazard ratio  95% CI  p  Hazard ratio  95% CI  p  Delirium  2.44  1.85  3.23  <.01  2.37  1.78  3.15  <.01  Frailty index  5.90  2.14  16.2  <.01  3.37  1.18  9.60  .02  Note: N = 708, all models adjusted by age and sex. Mean FI score = 0.23 (SD 0.09). View Large Figure 1. View largeDownload slide Kaplan–Meier curves showing survival of cohort, by delirium status, adjusted by age and sex. Figure 1. View largeDownload slide Kaplan–Meier curves showing survival of cohort, by delirium status, adjusted by age and sex. Interactions Between Delirium and Frailty We estimated the effect of delirium on mortality according to each tertile of FI. There was an inverse gradient of association, where stronger associations were observed in the fittest group (tertile 1 HR 3.4 [95% CI 2.1–5.6]; tertile 2 HR 2.7 [95% CI 1.5–4.6]; tertile 3 HR 1.9 [95% CI 1.2–3.0]). The delirium–frailty interaction was statistically significant if α = .1 (p = .07). Linearity of Mortality Gradients in Relation to Delirium Status Restricted cubic splines of the log-HR for mortality against FI score were linear across the four knots, giving no indication that mortality is driven by a particular portion of the frailty continuum (Figure 2). When additionally accounting for delirium status, the splines remained linear, though crossed over to suggest delirium may have greater associations with mortality at lower degrees of frailty and lower associations at higher degrees of frailty (p = .07; Figure 2). Figure 2. View largeDownload slide Relationship between frailty and mortality, by delirium status restricted cubic splines modeling relationship between frailty and mortality. The linearity suggests a continuous relationship between frailty and mortality. When stratified by delirium, the lines intersect suggesting greater effects with delirium at lower levels of mortality (p = .07). Figure 2. View largeDownload slide Relationship between frailty and mortality, by delirium status restricted cubic splines modeling relationship between frailty and mortality. The linearity suggests a continuous relationship between frailty and mortality. When stratified by delirium, the lines intersect suggesting greater effects with delirium at lower levels of mortality (p = .07). Discussion We found that on admission, both delirium and frailty were independently associated with increased risk of death, beyond that expected from all the acute and chronic health factors measured in this study. Moreover, delirium and frailty appeared to interact in such a way that although delirium increases the risk of death at all levels of frailty, the relative impact of this association was greatest in fitter patients. The relationship between frailty and mortality was linear, suggesting that mortality is not being driven by a subset of individuals. Delirium status had a small influence on these relationships. Taken together, our results suggest that delirium itself independently confers a mortality risk, and this risk applies whatever the underlying degree of frailty. Our data illustrate some of the advantages of using a FI in studying cognitive disorders by quantifying a multidimensional assessment, thereby allowing other “non-neurological factors” that nevertheless contribute to patient outcomes (eg nutritional status, polypharmacy, electrolyte abnormalities) to be considered (17). Our findings should be treated with caution. Data were collected from a single site, albeit a large London secondary care hospital with unselected medical admissions from a population of 1.2 million people and high generalizability. Secondly, despite a standardized protocol and training procedure, more than one rater performed the psychiatric evaluation, possibly introducing inter-rater variability. Thirdly, patients were assessed for delirium on admission only (defined as within 72 hours of admission). Any patients developing delirium after this point would not be included in this analysis, possibly underestimating the effects of delirium. Strengths included large sample size and validated assessments undertaken by specialist diagnosticians. Moreover, a wide variety of variables were available to construct a broad FI. The specific inclusion of physiological and laboratory items may generally relate to acute illness, and it is becoming clear that use of such measures is nonetheless informative for use in a FI (18). Although functional items were under-represented, for the requirements of this specific analysis, the combination of acute and chronic health factors made for a particularly robust FI measure. To understand why the relative risk is higher in fit patients compared with frail patients, the insult causing delirium in fit individuals needs to be large, in order to overcome their physiological and cognitive reserve. Another reason for delirium in fitter individuals may be a distinct neurological precipitant which could drive the poorer prognosis in these patients. Both frailty and delirium are associated with vulnerability and lack of physiological and cognitive reserve to insults, though few studies have been able to address this directly (19). One study found that individuals without a prior diagnosis of dementia (a marker of frailty and reserve) had a worse prognosis from delirium than those with a diagnosis, supporting our finding that fitter patients may incur a worse prognosis (20). A separate finding that mortality after delirium appears to arise independently of illness severity or frailty (21). We report a similar distribution of FI scores, though our study was large enough to explore the interactions presented here. The mechanisms by which delirium independently increases risk of death (after adjusting for illness severity) remain unclear. Delirium complicates and impairs recovery—patients with delirium are more likely to receive psychotropic drugs, more likely to fall, and less likely to mobilize effectively during and after their illness, all of which may have an adverse impact on survival (22). In addition, they may be less likely to maintain adequate hydration and malnutrition, and be less compliant with medication. Of particular concern might be hypoactive delirium, where reduced arousal might lead to aspiration pneumonia. Overall, such findings serve to emphasize the emergency nature of delirium. To conclude, in this exploratory analysis of a large cohort of consecutive admissions to an acute adult medical unit, we found that both delirium and frailty independently increase the risk of death. In addition, the risk of death is higher in delirious patients at all levels of frailty. The most striking finding using this approach is that the risk of death from delirium is highest in the fittest patients. This highlights the crucial importance of preventing, detecting, and treating delirium in any patient, and recognizing it as a serious condition with prognostic significance. Supplementary Material Supplementary data are available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding The original study was funded by the Medical Research Council (UK) Special Training Fellowship in Health Services Research to E.S. D.D. is funded through a Wellcome Trust Intermediate Clinical Fellowship (WT107467). Conflict of interest statement None declared. References 1. Siddiqi N, House AO, Holmes JD. Occurrence and outcome of delirium in medical in-patients: a systematic literature review. Age Ageing . 2006; 35: 350– 364. doi: 10.1093/ageing/afl005. Google Scholar CrossRef Search ADS PubMed  2. Kiely DK, Bergmann MA, Jones RN, Murphy KM, Orav EJ, Marcantonio ER. Characteristics associated with delirium persistence among newly admitted post-acute facility patients. J Gerontol A Biol Sci Med Sci . 2004; 59: 344– 349. Google Scholar CrossRef Search ADS PubMed  3. Jackson TA, Gladman JR, Harwood RHet al.   Challenges and opportunities in understanding dementia and delirium in the acute hospital. PLoS Med . 2017; 14: e1002247. doi: 10.1371/journal.pmed.1002247. Google Scholar CrossRef Search ADS PubMed  4. Witlox J, Eurelings LS, de Jonghe JF, Kalisvaart KJ, Eikelenboom P, van Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA . 2010; 304: 443– 451. doi: 10.1001/jama.2010.1013. Google Scholar CrossRef Search ADS PubMed  5. Gross AL, Jones RN, Habtemariam DAet al.   Delirium and long-term cognitive trajectory among persons with dementia. Arch Intern Med . 2012; 172: 1324– 1331. doi: 10.1001/archinternmed.2012.3203. Google Scholar CrossRef Search ADS PubMed  6. Davis DH, Muniz-Terrera G, Keage HAet al.  ; Epidemiological Clinicopathological Studies in Europe (EClipSE) Collaborative Members. Association of delirium with cognitive decline in late life: a neuropathologic study of 3 population-based cohort studies. JAMA Psychiatry . 2017; 74: 244– 251. doi: 10.1001/jamapsychiatry.2016.3423. Google Scholar CrossRef Search ADS PubMed  7. Davis DH, Muniz Terrera G, Keage Het al.   Delirium is a strong risk factor for dementia in the oldest-old: a population-based cohort study. Brain . 2012; 135: 2809– 2816. doi: 10.1093/brain/aws190. Google Scholar CrossRef Search ADS PubMed  8. Bellelli G, Frisoni GB, Turco R, Lucchi E, Magnifico F, Trabucchi M. Delirium superimposed on dementia predicts 12-month survival in elderly patients discharged from a postacute rehabilitation facility. J Gerontol A Biol Sci Med Sci . 2007; 62: 1306– 1309. Google Scholar CrossRef Search ADS PubMed  9. Jackson TA, Wilson D, Richardson S, Lord JM. Predicting outcome in older hospital patients with delirium: a systematic literature review. Int J Geriatr Psychiatry . 2016; 31: 392– 399. doi: 10.1002/gps.4344. Google Scholar CrossRef Search ADS PubMed  10. Kelaiditi E, Andrieu S, Cantet C, Vellas B, Cesari M; ICTUS/DSA Group. Frailty index and incident mortality, hospitalization, and institutionalization in Alzheimer’s disease: data from the ICTUS study. J Gerontol A Biol Sci Med Sci . 2016; 71: 543– 548. doi: 10.1093/gerona/glv137. Google Scholar CrossRef Search ADS PubMed  11. Rockwood K, Blodgett JM, Theou Oet al.   A frailty index based on deficit accumulation quantifies mortality risk in humans and in mice. Sci Rep . 2017; 7: 43068. doi: 10.1038/srep43068. Google Scholar CrossRef Search ADS PubMed  12. Sampson EL, Blanchard MR, Jones L, Tookman A, King M. Dementia in the acute hospital: prospective cohort study of prevalence and mortality. Br J Psychiatry . 2009; 195: 61– 66. doi: 10.1192/bjp.bp.108.055335. Google Scholar CrossRef Search ADS PubMed  13. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res . 1975; 12: 189– 198. Google Scholar CrossRef Search ADS PubMed  14. Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med . 1990; 113: 941– 948. Google Scholar CrossRef Search ADS PubMed  15. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr . 2008; 8: 24. doi: 10.1186/1471-2318-8-24. Google Scholar CrossRef Search ADS PubMed  16. Durand CP. Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study. PLoS One . 2013; 8: e71079. doi: 10.1371/journal.pone.0071079. Google Scholar CrossRef Search ADS PubMed  17. Canevelli M, Cesari M, Remiddi Fet al.   Promoting the assessment of frailty in the clinical approach to cognitive disorders. Front Aging Neurosci . 2017; 9: 36. doi: 10.3389/fnagi.2017.00036. Google Scholar CrossRef Search ADS PubMed  18. Blodgett JM, Theou O, Howlett SE, Wu FC, Rockwood K. A frailty index based on laboratory deficits in community-dwelling men predicted their risk of adverse health outcomes. Age Ageing . 2016; 45: 463– 468. doi: 10.1093/ageing/afw054. Google Scholar CrossRef Search ADS PubMed  19. Drubbel I, de Wit NJ, Bleijenberg N, Eijkemans RJ, Schuurmans MJ, Numans ME. Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A Biol Sci Med Sci . 2013; 68: 301– 308. doi: 10.1093/gerona/gls161. Google Scholar CrossRef Search ADS PubMed  20. Pitkala KH, Laurila JV, Strandberg TE, Tilvis RS. Prognostic significance of delirium in frail older people. Dement Geriatr Cogn Disord . 2005; 19: 158– 163. doi: 10.1159/000082888. Google Scholar CrossRef Search ADS PubMed  21. Eeles EM, White SV, O’Mahony SM, Bayer AJ, Hubbard RE. The impact of frailty and delirium on mortality in older inpatients. Age Ageing . 2012; 41: 412– 416. doi: 10.1093/ageing/afs021. Google Scholar CrossRef Search ADS PubMed  22. Fong TG, Davis D, Growdon ME, Albuquerque A, Inouye SK. The interface between delirium and dementia in elderly adults. Lancet Neurol . 2015; 14: 823– 832. doi: 10.1016/S1474-4422(15)00101-5. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences Oxford University Press

Loading next page...
 
/lp/ou_press/delirium-frailty-and-mortality-interactions-in-a-prospective-study-of-UGAJdc6R5J
Publisher
Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press on behalf of The Gerontological Society of America.
ISSN
1079-5006
eISSN
1758-535X
D.O.I.
10.1093/gerona/glx214
Publisher site
See Article on Publisher Site

Abstract

Abstract Background It is unknown whether the association between delirium and mortality is consistent for individuals across the whole range of health states. A bimodal relationship has been proposed, where delirium is particularly adverse for those with underlying frailty, but may have a smaller effect (perhaps even protective) if it is an early indicator of acute illness in fitter people. We investigated the impact of delirium on mortality in a cohort simultaneously evaluated for frailty. Methods We undertook an exploratory analysis of a cohort of consecutive acute medical admissions aged ≥70. Delirium on admission was ascertained by psychiatrists. A frailty index (FI) was derived according to a standard approach. Deaths were notified from linked national mortality statistics. Cox regression was used to estimate associations between delirium, frailty, and their interactions on mortality. Results The sample consisted of 710 individuals. Both delirium and frailty were independently associated with increased mortality rates (delirium: HR 2.4, 95% CI 1.8–3.3, p < .01; frailty (per SD): HR 3.5, 95% CI 1.2–9.9, p = .02). Estimating the effect of delirium in tertiles of FI, mortality was greatest in the lowest tertile: tertile 1 HR 3.4 (95% CI 2.1–5.6); tertile 2 HR 2.7 (95% CI 1.5–4.6); tertile 3 HR 1.9 (95% CI 1.2–3.0). Conclusion Although delirium and frailty contribute to mortality, the overall impact of delirium on admission appears to be greater at lower levels of frailty. In contrast to the hypothesis that there is a bimodal distribution for mortality, delirium appears to be particularly adverse when precipitated in fitter individuals. Outcomes, Longevity, Risk factors Delirium, characterized by a fluctuating disturbance in arousal, attention, and cognition secondary to an acute medical condition, is common, affecting 18%–35% of general medical inpatients, 8%–17% of older patients attending emergency departments, and 51% of patients in postacute care (1–3). It is associated with increased length of stay, institutionalization, and progression of dementia (4–7). Delirium is widely understood to be associated with mortality, with an overall HR = 1.9 consistent across a number of studies identified in a systematic review (4). This meta-analysis included observational studies adjusting for chronic comorbidity or acute illness, though none accounted for both. Therefore, an important unanswered question is whether the association remains independent of acute and chronic health factors that might otherwise drive mortality (8). Indeed, a more nuanced understanding of delirium and mortality is relevant given the proposal that the relationship may be bimodal (9). That is, although delirium may have catastrophic outcomes in some patients, for others, it may be an early indicator of acute illness leading to earlier recognition and treatment, perhaps even being protective. Accounting for underlying frailty may provide an insight into the relationship between delirium and mortality because frailty itself is so closely related to risk of both mortality and delirium. One view of frailty describes the gradual accumulation of deficits as individuals’ age, which results in loss of physiological reserve (physical, mental, and functional) and increased vulnerability to insults (10). Taking baseline functional status and chronic comorbidity into account can resolve the issue of “unmeasured heterogeneity”—the factors that increase risk despite the same level of acute illness (11). Our aim was to investigate the effect of delirium and frailty on mortality in a large cohort of acutely unwell adults, setting out to answer the following questions (i): Is delirium associated with mortality, even after adjusting for underlying frailty? (ii) Is there an interaction between delirium and frailty? (iii) Is the relationship with frailty and mortality linear across the range of frailty states and does this change according to the presence of delirium? Methods We undertook an exploratory analysis of a cohort prospectively ascertaining outcomes from acutely hospitalized elders. Participants were recruited as previously described (12). Briefly, all patients aged ≥70 years consecutively admitted to the acute medical unit between June 2007 and December 2007 were screened for inclusion. Exclusion criteria were (i) admission length of under 48 hours and (ii) insufficient English to be assessed for cognitive and mental status. The Royal Free Hospitals NHS Trust Ethics Committee gave ethical approval (06/Q0501/31). Outcomes The study was notified of all deaths through linkage with the UK Office for National Statistics for up to 3 years following the index admission. Exposures Delirium All participants were evaluated on admission by trained psychiatrists. Formal cognitive testing included the Mini-Mental State Examination (13) and delirium was defined using the Confusion Assessment Method algorithm (14). Key symptoms such as inattention were identified through the “serial 7s” or “W-O-R-L-D backwards” tasks. Other items such as acute onset, disorganized thinking and altered level of consciousness, and degree of fluctuation of these symptoms were ascertained through clinical assessment which included information from ward staff and the medical chart. Frailty A 31-item frailty index (FI) was constructed according to a standardized procedure (15), where the following variables were included: comorbidity (13 variables), examination findings (5 variables), laboratory findings (9 variables), and functional status (4 variables) (Table 1). These variables were selected to encompass the full range of acute and chronic health factors that could account for any observed association with mortality. All items were given a binary score (0 = no deficit, 1 = deficit present). For each participant, an FI score was calculated by dividing the number of deficits present by the denominator of 31 maximum deficits, resulting in a score between 0 and 1. For example, for an individual with 10 deficits present out of 31, their FI score would be 10/31 = 0.32. Across several iterations of FI in several hundred datasets, the usual upper limit of frailty observed asymptotically approaches 0.70. In our dataset, data were not missing for more than 6% in each variable. Table 1. Frailty Index Variables   Frequency (%)  Comorbidities   Congestive heart failure  21   Dementia  24   Diabetes  20   COPD  17   Metastatic disease  6   Peripheral vascular disease  7   Severe liver disease  Any prior tumor  12 16   Peptic ulcer disease  9   History of MI  28   History of CVA  27   Psychiatric history  21   Alcohol consumption > 1 unit/wk  27  Examination findings   Heart rate (bpm) <70 or >109  34   Respiratory rate (breaths/min) <12 or >24  15   Temperature (oC) <36 or >38.4  10   Glasgow coma score ≤13  13   MAP (mmHg) <70 or >109  28  Laboratory findings   Packed cell volume (%) <30 or >45.9  18   Potassium (mMol/L) <3.5 or >5.4  13   Creatinine (μmol/L) <60 or >140  37   Sodium (mmol/L) <130 or >149  13   White cells (×109/L) <3 or >14.9  21   Platelets (×109/L) <150 or >400  21   Urea (mmol/L) <2.5 or >7  62   CRP (mg/dL) >5  73   Albumin (g/L) <35 or >55  19  Functional status   Care home resident  28   Urinary and/or faecal incontinence  23   Pressure sores present  9   Polypharmacy (>4 medications)  52    Frequency (%)  Comorbidities   Congestive heart failure  21   Dementia  24   Diabetes  20   COPD  17   Metastatic disease  6   Peripheral vascular disease  7   Severe liver disease  Any prior tumor  12 16   Peptic ulcer disease  9   History of MI  28   History of CVA  27   Psychiatric history  21   Alcohol consumption > 1 unit/wk  27  Examination findings   Heart rate (bpm) <70 or >109  34   Respiratory rate (breaths/min) <12 or >24  15   Temperature (oC) <36 or >38.4  10   Glasgow coma score ≤13  13   MAP (mmHg) <70 or >109  28  Laboratory findings   Packed cell volume (%) <30 or >45.9  18   Potassium (mMol/L) <3.5 or >5.4  13   Creatinine (μmol/L) <60 or >140  37   Sodium (mmol/L) <130 or >149  13   White cells (×109/L) <3 or >14.9  21   Platelets (×109/L) <150 or >400  21   Urea (mmol/L) <2.5 or >7  62   CRP (mg/dL) >5  73   Albumin (g/L) <35 or >55  19  Functional status   Care home resident  28   Urinary and/or faecal incontinence  23   Pressure sores present  9   Polypharmacy (>4 medications)  52  View Large Statistical Analysis Proportional hazards for mortality were assessed in a series of Kaplan–Meier plots and Cox regression models, where outcome was date of death. Postestimation procedures included Schoenfeld residuals for checking assumptions of proportionality. Multiplicative interactions between delirium and FI score were assessed in order to estimate the association of delirium and mortality with respect to underlying frailty, using α = .1 as a threshold for type 1 errors. This approach has been justified by quantifying the gains in power using a less stringent α for samples of this size where interactions between dichotomous (delirium status) and continuous (FI) variables are being considered (16). Linearity of any association with mortality across the distribution of frailty, restricted cubic splines were fitted (four knots), plotting log-hazard ratio for mortality against FI score, stratified by delirium status. Stata version 14.1 was used for all statistical procedures. Results The sample contained 710 individuals, with a mean age of 83.1 years (standard deviation 7.41), and 59% were female (Supplementary Table 1). A diagnosis of dementia was present in 42%. At the end of the 3-year follow-up, 340 individuals remained alive and 370 had died (median follow-up 5 months, IQR 1 to 17 months). The prevalence of delirium on admission was 10.3% (n = 73). The prevalence of deficits on index items ranged from 6% (metastatic disease) to 73% (CRP > 5) (Table 1). The mean FI score was 0.23 (SD 0.096, upper limit 0.55), with a broadly normal distribution (Supplementary Figure 1). Delirium, Frailty, and Mortality Delirium was strongly associated with mortality, with 59 (81%) dying within the next 3 years, compared with 311 (49%) without delirium. Tertiles of FI score (first 0–0.19; second 0.20–0.26; third 0.27–0.55) also showed increasing associations with mortality (first 48%; second 51%; third 60%). Both delirium and frailty were crudely associated with increased hazard for death (delirium: 2.4, 95% CI 1.8–3.2; FI [per SD]; HR 5.9 [95% CI 2.1–16]). This remained the case with a multivariable model including both terms adjusted by age and sex (delirium: HR 2.4, 95% CI 1.8–3.3, p < .01; frailty [per SD]: HR 3.5, 95% CI 1.2–9.9, p = .02; Table 2). Kaplan–Meier survival curves (adjusted by age and sex), according to delirium status, demonstrated worse survival for those with delirium (Figure 1). Table 2. Survival Analysis Showing the Associations Between Delirium, Frailty, and Mortality   Univariable  Multivariable  Hazard ratio  95% CI  p  Hazard ratio  95% CI  p  Delirium  2.44  1.85  3.23  <.01  2.37  1.78  3.15  <.01  Frailty index  5.90  2.14  16.2  <.01  3.37  1.18  9.60  .02    Univariable  Multivariable  Hazard ratio  95% CI  p  Hazard ratio  95% CI  p  Delirium  2.44  1.85  3.23  <.01  2.37  1.78  3.15  <.01  Frailty index  5.90  2.14  16.2  <.01  3.37  1.18  9.60  .02  Note: N = 708, all models adjusted by age and sex. Mean FI score = 0.23 (SD 0.09). View Large Figure 1. View largeDownload slide Kaplan–Meier curves showing survival of cohort, by delirium status, adjusted by age and sex. Figure 1. View largeDownload slide Kaplan–Meier curves showing survival of cohort, by delirium status, adjusted by age and sex. Interactions Between Delirium and Frailty We estimated the effect of delirium on mortality according to each tertile of FI. There was an inverse gradient of association, where stronger associations were observed in the fittest group (tertile 1 HR 3.4 [95% CI 2.1–5.6]; tertile 2 HR 2.7 [95% CI 1.5–4.6]; tertile 3 HR 1.9 [95% CI 1.2–3.0]). The delirium–frailty interaction was statistically significant if α = .1 (p = .07). Linearity of Mortality Gradients in Relation to Delirium Status Restricted cubic splines of the log-HR for mortality against FI score were linear across the four knots, giving no indication that mortality is driven by a particular portion of the frailty continuum (Figure 2). When additionally accounting for delirium status, the splines remained linear, though crossed over to suggest delirium may have greater associations with mortality at lower degrees of frailty and lower associations at higher degrees of frailty (p = .07; Figure 2). Figure 2. View largeDownload slide Relationship between frailty and mortality, by delirium status restricted cubic splines modeling relationship between frailty and mortality. The linearity suggests a continuous relationship between frailty and mortality. When stratified by delirium, the lines intersect suggesting greater effects with delirium at lower levels of mortality (p = .07). Figure 2. View largeDownload slide Relationship between frailty and mortality, by delirium status restricted cubic splines modeling relationship between frailty and mortality. The linearity suggests a continuous relationship between frailty and mortality. When stratified by delirium, the lines intersect suggesting greater effects with delirium at lower levels of mortality (p = .07). Discussion We found that on admission, both delirium and frailty were independently associated with increased risk of death, beyond that expected from all the acute and chronic health factors measured in this study. Moreover, delirium and frailty appeared to interact in such a way that although delirium increases the risk of death at all levels of frailty, the relative impact of this association was greatest in fitter patients. The relationship between frailty and mortality was linear, suggesting that mortality is not being driven by a subset of individuals. Delirium status had a small influence on these relationships. Taken together, our results suggest that delirium itself independently confers a mortality risk, and this risk applies whatever the underlying degree of frailty. Our data illustrate some of the advantages of using a FI in studying cognitive disorders by quantifying a multidimensional assessment, thereby allowing other “non-neurological factors” that nevertheless contribute to patient outcomes (eg nutritional status, polypharmacy, electrolyte abnormalities) to be considered (17). Our findings should be treated with caution. Data were collected from a single site, albeit a large London secondary care hospital with unselected medical admissions from a population of 1.2 million people and high generalizability. Secondly, despite a standardized protocol and training procedure, more than one rater performed the psychiatric evaluation, possibly introducing inter-rater variability. Thirdly, patients were assessed for delirium on admission only (defined as within 72 hours of admission). Any patients developing delirium after this point would not be included in this analysis, possibly underestimating the effects of delirium. Strengths included large sample size and validated assessments undertaken by specialist diagnosticians. Moreover, a wide variety of variables were available to construct a broad FI. The specific inclusion of physiological and laboratory items may generally relate to acute illness, and it is becoming clear that use of such measures is nonetheless informative for use in a FI (18). Although functional items were under-represented, for the requirements of this specific analysis, the combination of acute and chronic health factors made for a particularly robust FI measure. To understand why the relative risk is higher in fit patients compared with frail patients, the insult causing delirium in fit individuals needs to be large, in order to overcome their physiological and cognitive reserve. Another reason for delirium in fitter individuals may be a distinct neurological precipitant which could drive the poorer prognosis in these patients. Both frailty and delirium are associated with vulnerability and lack of physiological and cognitive reserve to insults, though few studies have been able to address this directly (19). One study found that individuals without a prior diagnosis of dementia (a marker of frailty and reserve) had a worse prognosis from delirium than those with a diagnosis, supporting our finding that fitter patients may incur a worse prognosis (20). A separate finding that mortality after delirium appears to arise independently of illness severity or frailty (21). We report a similar distribution of FI scores, though our study was large enough to explore the interactions presented here. The mechanisms by which delirium independently increases risk of death (after adjusting for illness severity) remain unclear. Delirium complicates and impairs recovery—patients with delirium are more likely to receive psychotropic drugs, more likely to fall, and less likely to mobilize effectively during and after their illness, all of which may have an adverse impact on survival (22). In addition, they may be less likely to maintain adequate hydration and malnutrition, and be less compliant with medication. Of particular concern might be hypoactive delirium, where reduced arousal might lead to aspiration pneumonia. Overall, such findings serve to emphasize the emergency nature of delirium. To conclude, in this exploratory analysis of a large cohort of consecutive admissions to an acute adult medical unit, we found that both delirium and frailty independently increase the risk of death. In addition, the risk of death is higher in delirious patients at all levels of frailty. The most striking finding using this approach is that the risk of death from delirium is highest in the fittest patients. This highlights the crucial importance of preventing, detecting, and treating delirium in any patient, and recognizing it as a serious condition with prognostic significance. Supplementary Material Supplementary data are available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding The original study was funded by the Medical Research Council (UK) Special Training Fellowship in Health Services Research to E.S. D.D. is funded through a Wellcome Trust Intermediate Clinical Fellowship (WT107467). Conflict of interest statement None declared. References 1. Siddiqi N, House AO, Holmes JD. Occurrence and outcome of delirium in medical in-patients: a systematic literature review. Age Ageing . 2006; 35: 350– 364. doi: 10.1093/ageing/afl005. Google Scholar CrossRef Search ADS PubMed  2. Kiely DK, Bergmann MA, Jones RN, Murphy KM, Orav EJ, Marcantonio ER. Characteristics associated with delirium persistence among newly admitted post-acute facility patients. J Gerontol A Biol Sci Med Sci . 2004; 59: 344– 349. Google Scholar CrossRef Search ADS PubMed  3. Jackson TA, Gladman JR, Harwood RHet al.   Challenges and opportunities in understanding dementia and delirium in the acute hospital. PLoS Med . 2017; 14: e1002247. doi: 10.1371/journal.pmed.1002247. Google Scholar CrossRef Search ADS PubMed  4. Witlox J, Eurelings LS, de Jonghe JF, Kalisvaart KJ, Eikelenboom P, van Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA . 2010; 304: 443– 451. doi: 10.1001/jama.2010.1013. Google Scholar CrossRef Search ADS PubMed  5. Gross AL, Jones RN, Habtemariam DAet al.   Delirium and long-term cognitive trajectory among persons with dementia. Arch Intern Med . 2012; 172: 1324– 1331. doi: 10.1001/archinternmed.2012.3203. Google Scholar CrossRef Search ADS PubMed  6. Davis DH, Muniz-Terrera G, Keage HAet al.  ; Epidemiological Clinicopathological Studies in Europe (EClipSE) Collaborative Members. Association of delirium with cognitive decline in late life: a neuropathologic study of 3 population-based cohort studies. JAMA Psychiatry . 2017; 74: 244– 251. doi: 10.1001/jamapsychiatry.2016.3423. Google Scholar CrossRef Search ADS PubMed  7. Davis DH, Muniz Terrera G, Keage Het al.   Delirium is a strong risk factor for dementia in the oldest-old: a population-based cohort study. Brain . 2012; 135: 2809– 2816. doi: 10.1093/brain/aws190. Google Scholar CrossRef Search ADS PubMed  8. Bellelli G, Frisoni GB, Turco R, Lucchi E, Magnifico F, Trabucchi M. Delirium superimposed on dementia predicts 12-month survival in elderly patients discharged from a postacute rehabilitation facility. J Gerontol A Biol Sci Med Sci . 2007; 62: 1306– 1309. Google Scholar CrossRef Search ADS PubMed  9. Jackson TA, Wilson D, Richardson S, Lord JM. Predicting outcome in older hospital patients with delirium: a systematic literature review. Int J Geriatr Psychiatry . 2016; 31: 392– 399. doi: 10.1002/gps.4344. Google Scholar CrossRef Search ADS PubMed  10. Kelaiditi E, Andrieu S, Cantet C, Vellas B, Cesari M; ICTUS/DSA Group. Frailty index and incident mortality, hospitalization, and institutionalization in Alzheimer’s disease: data from the ICTUS study. J Gerontol A Biol Sci Med Sci . 2016; 71: 543– 548. doi: 10.1093/gerona/glv137. Google Scholar CrossRef Search ADS PubMed  11. Rockwood K, Blodgett JM, Theou Oet al.   A frailty index based on deficit accumulation quantifies mortality risk in humans and in mice. Sci Rep . 2017; 7: 43068. doi: 10.1038/srep43068. Google Scholar CrossRef Search ADS PubMed  12. Sampson EL, Blanchard MR, Jones L, Tookman A, King M. Dementia in the acute hospital: prospective cohort study of prevalence and mortality. Br J Psychiatry . 2009; 195: 61– 66. doi: 10.1192/bjp.bp.108.055335. Google Scholar CrossRef Search ADS PubMed  13. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res . 1975; 12: 189– 198. Google Scholar CrossRef Search ADS PubMed  14. Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med . 1990; 113: 941– 948. Google Scholar CrossRef Search ADS PubMed  15. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr . 2008; 8: 24. doi: 10.1186/1471-2318-8-24. Google Scholar CrossRef Search ADS PubMed  16. Durand CP. Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study. PLoS One . 2013; 8: e71079. doi: 10.1371/journal.pone.0071079. Google Scholar CrossRef Search ADS PubMed  17. Canevelli M, Cesari M, Remiddi Fet al.   Promoting the assessment of frailty in the clinical approach to cognitive disorders. Front Aging Neurosci . 2017; 9: 36. doi: 10.3389/fnagi.2017.00036. Google Scholar CrossRef Search ADS PubMed  18. Blodgett JM, Theou O, Howlett SE, Wu FC, Rockwood K. A frailty index based on laboratory deficits in community-dwelling men predicted their risk of adverse health outcomes. Age Ageing . 2016; 45: 463– 468. doi: 10.1093/ageing/afw054. Google Scholar CrossRef Search ADS PubMed  19. Drubbel I, de Wit NJ, Bleijenberg N, Eijkemans RJ, Schuurmans MJ, Numans ME. Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A Biol Sci Med Sci . 2013; 68: 301– 308. doi: 10.1093/gerona/gls161. Google Scholar CrossRef Search ADS PubMed  20. Pitkala KH, Laurila JV, Strandberg TE, Tilvis RS. Prognostic significance of delirium in frail older people. Dement Geriatr Cogn Disord . 2005; 19: 158– 163. doi: 10.1159/000082888. Google Scholar CrossRef Search ADS PubMed  21. Eeles EM, White SV, O’Mahony SM, Bayer AJ, Hubbard RE. The impact of frailty and delirium on mortality in older inpatients. Age Ageing . 2012; 41: 412– 416. doi: 10.1093/ageing/afs021. Google Scholar CrossRef Search ADS PubMed  22. Fong TG, Davis D, Growdon ME, Albuquerque A, Inouye SK. The interface between delirium and dementia in elderly adults. Lancet Neurol . 2015; 14: 823– 832. doi: 10.1016/S1474-4422(15)00101-5. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Journal

The Journals of Gerontology Series A: Biomedical Sciences and Medical SciencesOxford 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