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Predictors of Functional Decline in Hospitalized Elderly Patients

A Systematic Review

^a Department of Clinical Epidemiology and Community Studies, St. Mary's Hospital, and Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada

Jane McCusker, Department of Clinical Epidemiology and Community Studies, St. Mary's Hospital Center, 3830 Lacombe Avenue, Room 2508, Montreal, Quebec H3T 1M5, Canada E-mail: jane.mccusker{at}mcgill.ca.

	Abstract

Top Abstract Methods Results Discussion Appendix ENDIX References

 
Background. This article will systematically review the methodological characteristics and results of studies of variables and indices that predict functional decline in older hospitalized patients.

Methods. We restricted this review to original longitudinal studies of predictors of either physical functional decline or nursing home admission among patients aged 60 and older. Two reviewers independently abstracted information on methodological characteristics and substantive results.

Results. Thirty articles were identified, derived from 27 different studies, reporting on 33 substudies. Substantial variability was found with respect to study design, outcomes measured, period of follow-up, predictors investigated, and analytic methods. Multivariable predictive indices were significantly associated with adverse outcomes in the majority of studies that investigated them, as were the following variables: age, diagnosis, activities of daily living, cognitive impairment (including delirium), and residence.

Conclusions. The methodological heterogeneity of the studies identified limits quantitative synthesis of the results. Predictive indices for hospitalized elders appear to have moderate short-term predictive ability.

DISABILITY refers to limitation of function (usually of activities of daily living [ADLs]) or restriction of activities ⁽¹⁾. Although disability tends to increase with advancing age, different change trajectories can be identified, including functional decline and recovery ^(2)(3)(4). For clinicians, the identification of predictors of functional decline can help with the targeting of interventions to high-risk groups, including secondary prevention (to reduce the incidence of disability among patients with impairments) and tertiary prevention (to reduce the incidence of handicap among patients with disabilities) ⁽⁵⁾. Examples of screening tools based on these predictors have been reported both for hospital inpatients and for elders in the community ^(6)(7)(8)(9). Because both disability and functional decline are associated with increased risk of mortality, institutionalization, and service utilization, and with higher societal costs ^(2)(10), the predictors of functional decline are of interest not only to clinicians, but also to patients and their family members, health services administrators, and health policy makers.

Most prior research has investigated functional decline and its predictors in community-dwelling elders ⁽¹¹⁾. A recent synthesis of 78 studies of community-dwelling elders reported a wide range of predictors of functional decline including cognition, affect, comorbidity, health behaviors, and specific impairments ⁽¹¹⁾. Hospitalized elders, who have been less well studied, appear to be at increased risk of functional decline, both during hospitalization and following discharge ⁽¹²⁾.

We undertook this systematic review to examine the methodological characteristics and results of studies of the predictors of functional decline in older hospitalized patients. We included studies that measured functional decline directly, by a reduced ability to carry out activities of daily living, or indirectly, by the need to transfer the patient to a nursing home or similar institution providing increased assistance with activities of daily living. In planning the review, we considered three types of study: the first type investigates a primary predictive variable, such as delirium, with all other predictive variables being considered as potential confounders; the second type investigates several potential predictors and then uses multivariable analyses to identify those that are independent predictors of functional decline; and the third type proceeds further to the development of predictive models and/or indices.

	Methods

Top Abstract Methods Results Discussion Appendix ENDIX References

 
Search Strategy
The search strategy for relevant studies focused on published studies identified through computerized databases and hand searches of the bibliographies of relevant studies and review articles. The Medline database was searched from 1976 to 1998 to identify studies that were conducted on elderly patients, used a longitudinal design, and investigated one or more predictors of functional decline. The search strategy consisted of four sets of terms that were subsequently combined using the Boolean term "and." These sets were elderly (i.e., "aged," "elderly"), performance characteristics (i.e., "sensitivity," "specificity"), outcomes of interest (i.e., "mortality," "hospitalization"), and study design (i.e., "longitudinal studies," etc.). The complete search strategy is shown in the Appendix.

Screening of Articles
The abstract of each article identified through the search was screened, and a set of exclusion criteria was applied hierarchically. Articles were excluded if they did not report data from an original study; they were restricted to a particular medical condition or procedure; the study population included patients younger than the age of 60 (unless the results for those aged 60 and older were presented separately); patients were not followed longitudinally; no predictive variable was investigated; a particular treatment or other intervention was the only predictor studied; the study outcomes did not include measures of either physical functional decline or nursing home admission; the study was conducted in a setting other than an acute-care hospital; or the study was written in a language other than English or French.

Data Extraction and Analysis
All the articles determined to be eligible were reviewed independently by two reviewers; the reviewers then met to discuss their findings with the first author (Jane McCusker), and a consensus review was completed. The data extracted included study setting (country, number and type of hospitals, admitting service); sample size at baseline and follow-up; characteristics of the study population (age, gender, residence, and level of disability at baseline); length of follow-up; definition and time of measurement of outcomes and predictors; statistical methods; and results (associations between predictors and outcomes, and performance characteristics of predictor variables or indices, if reported).

Simple descriptive techniques, including contingency table analyses, were used to analyze the data.

	Results

Top Abstract Methods Results Discussion Appendix ENDIX References

 
Search for Articles
The Medline search yielded 1840 citations, of which 20 met eligibility criteria for this review. Excluded were 1056 articles that were restricted to particular conditions (e.g., a particular diagnosis, a surgical series, or patients admitted to an intensive care unit) and 307 studies that did not include outcome measures of either physical functional decline or nursing home admission. Other reasons for exclusion are shown in Fig. 1. Hand searching of the eligible articles and personal files yielded 10 additional articles, for a total of 30 articles ^{(6)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)}. These articles were based on 27 different studies; three studies each had two publications, dealing with different predictors and/or outcomes ^(15)(16), different stages of follow-up ^(17)(18), or separate reports on development and validation of a predictive index ^(31)(32). Three other articles presented both development and validation components of a predictive index ^(6)(21)(35). Thus, for consistency, we considered development and validation components as separate substudies, giving a total of 33 substudies. Analyses were conducted either at the study (n = 27) or substudy (n = 33) level.

View larger version (18K): [in this window] [in a new window]   Figure 1. Screening of articles.

Other study characteristics are not shown in Table 1 . All studies used an observational cohort design, except for one case-control study that compared 50 patients discharged to a nursing home with 50 patients discharged home ⁽³⁸⁾. Predictors were examined within 48 hours of admission only in 18 (55%) of the substudies; 14 (42%) included one or more predictors assessed later during the hospitalization (this information was not reported in one substudy). The most common exclusion criterion was nursing home residence at admission (42% of substudies); 18% of substudies excluded patients with cognitive impairment at time zero and those with a very short length of stay. Methods of analysis are shown in Table 2 .

Several other significant predictor variables are not presented in Table 3 because they were examined in fewer than five studies. Other predictors of nursing home admission included attitudinal factors (patient and/or caregiver wish for patient not to return home [31,32,40], or expectation of referring physician that patient would be discharged to a nursing home [35]); informal support factors (primary caregiver was patient's child [26], lack of relative at home to assist [37,38], not having children [24], receipt of professional services at home [31,32,38]); and miscellaneous medical and other factors (greater number of medications [29,37], prolonged bed rest and visual impairment [29], previous admissions [37], admission to surgical service [24], longer hospital stay [40], and geographic area of residence [28]). Other predictors of functional decline included depression ^(15)(21); lack of social activity and support ⁽²¹⁾; longer hospital stay ⁽⁶⁾; and other measures of physical function ⁽³⁰⁾. Other predictors of composite outcomes included functional decline at admission, defined as a change in the ability to perform personal and/or instrumental ADLs before admission ^(22)(41).

Performance Characteristics
Table 4 shows the performance characteristics of multivariable predictive indices and models. The predictive indices included five developed from multivariate analyses, either logistic regression ^{(6)(21)(32)(41)} or discriminant function analysis ⁽³⁵⁾; one index based on clinical targeting criteria ⁽²³⁾; and one computed from a set of ad hoc algorithms ⁽²⁶⁾. Two studies reported the area under the curve (AUC); these were similar and indicated moderate predictive performance (AUC 0.65 and 0.66, respectively) ^(6)(41). Four studies ^{(26)(32)(35)(41)} reported sensitivity and specificity, and two studies that did not explicitly report sensitivity and specificity provided sufficient data for computation of these measures ^(21)(23). In general, these results indicated moderate performance. The best performance characteristics were for the hand-developed algorithm ⁽²⁶⁾, with both sensitivity and specificity of more than 80%.

	Discussion

Top Abstract Methods Results Discussion Appendix ENDIX References

Conceptual and Methodological Issues
The studies reviewed here were heterogeneous in their goals, some focusing on a primary predictor, others on multiple predictors, and some of the latter proceeding to the development of a predictive model or index.

The conceptualization and measurement of functional decline was diverse. Most investigators have focused on nursing home admission, an indirect but easily measured outcome. However, the decision to admit to a nursing home involves cultural and social issues, in addition to functional decline itself ⁽¹⁰⁾. Thus, the predictors of nursing home admission more often included social and psychological variables such as living alone and residential preferences of the patient and/or family.

The use of a composite outcome measure, combining functional decline, death, nursing home admission, and/or other outcomes into a single, dichotomous outcome variable, is appealing, first because these outcomes may share some (but not all) predictors (as shown, for example, in Table 3 ). Second, in populations with a nonnegligible death rate, the exclusion of deaths poses problems in interpretation ⁽⁴²⁾. Third, multiple tests of statistical significance are avoided when a single outcome variable is used. However, there are also some arguments against using composite outcome variables. First, differences in associations between a given predictor and different outcomes will not be identified. For example, men tend to have higher death rates than women, whereas women tend to have higher rates of functional decline than men ⁽³⁾. Thus, use of a composite outcome combining death and functional decline might result in finding no predictive effect of gender. A related problem is that differences in the effects of predictors on different outcomes may induce spurious differences in the results of various studies using the same composite outcome, if the proportions of specific outcomes vary across the studies. Thus, the decision of whether to use a composite outcome variable will depend upon the goals and context of the study.

There was also heterogeneity in both the "time zero" and follow-up period used for measuring functional decline; some studies measured decline from the premorbid status while others measured decline from admission (or shortly after admission). The former studies conceptualize functional decline more broadly, as related to the episode of illness or injury that led to the hospital admission, while the latter studies focus upon functional decline that may result more specifically from processes and events during the hospitalization. The distinction is important because functional decline before admission was a strong predictor of outcome in the two studies that assessed it ^(26)(41). The time frame for follow-up varied, with many studies having no postdischarge follow-up. Because of trends toward shorter hospital stays and differences in the average length of stay in different health care systems, assessment of functional decline over a standard time period from admission rather than at the time of discharge would facilitate comparisons between studies.

The specific predictors examined in these studies were diverse. At the study level (n = 27), the most frequently examined predictors included age and diagnosis, while important predictors such as baseline disability and cognitive status were reported only in a minority of studies. Studies also varied in whether they included only baseline (admission) predictors or postbaseline predictors, such as length of stay or level of disability at discharge.

Substantive Findings
The considerable methodological differences between studies limited our ability to draw substantive conclusions. First, we were able to employ only a very crude measure to compare study results, that is, the statistical significance of specific effects (or confidence interval that excluded the null value) reported either from univariate or multivariate analyses. Second, most predictors (except for age and diagnosis) were examined and reported only in a minority of studies. Nevertheless, certain tentative conclusions can be drawn from our results. First, predictive indices (including clinically and empirically developed indices) were more consistent than individual predictor variables in their ability to predict adverse outcomes. Second, there appear to be similarities and differences between the predictors of nursing home admission and functional decline. Common predictors for these two outcomes (found in the majority of studies that examine them) include age, institutional residence, IADLs, delirium, and other cognitive impairment. On the other hand, living alone and patient/family preferences were more useful in the prediction of nursing home admission than of functional decline. Third, gender, marital status, and race were only significant in a minority of studies and appear to be less useful as predictors. Fourth, several other variables that have been examined in fewer than five studies but found to be significant may be worth assessing in future research. These include social activities and support, functional decline at admission, depression, visual impairment, and number of medications. Indeed, three of these variables (functional decline at admission, visual impairment, and polymedication) were among the strongest predictors of functional decline in one study and were subsequently incorporated into the six-item Identification of Seniors At Risk (ISAR) predictive index ⁽⁴¹⁾.

These results may be useful for clinicians looking for a screening tool to identify hospitalized elders who are at greatest risk of adverse outcomes, to help in the targeting of geriatric evaluation and management interventions. In general, the performance characteristics of predictive indices reported (or computed from reported data) indicate moderate predictive ability, with an area under the receiver operating characteristic curve of approximately 0.66. An exception was an ad hoc algorithm ⁽²⁶⁾, which had values for both sensitivity and specificity of more than 80%. This algorithm was developed from data in a single study and needs to be validated independently. Apart from this algorithm, the various indices presented in Table 4 appear to perform similarly, despite differences in the variables that compose each index. Thus, easily measured indices, such as those derived from information readily available or from simple self-reported questions, may be preferable. It should be noted that some of these indices were studied in samples that excluded deaths, so that the predictive ability is conditional on survival and may be difficult to interpret. The results in Table 4 also indicate that the choice of a cut-point should depend on the goals of screening and the resources available. For example, the use of a cut-point of 3 versus 2 for the ISAR screen reduces the sensitivity but also reduces the proportion of patients with a positive result ⁽⁴¹⁾. Thus, a hospital with fewer resources may wish to adopt the higher cut-point to increase the specificity of the screen and, hence, the predictive value of a positive result. Finally, a thorough evaluation of the benefits and costs of screening requires information on the relative effectiveness of interventions among patient populations at different levels of risk of adverse outcomes.

Suggestions for Future Research
Based on the findings of this review, we would like to make suggestions for future research on the predictors of functional decline in hospitalized elders in order to facilitate comparison of their results. Regarding outcomes, the use of nursing home admission as the sole outcome should be avoided; at least one (and preferably several) postdischarge measure of functional status should be made. It is recommended that composite outcome variables be used that incorporate death, possibly using methods described by Diehr and colleagues ⁽⁴²⁾. Regarding predictors, studies should include at least a basic set of variables that appear to have predictive value in most studies: age, residence, physical function (basic ADL and IADL), and cognitive status (preferably the MMSE, the most commonly used measure). These should be included in the multivariate analyses to ensure appropriate adjustments in studies that investigate additional predictive effects of other variables of interest. It would also be useful if the coding of these predictor variables were standardized (e.g., by using standard cut-points for continuous predictors and standard grouping of categorical predictors). Regarding analytic methods, the results of both univariate and multivariable analyses should be reported, and both affect measures (odds ratios, etc.) and confidence intervals presented. Validation of predictive indices and estimation of their performance characteristics should be done on independent samples. Finally, the reporting on methods, including statistical analyses, should be detailed enough to allow other researchers to replicate these methods.

In addition to these suggestions, synthesis and comparison of study results would be facilitated by the standardization of various methodological aspects of studies (e.g., measures of functional decline, the main inclusion/exclusion criteria, and duration of follow-up and timing of assessments).

REVIEW ARTICLE

	Acknowledgments

 
This work was supported by a grant from the Fonds de la Recherche en Santé du Québec, Programme 10. Dr. Abrahamowicz is a Scientist of the Canadian Institutes for Health Research. The results were reported, in part, at the Canadian Cochrane Symposium, Hamilton, ON, November 19, 1999.

Received February 7, 2002

Accepted March 19, 2002

	Appendix ENDIX

Top Abstract Methods Results Discussion Appendix ENDIX References

 
Medline Search Strategy
001 exp aged/

002 exp aging/

003 age:.tw,sh,ab.

004 aging:.tw,sh,ab.

005 elder:.tw,sh,ab.

006 or/1-5

007 roc curve:.tw,sh,ab.

008 sensitiv:.tw,sh,ab.

009 specific:.tw,sh,ab.

010 exp prognosis/

011 predict:.tw,sh,ab.

012 or/7-11

013 exp activities of daily living/

014 exp quality of life/

015 exp hospitalization/

016 exp institutionalization/

017 exp mortality/

018 institutionali:.tw,sh,ab.

019 quality of life.tw.

020 activities of daily living.tw.

021 mortality.tw.

022 death.tw.

023 exp accidental falls/

024 functional decline.tw.

025 or/13–24

026 exp longitudinal studies/

027 exp cohort studies/

028 exp follow-up studies/

029 longitudinal:.tw.

030 prospective:.tw.

031 follow up.tw.

032 or/26–31

033 and/6,12,25,32

034 limit 33 to "aged <65 and over>"

035 exp intervention studies/

036 exp child/

037 exp middle age/

038 or/35–37

039 34 not 38

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Top Abstract Methods Results Discussion Appendix ENDIX References

 

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