Published online before print July 1, 2004, doi: 10.1161/01.STR.0000135225.80898.1c
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(Stroke. 2004;35:1941.)
© 2004 American Heart Association, Inc.
Original Contributions |
Charlson Index Comorbidity Adjustment for Ischemic Stroke Outcome Studies
From the Durham VA Medical Center (L.B.G., D.B.M.), the Department of Medicine (Neurology [L.B.G.] and General Internal Medicine [G.P.S., D.B.M.]), the Stroke Policy Program, Center for Clinical Health Policy Research (L.B.G., G.P.S., D.B.M.), and the Center for Cerebrovascular Disease (L.B.G., G.P.S., D.B.M.), Duke University, Durham, NC; and the Office of Minority Health and Research (R.D.H.), National Institute of Neurological Disorders and Stroke, Bethesda, Md.
Correspondence to Dr Larry B. Goldstein, Director of Duke Center for Cerebrovascular Disease, Head of Stroke Policy Program, Center for Clinical Health Policy Research, Box 3651, Duke University Medical Center, Durham, NC 27710. E-mail golds004{at}mc.duke.edu
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Abstract |
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Background and Purpose— The Charlson Index is commonly used in outcome studies to adjust for patient comorbid conditions, but has not been specifically validated for use in studies of ischemic stroke. The purpose of the present study was to determine whether outcomes of ischemic stroke patients varied on the basis of the Charlson Index.
Methods— The Department of Veterans Affairs (VA) Stroke Study prospectively identified stroke patients admitted to 9 VA hospitals between April 1995 and March 1997. The Charlson Index was scored on the basis of discharge International Classification of Diseases, 9th Revision, Clinical Modification coding and dichotomized (low comorbidity 0 or 1 versus high 
2) for analysis. Validity was assessed on the basis of modified Rankin score at hospital discharge (good outcome 0 or 1 versus poor 2 or dead) and 1-year mortality, adjusting for initial stroke severity.
Results— Of the 960 enrolled ischemic stroke patients, 23% had a Charlson Index of 0, 34% 1, 22% 2, 12% 3, and 8% 4. Forty-eight percent of those with a low Charlson Index had a good outcome at discharge versus 37% of those with a high Charlson Index (P<0.001). For 1-year mortality, the proportions were 16% versus 26%, respectively (P<0.001). Logistic regression adjusting for initial stroke severity showed that those with a high Charlson Index had 36% increased odds of having a poor outcome at discharge (P=0.038) and 72% greater odds of death at 1 year (P=0.001). Every 1-point increase in Charlson Index was independently associated with a 15% increase in the odds of a poor outcome at discharge (P<0.005) and a 29% increase in the odds of death by 1 year (P<0.001).
Conclusions— These data support the validity of the Charlson Index as a measure of comorbidity for use in ischemic stroke outcome studies.
Key Words: stroke • outcome • morbidity • mortality • outcome assessment
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Introduction |
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Several studies have developed mathematical models for predicting the outcomes of acute ischemic stroke patients. These types of prognostic models generally require data obtained either prospectively or through medical record review and allow for statistical adjustments on the basis of the patients’ relevant comorbid conditions. Outcome studies that use large databases also need to consider other diseases that patients may have that could impact the analyses. Direct access to the patients’ medical records is often impractical or impossible. These databases generally include listings of comorbid conditions categorized using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) coding system. The Charlson Index is a comorbidity scoring system that includes weighting factors on the basis of disease severity (Table 1).1 The system was developed originally as a prognostic indicator on the basis of patients with a variety of conditions admitted to a general medical service and then validated in an independent cohort of women with breast cancer. The Charlson Index has been used subsequently to account for the impact of comorbid conditions on studies of conditions such as ischemic stroke. Despite being used for this purpose, the Charlson Index has not been specifically validated for ischemic stroke outcome studies. The purpose of the present study was to determine whether a modified version of the Charlson Index on the basis of hospital discharge ICD-9-CM codes reflected short-term functional status and 1-year mortality in a cohort of patients with acute ischemic stroke independent of stroke severity.
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Subjects and Methods |
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Study Design
Data collected as part of the Department of Veterans Affairs (VA) Stroke (VASt) Study were used for this analysis. The overall study design has been published previously.2 Briefly, the VASt Study was a 9-site nationwide prospective observational cohort study of 1073 acute stroke patients hospitalized within the VA Health Administration between April 1, 1995, and March 31, 1997. For confidentiality, individual sites are not identified. Data were collected through medical record review using a standardized chart abstraction protocol. The institutional review board for each site approved the study protocol.
Subjects
A research assistant identified patients with possible stroke within 48 hours of hospital admission by screening admission logs. The diagnosis was then confirmed by review of medical records and discussion with the patient’s attending physician. This patient identification method was supplemented by review of the hospital discharge files for diagnoses of intracerebral hemorrhage (ICD-9-CM code 431) or acute cerebral infarction (ICD-9-CM code 434 or 436). In addition, all discharge ICD-9-CM codes were recorded. Patients whose stroke was iatrogenic because of brain trauma or neoplasm, occurred during hospitalization for other medical conditions, was an extension of a previous stroke, or occurred >7 days before admission were excluded. The present analysis focused only on patients with ischemic stroke. Those with hemorrhagic stroke were not included.
Demographic Variables and Clinical Outcome
Demographic characteristics included age, sex, and self-reported race/ethnic group (white, black, Hispanic, other). Stroke subtype was assigned using the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria with the aid of a computerized system to optimize reliability.3 Stroke severity was determined retrospectively with the Canadian Neurological Scale.2,4,5 The patient’s functional status at hospital discharge (modified Rankin score [mRS]6) was recorded prospectively, and mortality rates at 1 year were ascertained.
Modified Charlson Index
Comorbid conditions were classified with a modified version of the Charlson Index on the basis of hospital discharge ICD-9-CM codes. (Table 1)1 Each of the indicated diagnoses is assigned a weight and summed to provide a patient’s total score. The original Charlson Index includes cerebrovascular disease (weight 1) and hemiplegia (weight 2). Because these items are reflected in the condition being evaluated (ie, stroke), they are not included. In addition, the Charlson Index has separate categories for mild versus moderate or severe liver disease as well as moderate or severe renal disease. Because disease severity cannot be derived from ICD-9-CM codes, liver disease codes were all categorized as reflecting mild disease, and all renal disease codes were categorized as reflecting moderate or severe disease (Table 1). Patients with diabetes and renal disease were included in the diabetes with end-organ damage category.
Statistical Analysis
The modified Charlson Index was dichotomized (low comorbidity 0 or 1 versus high 2) for analysis. The mRS was also dichotomized as commonly used in stroke outcome studies (good outcome, mRS 0 or 1 versus poor outcome, mRS 2 or dead). The relationships between the unadjusted modified Charlson Index and functional status at hospital discharge as well as 1-year mortality were determined. Multivariable logistic regression was then used to determine the independent effect of Charlson Index on each outcome, controlling for stroke severity.
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Results |
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Of 1073 enrolled patients, 960 had an ischemic stroke. The individual sites enrolled 61 to 118 patients each. Because there was no difference in outcomes among the participating institutions, the data were collapsed across sites for further analysis.
The patient mean age was 68.1 (±9.5) years with 68.8% whites, 27.7% blacks, and 3.2% Hispanics. Comorbid conditions by history included diabetes in 36.0%, hypertension in 58.1%, ischemic cardiac disease in 26.0%, atrial fibrillation in 12.7%, and congestive heart failure in 7.7%. More than 76% of the patients had a history of cigarette smoking. On the basis of the TOAST classification scheme, 13.5% of patients had large-vessel atherothrombotic, 18.8% cardioembolic, and 24.7% lacunar subtypes, with the remainder having strokes of undetermined or multiple causes. Because this was a veteran population, only 1.6% of the patients were women. The mean admission Canadian Neurological Scale score was 8.4 (±2.5). Figure 1 gives the distribution of Charlson Index scores for the entire cohort (57.4% of patients had Charlson scores of either 0 or 1. Overall, 7.9% of patients died by 1 month after stroke, and 20.8% died by 1 year. Among patients surviving at discharge, 15.8% had an mRS of 0; 31.9%, 1; 3.5%, 2; 13.0%, 3; 27.6%, 4; and 8.1% had an mRS of 5.
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Table 1 gives the proportion of patients having each condition on the basis of the indicated discharge ICD-9-CM codes. Other ICD-9-CM codes that may appropriately reflect diagnoses within the indicated categories exist but were not used for any patient in this cohort. Table 1 also gives the frequencies that each ICD-9-CM code was assigned. Table 2 gives ICD-9-CM codes used in >1% of the patients in the cohort, the proportions of patients (n=960) assigned each code, and the proportion of patients with a condition included in each category accounted for by each code. Figure 2 gives the univariable relationships between the modified Charlson Index and the mRS at hospital discharge and 1-year mortality.
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Logistic regression adjusting for initial stroke severity showed that those with a high Charlson Index (ie, 2) had 36% increased odds of having a poor outcome (mRS 2) at discharge (P=0.038) and 72% greater odds of death at 1 year (P=0.001). The relationship between Charlson Index and discharge status remained if the Rankin Index was considered as a full rather than a dichotomized scale. Every 1-point increase in the Charlson Index was associated independently with a 15% increase in the odds of a poor outcome at discharge (P<0.005) and a 29% increase in the odds of death by 1 year (P<0.001). A high Charlson Index was also associated with 32% increased odds of death at hospital discharge (P=0.266) and 60% increased odds of 30-day mortality (0.066), but the differences were not significant after adjustment for stroke severity (P<0.001). Inclusion of age as a covariate yielded essentially equivalent results. (Compared with models that included only severity, the odds ratios for the Charlson Index in models that controlled for both age and severity increased by 0.9% to 3.5%.)
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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These data show that both functional outcome at the time of hospital discharge and 1-year mortality rates are associated independently with the number and severity of patients’ comorbid diseases as reflected in a modified version of the Charlson Index. This analysis specifically extends the usefulness of the Charlson Index as a measure of comorbidity for studies focused on acute ischemic stroke.
There are a variety of parameters that may reflect the validity of a scale. Content validity indicates how well a scale includes domains thought to be relevant to a condition. Convergent validity is demonstrated when scales or items that are thought to measure the same construct have high correlation coefficients. Divergent validity is demonstrated when items or scales thought to measure different constructs have low correlation coefficients. In this case, there is no "gold standard" measure of aggregate comorbidity for comparison, so these types of validity cannot be assessed. The present study does provide evidence of both known groups and predictive validity of the Charlson Index in this setting. Known groups validity tests for differences in scores from patients known to be clinically different (in this study, they are reflected in discharge status and 1-year mortality). Predictive validity reflects how well a scale predicts future events.
It should be noted that this analysis was designed to determine whether the Charlson Index would provide a valid comorbidity adjustment for stroke outcome studies and not intended to provide a clinically applicable prognostic model. A variety of such models exist that were developed to address particular questions.7–11 In addition, several recent studies that generally require prospective data collection have devised and tested prognostic models for use in specific settings. For example, the Combined Lysis of Thrombus in Brain Ischemia Using Transcranial Ultrasound and Systemic TPA investigators assessed the relative contributions of recanalization during the first hours of middle cerebral artery occlusion and the clinical and neuroradiological data in predicting good outcome (mRS 
2) 3 months after stroke.12 Recanalization within 300 minutes (odds ratio [OR], 4.11; 95% CI, 2.42 to 6.95), baseline National Institutes of Health Stroke Scale (NIHSS) score (OR, 0.35; 95% CI, 0.16 to 0.78), stroke volume on brain computed tomography scan (Alberta Stroke Program Early CT score;13 OR, 2.98; 95% CI, 1.13 to 7.85), systolic blood pressure (OR, 0.32; 95% CI, 0.13 to 0.76), and proximal occlusion (OR, 0.25; 95% CI, 0.10 to 0.61) were associated independently with good outcome at 3 months. The German Stroke Study Collaboration validated 2 models predicting complete functional recovery (Barthel Index >95) and mortality at 3 months on the basis of patients’ age and NIHSS score obtained within 6 hours of symptom onset.14 Severely affected patients were not included in the analysis. The models explained 51% of the variance for complete functional recovery and 30% of the variance in mortality.
Although the Charlson Index would be useful to adjust for comorbidity in studies with prospective data collection, the present analysis supports its use in outcome studies involving large databases that often lack clinical information. These databases record comorbid diseases using ICD-9-CM codes. As shown in Table 2, a few ICD-9-CM codes accounted for the majority of conditions in each Charlson Index category. In addition to those codes actually used in stroke patients included in this cohort as reflected in the tables, other ICD-9-CM codes are available that can also reflect diseases included in the Charlson Index. It is well recognized that ICD-9-CM codes may be inaccurate, including their application to stroke conditions.15–17 There was no attempt to confirm the accuracy of ICD-9-CM discharge coding for comorbid conditions as applied in the present study because such confirmation would not be feasible in large database outcome studies in which records generally cannot be linked to a patient’s medical records. Moreover, there may be undercoding of comorbid conditions among patients who die in the hospital. Finally, this modified version of the Charlson Index ignores previous strokes, the impact of which is only partially reflected in the Canadian Stroke Scale. Additional studies could be conducted in nonveteran populations to further investigate the utility of case-mix adjustment on the basis of the modified Charlson Index. Despite these limitations, ICD-9-CM coding remained related independently to both functional status at hospital discharge and 1-year mortality, supporting its usefulness for this purpose.
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Acknowledgments |
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This work was supported by a grant from the Department of Veterans Affairs (SDR 93-003 to R.D.H. and D.B.M.). L.B.G. was supported in part by a National Institutes of Health mid-career development award in patient-oriented research (K24 NS02165). R.D.H. was supported in part by a grant from the VA Cooperative Studies/Epidemiologic Research and Information Center Programs (CSP/ERIC 602).
Received March 5, 2004; revision received May 7, 2004; accepted May 14, 2004.
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References |
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3. Goldstein LB, Jones MR, Matchar DB, Edwards LJ, Hoff J, Chilukuri V, Armstrong B, Horner RD. Improving the reliability of stroke subgroup classification using the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria. Stroke. 2001; 32: 1091–1097.
4. Goldstein LB, Chilukuri V. Retrospective assessment of initial stroke severity with the Canadian Neurological Scale. Stroke. 1997; 48: 1181–1184.
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