Assessment of Comorbidity Burden and its Association With Functional Rehabilitation Outcome After Stroke or Hip Fracture: A Systematic Review and Meta-Analysis

doi:10.1016/j.jamda.2016.07.028

Journal of the American Medical Directors Association

Volume 17, Issue 11, 1 November 2016, Pages 1066.e13-1066.e21

https://doi.org/10.1016/j.jamda.2016.07.028 Get rights and content

Abstract

Background

A well-grounded functional prognosis during triage for rehabilitation is important, especially in older patients who experience the burden of comorbidity. However, it remains unclear what impact comorbidity has on functional outcome after rehabilitation.

Aim

To investigate the associations between comorbidity indexes and functional outcome after inpatient stroke or hip fracture rehabilitation. Furthermore, to identify which method of comorbidity assessment best reveals this relationship.

Design

Systematic review and meta-analysis.

Methods

An extensive search in PubMed, EMBASE, COCHRANE, Web of Science, and CINAHL of cited references and gray literature was carried out on March 4, 2016. This meta-analysis was conducted in agreement with the guidelines for Preferred Reporting Items for Systematic reviews and Meta-Analyses. Studies were included if participants were adult patients with a stroke or hip fracture, participants received inpatient rehabilitation, comorbidity was assessed with a valid index, and functional status was an outcome measure. Two reviewers independently extracted data; according to the predefined data extraction plan, included studies were independently evaluated on risk of bias.

Results

Twenty studies were eligible for review, and 7 studies were included in the meta-analysis. The pooled correlation between comorbidity and functional status at discharge was −0.43 [−0.69; −0.06]. Presence and strength of correlations differed between comorbidity indexes. Charlson index: range = 0.0 to −0.88 and 0%–1% explained variance (%var). Cumulative illness rating scale (CIRS) total or cumulative: range = −0.02 to −0.34 and unknown %var. CIRS-severity index: range = −0.25 to −0.40 and 12–16 %var. Comorbidity-severity index: range = −0.39 and −0.47 and 5 %var. Liu index: range = −0.28 to −0.50 and 4–7 %var. When the index contained a severity weighting, the associations were more evident.

Conclusions

An association between comorbidity burden and functional outcome exists, albeit modest. Assessment of severity weighted comorbidity is preferred for estimating the functional prognosis after stroke and hip fracture rehabilitation.

Section snippets

Search Strategy

This meta-analysis was conducted following A Measurement Tool to Assess Systematic Reviews and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) (Appendices A and B).15, 16, 17 A systematic search of publications was carried out in the following electronic databases: PubMed (Medline), Embase, The Cochrane Library (CENTRAL), Web of Science, and CINAHL from the earliest record to March 4, 2016. The search strategy was designed under the supervision of an experienced

Study Selection

The database search identified 2910 articles, and 1514 articles were identified by using other sources. After removing the duplicates, 2551 articles were screened for eligibility of which 20 met all criteria. Reasons for exclusion are reported in the PRISMA flowchart (Appendix E).

Studies that assessed comorbidity using the Tier ranking system were excluded after discussion with 2 other reviewers (W.A. and R.vB.).20, 21 This system was developed by the Centers for Medicare and Medicaid Services

Main Findings

This review supports the hypothesis that preexisting comorbidity is negatively associated with functional rehabilitation outcome. This relation becomes more evident when comorbidity is assessed by indicating the severity of present comorbidities.

In the studied patient populations, we detected 4 comorbidity indexes: the CharlsonCI, the LiuCI, the COM-SI, and the CIRS(G) scored as total, cumulative, or severity index. The LiuCI and COM-SI were specifically designed for use in a rehabilitation

Conclusions

There seems to be insufficient evidence that assessing comorbidity helps predicting the functional prognosis if current comorbidity indexes are used. This review adds new insights in emphasizing the severity of comorbidity to assist in estimating their functional prognosis after acute illnesses such as stroke or hip fracture. More research is needed to investigate whether a brief and practical index that captures individual impact of comorbidity, is feasible, reliable, and valid for use in

Acknowledgments

The authors thank J.W. Schoones, medical information specialist from the Leiden University Medical Center (LUMC) for his supervision regarding the design of the search strategy. The corresponding author (AK) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

References (49)

C.L. McVeigh et al.
Selecting Patients for Rehabilitation
J Am Med Dir Assoc
(2015)
E.J. Roth et al.
Stroke rehabilitation. 2. Comorbidities and complications
Arch Phys Med Rehabil
(1994)
M.G. Stineman et al.
A functional diagnostic complexity index for rehabilitation medicine: Measuring the influence of many diagnoses on functional independence and resource use
ArchP hys Med Rehabil
(2000)
J. Gindin et al.
Predictors of rehabilitation outcomes: A comparison of Israeli and Italian geriatric post-acute care (PAC) facilities using the minimum data set (MDS)
J Am Med Dir Assoc
(2007)
G. Bellelli et al.
Outcomes at 12 months in a population of elderly patients discharged from a Rehabilitation Unit
J Am Med Dir Assoc
(2008)
D. Fusco et al.
Predictors of rehabilitation outcome among frail elderly patients living in the community
J Am Med Dir Assoc
(2009)
V. de Groot et al.
How to measure comorbidity: A critical review of available methods
J Clin Epidemiol
(2003)
M. Extermann
Measurement and impact of comorbidity in older cancer patients
Crit Rev Oncol Hemato
(2000)
R. DerSimonian et al.
Meta-analysis in clinical trials
Control Clin Trials
(1986)
T. Reistetter et al.
Regional variation in stroke rehabilitation outcomes
Arch Phys Med Rehabil
(2014)

D.R. Berlowitz et al.

Impact of comorbidities on stroke rehabilitation outcomes: Does the method matter?

Arch Phys Med Rehabil

(2008)

M. Liu et al.

Comorbidity measures for stroke outcome research: A preliminary study

Arch Phys Med Rehabil

(1997)

M.C. Munin et al.

Effect of rehabilitation site on functional recovery after hip fracture

Arch Phys Med Rehabil

(2005)

R.A. Deyo et al.

Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

J Clin Epidemiol

(1992)

Y. Press et al.

The effect of co-morbidity on the rehabilitation process in elderly patients after hip fracture

Arch Gerontol Geriatr

(2007)

D.M. Reker et al.

Stroke rehabilitation outcome variation in Veterans Affairs rehabilitation units: Accounting for case-mix

Arch Phys Med Rehabil

(1998)

A. Tessier et al.

Validation of the Charlson Comorbidity Index for predicting functional outcome of stroke

Arch Phys Med Rehabil

(2008)

D.L. Groll et al.

The development of a comorbidity index with physical function as the outcome

J Clin Epidemiol

(2005)

R. Meijer et al.

Prognostic factors for ambulation and activities of daily living in the subacute phase after stroke. A systematic review of the literature

Clin Rehabil

(2003)

G. Ferriero et al.

The influence of comorbidities and complications on discharge function in stroke rehabilitation inpatients

EuraMedicophys

(2006)

J.M. Veerbeek et al.

Early prediction of outcome of activities of daily living after stroke: A systematic review

Stroke

(2011)

S. van Almenkerk et al.

What predicts a poor outcome in older stroke survivors? A systematic review of the literature

Disabil Rehabil

(2013)

M. Fortin et al.

Comparative assessment of three different indices of multimorbidity for studies on health-related quality of life

Health Qual Life Outcomes

(2005)

A.L. Huntley et al.

Measures of multimorbidity and morbidity burden for use in primary care and community settings: A systematic review and guide

Ann Fam Med

(2012)

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Given the significant non-neurologic sequelae of ABI, effective models of post-acute care for ABI need to address the individual's broader health needs to maximize outcomes [8]. Our findings add to a growing body of research highlighting the negative association between comorbidity and post-acute recovery in ABI [10–14]. LOS was on average 6.1 months longer for participants with comorbidity than those without; however, functional gains at discharge were significantly lower, despite the longer LOS.
Current understanding of comorbidities associated with acquired brain injury (ABI) and the effects on post-acute ABI outcomes is poor.
To describe the prevalence, severity and patterns of comorbidity for the pre-injury, acute and post-acute phases, and to examine the effects of post-acute comorbidities on functional independence and length of stay (LOS) at discharge from post-acute care.
Retrospective whole-population cohort study of n = 1,011 individuals with traumatic (TBI) or non-traumatic brain injury (NTBI), or eligible neurologic conditions admitted to a post-acute neurorehabilitation and disability support service in Western Australia (WA) between 1991 and 2020. Comorbidities were ascertained from internal electronic medical records and linked hospital and emergency department data from the WA Data Linkage System. We measured comorbidities across 14 body systems using the Cumulative Illness Rating Scale (CIRS) and Elixhauser Comorbidity Index (ECI), and functional independence with the UK Functional Independence Measure and Functional Assessment Measure (UK FIM+FAM). We used multilevel mixed-effect regression models to determine the effects of comorbidity on post-acute outcomes.
NTBI was the most common diagnosis (54%), followed by TBI (34%) and neurologic conditions (10%). Pre-injury comorbidities were present in over half the cohort. Comorbidity prevalence increased significantly from 57% to 84% (∆+27%) and severity (mean ECI score) increased significantly from 2.1 to 13.8 (∆+11.7) between pre-injury and the acute phase and remained elevated at admission to post-acute services (82%, mean ECI score 7.3). Psychiatric comorbidity was the most prevalent (56%) and was associated with significantly poorer functional outcomes at discharge and an increase in LOS of 6.5 months. Genitourinary, musculoskeletal, eye, ear nose and throat, and renal comorbidities also had significant effects on post-acute outcomes.
ABI has a long-term impact on multiple body systems. Identification and management of comorbidities is critical to maximize functional outcomes and reduce the cost of post-acute care.
Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics
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Although the CDCI consists of fewer comorbidities than the ECI and FCI do, the combination of the comorbidity items in the CDCI is weighted based on their severity to account for the disease burden, which contrasts with the simple summation of the comorbidity items in the ECI and FCI. Several studies have reported the superior prognostic performance of weighted measures of multimorbidity compared with simple measures [39,60-62]. We contribute to this literature by highlighting the importance of accounting for the severity of the diseases for better prognostications with multimorbidity indices, but we are the first to highlight such importance for improving the predictability of multimorbidity indices.
Patient complexity among older delayed-discharge patients complicates discharge planning, resulting in a higher rate of adverse outcomes, such as readmission and mortality. Early prediction of multimorbidity, as a common indicator of patient complexity, can support proactive discharge planning by prioritizing complex patients and reducing healthcare inefficiencies.
We set out to accomplish the following two objectives: 1) to examine the predictability of three common multimorbidity indices, including Charlson–Deyo Comorbidity Index (CDCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) using machine learning (ML), and 2) to assess the prognostic power of these indices in predicting 30-day readmission and mortality.
We used data including 163,983 observations of patients aged 65 and older who experienced discharge delay in Ontario, Canada, during 2004 – 2017. First, we utilized various classification ML algorithms, including classification and regression trees, random forests, bagging trees, extreme gradient boosting, and logistic regression, to predict the multimorbidity status based on CDCI, ECI, and FCI. Second, we used adjusted multinomial logistic regression to assess the association between multimorbidity indices and the patient-important outcomes, including 30-day mortality and readmission.
For all ML algorithms and regardless of the predictive performance criteria, better predictions were established for the CDCI compared with the ECI and FCI. Remarkably, the most predictable multimorbidity index (i.e., CDCI with Area Under the Receiver Operating Characteristic Curve = 0.80, 95% CI = 0.79 – 0.81) also offered the highest prognostications regarding adverse events (RRR_mortality = 3.44, 95% CI = 3.21 – 3.68 and RRR_readmission = 1.36, 95% CI = 1.31 – 1.40).
Our findings highlight the feasibility and utility of predicting multimorbidity status using ML algorithms, resulting in the early detection of patients at risk of mortality and readmission. This can support proactive triage and decision-making about staffing and resource allocation, with the goal of optimizing patient outcomes and facilitating an upstream and informed discharge process through prioritizing complex patients for discharge and providing patient-centered care.
Rehabilitation prioritization: Development of expert consensus on essential rehabilitation during pandemics
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Relationship between pre-stroke frailty status and short-term functional outcome in older patients with acute stroke–A mediation analysis-
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Thus, clinicians have to predict their functional outcomes not only in the long term (3 months or more), but also in the short term such as at discharge from an acute hospital. The predictors of functional outcome in patients with stroke are disease severity (Sato et al., 2008), premorbid disability (Han et al., 2020), presence of comorbidities (Kabboord, van Eijk, Fiocco, van Balen & Achterberg, 2016), and others (Stinear, Smith & Byblow, 2019). The presence of geriatric syndrome, such as sarcopenia (Nozoe et al., 2019), was also reported to be related to a poor functional outcome in patients with stroke.
The aim of this study was to investigate the effects of pre-stroke frailty status on short-term functional outcome in older patients with acute stroke.
In this prospective longitudinal study, we assessed the pre-stroke frailty status (robust, prefrail, or frail) by the Frailty Screening Index, disease severity by the National Institutes of Stroke Scale (NIHSS), and short-term functional outcome by the modified Rankin Scale (mRS) at discharge from acute hospital in patients with older stroke. We considered poor functional outcome to be a mRS >2. Logistic regression analysis and mediation analysis were used to investigate the relationships among pre-stroke frailty status, disease severity, length of stay (LOS), and short-term functional outcome.
A total of 232 patients were enrolled in this study. The NIHSS and LOS were significantly different between groups (p<0.001, p = 0.01, respectively), but there was no relationship between frailty status and short-term functional outcome (p = 0.22). Based on the logistic regression analyses after adjusting for potential confounders, the NIHSS (odds ratio (OR): 1.75, 95% confidence interval (CI): 1.44–2.14, p<0.001) and LOS (OR: 1.07, 95%CI: 1.03–1.11) were independently associated with a poor functional outcome. In the mediation analysis, the NIHSS (β=0.137, p<0.001) and LOS (β=0.09, p<0.004) were significant mediators between pre-stroke frailty status and poor functional outcome.
The relationship between pre-stroke frailty status and short-term functional outcome was mediated by disease severity and LOS in older patients with acute stroke.
Prevalence, Patterns, and Predictors of Multimorbidity in Adults With Acquired Brain Injury at Admission to Staged Community-Based Rehabilitation
2020, Archives of Rehabilitation Research and Clinical Translation
To describe the prevalence, patterns, and predictors of multimorbidity in adults with an acquired brain injury (ABI) on presentation to a community-based neurorehabilitation service.
Retrospective cohort study using routinely collected admissions and clinical data.
Community-based neurorehabilitation.
Individuals (N=263) with non-traumatic brain injury (NTBI; n=187 [71.1%]) versus traumatic brain injury (TBI; n=76 [28.9%]).
Not applicable.
Comorbidity was defined as the co-occurrence of at least one chronic condition in conjunction with a primary diagnosis of ABI. Multimorbidity was defined as the co-occurrence of 2 or more chronic conditions across 2 or more body systems, in conjunction with a primary diagnosis of ABI.
Comorbidity was present in 72.2% of participants overall, whereas multimorbidity was present in 35.4% of the cohort. The prevalence of comorbidity (76% vs 63%; P=.036) and multimorbidity (40% vs 24%; P=.012) was higher in NTBI compared with participants with TBI. Participants with NTBI had a higher prevalence of physical health multimorbidities, including cardiovascular (44% vs 6%; P<.001) and endocrine (34% vs 10%; P=.002) disease, whereas participants with TBI had a higher prevalence of mental health conditions (79% vs 48%; P<.001). Depression (36.3%) and hypertension (25.8%) were the most common diagnoses. Increasing age was the only significant predictor of multimorbidity.
Most participants experienced multimorbidity. Effective management of multimorbidity should be included as part of individual rehabilitation for ABI and planning of resource allocation and service delivery. The results of this study can help guide the provision of treatment and services for individuals with ABI in community-based rehabilitation. Our study highlights access to mental health, cardiovascular, endocrine, and neurology services as essential components of rehabilitation for ABI.
Non-paretic lower limb muscle wasting during acute phase is associated with dependent ambulation in patients with stroke
2020, Journal of Clinical Neuroscience
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The recovery of independent ambulation is a major rehabilitation target after stroke [2]. Several factors have been reported to predict independent ambulation in stroke patients [3]: age [3], sex [4], disease severity [5] (e.g., National Institutes of Health stroke scale score, NIHSS), and the presence of comorbidities [6]. Predicting functional outcomes correctly is important for improving these patients’ function and ensuring satisfactory rehabilitation.
Muscle wasting in patients with stroke is a factor for a poor functional outcome. However, there have been few studies on the relationships between lower limb muscle wasting during the acute phase and dependent ambulation. This study examined whether lower limb muscle wasting during the acute phase is associated with the dependent ambulation status at 3 months after stroke.
In this observational cohort study, we measured the quadriceps muscle thickness in the paretic and non-paretic limbs within 1 and 3 weeks after admission using ultrasonography in non-ambulatory patients with acute stroke. Patients were categorized into two groups based on their ambulation ability: dependent, functional ambulation category (FAC) 0–3 and independent, FAC 4–5) at 3 months after stroke.
In total, 55 patients were included and completed follow-up, and 28 patients (51%) had an ambulation status of dependent at 3 months after stroke. Significant differences in muscle wasting were observed in the non-paretic limb (−5.5% [8.7%] : −16.8% [13.6%], independent group: dependent group, respectively, P < 0.001), but not in the paretic limb (−15.0% [14.3%] : −18.7% [15.1%], P = 0.36). Even after adjusting for covariates, muscle wasting in the non-paretic limb was a significant independent predictor of dependent ambulation (adjusted odds ratio, 0.87; 95% confidence interval, 0.76–0.99, P = 0.033).
Post-stroke muscle wasting in the non-paretic lower limb during the acute phase is independently associated with dependent ambulation after stroke.

View all citing articles on Scopus

: This work was supported by Laurens, nursing care organization in Rotterdam.

: The authors declare no conflicts of interest.

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JAMDA OnlineReview ArticleAssessment of Comorbidity Burden and its Association With Functional Rehabilitation Outcome After Stroke or Hip Fracture: A Systematic Review and Meta-Analysis

Abstract

Background

Aim

Design

Methods

Results

Conclusions

Section snippets

Search Strategy

Study Selection

Main Findings

Conclusions

Acknowledgments

J Am Med Dir Assoc

Arch Phys Med Rehabil

ArchP hys Med Rehabil

J Am Med Dir Assoc

J Am Med Dir Assoc

J Am Med Dir Assoc

J Clin Epidemiol

Crit Rev Oncol Hemato

Control Clin Trials

Arch Phys Med Rehabil

Arch Phys Med Rehabil

Arch Phys Med Rehabil

Arch Phys Med Rehabil

J Clin Epidemiol

Arch Gerontol Geriatr

Arch Phys Med Rehabil

Arch Phys Med Rehabil

J Clin Epidemiol

Prognostic factors for ambulation and activities of daily living in the subacute phase after stroke. A systematic review of the literature

Clin Rehabil

The influence of comorbidities and complications on discharge function in stroke rehabilitation inpatients

EuraMedicophys

Early prediction of outcome of activities of daily living after stroke: A systematic review

Stroke

What predicts a poor outcome in older stroke survivors? A systematic review of the literature

Disabil Rehabil

Comparative assessment of three different indices of multimorbidity for studies on health-related quality of life

Health Qual Life Outcomes

Measures of multimorbidity and morbidity burden for use in primary care and community settings: A systematic review and guide

Ann Fam Med

JAMDA Online
Review Article
Assessment of Comorbidity Burden and its Association With Functional Rehabilitation Outcome After Stroke or Hip Fracture: A Systematic Review and Meta-Analysis