This Article Abstract Full Text (PDF) All Versions of this Article: 35/8/1941    most recent 01.STR.0000135225.80898.1cv1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Goldstein, L. B. Articles by Horner, R. D. Search for Related Content PubMed PubMed Citation Articles by Goldstein, L. B. Articles by Horner, R. D. Related Collections Health policy and outcome research Acute Cerebral Infarction

(Stroke. 2004;35:1941.)
© 2004 American Heart Association, Inc.

Original Contributions

Charlson Index Comorbidity Adjustment for Ischemic Stroke Outcome Studies

Larry B. Goldstein, MD; Gregory P. Samsa, PhD; David B. Matchar, MD Ronnie D. Horner, PhD

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

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
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

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
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.

View this table: [in this window] [in a new window]   TABLE 1. Charlson Index Categories, ICD-9 Codes, and Frequencies (n=960)

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
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.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
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.

View larger version (17K): [in this window] [in a new window]   Figure 1. Distribution of modified Charlson Index scores.

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.

View this table: [in this window] [in a new window]   TABLE 2. Charlson Index Categories, Frequencies of ICD-9 Codes, and Proportions Within Each Category

View larger version (21K): [in this window] [in a new window]   Figure 2. Relationships between Charlson Index and mRS at hospital discharge and 1-year mortality.

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%.)

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
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.

	Acknowledgments

 
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.

	References

Top Abstract Introduction Subjects and Methods Results Discussion References

 

1. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40: 373–383.[CrossRef][Medline] [Order article via Infotrieve]
2. Jones MR, Horner RD, Edwards LJ, Hoff J, Armstrong SB, Smith-Hammond CA, Matchar DB, Oddone EZ. Racial variation in initial stroke severity. Stroke. 2000; 31: 563–567.[Abstract/Free Full Text]
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.[Abstract/Free Full Text]
4. Goldstein LB, Chilukuri V. Retrospective assessment of initial stroke severity with the Canadian Neurological Scale. Stroke. 1997; 48: 1181–1184.
5. Bushnell CD, Johnston DCC, Goldstein LB. Retrospective assessment of initial stroke severity: comparison of the NIH Stroke Scale and the Canadian Neurological Scale. Stroke. 2001; 32: 656–660.[Abstract/Free Full Text]
6. Rankin J. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott Med J. 1957; 2: 200–215.[Medline] [Order article via Infotrieve]
7. Johnston KC, Connors AFJ, Wagner DP, Knaus WA, Wang X, Haley ECJ. A predictive risk model for outcomes of ischemic stroke. Stroke. 2000; 31: 448–455.[Abstract/Free Full Text]
8. Hankey GJ, Jamrozik K, Broadhurst RJ, Forbes S, Burvill PW, Anderson CS, Stewart-Wynne EG. Five-year survival after first-ever stroke and related prognostic factors in the Perth Community Stroke Study. Stroke. 2000; 31: 2080–2086.[Abstract/Free Full Text]
9. Stineman MG, Maislin G, Fiedler RC, Granger CV. A prediction model for functional recovery in stroke. Stroke. 1997; 28: 550–556.[Abstract/Free Full Text]
10. Modell JG, Foster NL, DuPree ES, Ackermann RJ, Petry NA, Bluemlein LE, Kuhl DE. Prognostication of recovery following stroke using the comparison of CT and technetium-99m HM-PAO SPECT. J Nucl Med. 1990; 31: 61–66.[Abstract/Free Full Text]
11. Loewen SC, Anderson BA. Predictors of stroke outcome using objective measurement scales. Stroke. 1990; 21: 78–81.[Abstract/Free Full Text]
12. Molina CA, Alexandrov AV, Demchuck AM, Saqqur M, Uchino K, Alvarez-Sabin J. Improving the predictive accuracy of recanalization on stroke outcome in patients treated with tissue plasminogen activator. Stroke. 2004; 35: 151–157.[Abstract/Free Full Text]
13. Barber PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. Lancet. 2000; 355: 670–1674.
14. Weimar C, König IR, Kraywinkel K, Ziegler A, Diener HC. Age and National Institutes of Health Stroke Scale score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia. Stroke. 2004; 35: 158–162.[Abstract/Free Full Text]
15. Benesch C, Witter DMJ, Wilder AL, Duncan PW, Samsa GP, Matchar DB. Inaccuracy of the International Classification of Diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease. Neurology. 1997; 49: 660–664.[Abstract/Free Full Text]
16. Goldstein LB. Accuracy of ICD-9-CM coding for the identification of patients with acute ischemic stroke: effect of modifier codes. Stroke. 1998; 29: 1602–1604.[Abstract/Free Full Text]
17. Leibson CL, Naessens JM, Brown RD, Whisnant JP. Accuracy of hospital discharge abstracts for identifying stroke. Stroke. 1994; 25: 2348–2355.[Abstract]

This article has been cited by other articles:

				H. Zhu and M. D. Hill Stroke: The Elixhauser Index for comorbidity adjustment of in-hospital case fatality Neurology, July 22, 2008; 71(4): 283 - 287. [Abstract] [Full Text] [PDF]

				J. Tang, J. Y. Wan, and J. E. Bailey Performance of Comorbidity Measures to Predict Stroke and Death in a Community-Dwelling, Hypertensive Medicaid Population Stroke, July 1, 2008; 39(7): 1938 - 1944. [Abstract] [Full Text] [PDF]

				C. D. Bushnell, J. Lee, P. W. Duncan, L. K. Newby, and L. B. Goldstein Impact of Comorbidities on Ischemic Stroke Outcomes in Women Stroke, July 1, 2008; 39(7): 2138 - 2140. [Abstract] [Full Text] [PDF]

				J. A. Driver, T. Kurth, J. E. Buring, J. M. Gaziano, and G. Logroscino Parkinson disease and risk of mortality: A prospective comorbidity-matched cohort study Neurology, April 15, 2008; 70(16_Part_2): 1423 - 1430. [Abstract] [Full Text] [PDF]

				M. L. Sorror, S. Giralt, B. M. Sandmaier, M. De Lima, M. Shahjahan, D. G. Maloney, H. J. Deeg, F. R. Appelbaum, B. Storer, and R. Storb Hematopoietic cell transplantation specific comorbidity index as an outcome predictor for patients with acute myeloid leukemia in first remission: combined FHCRC and MDACC experiences Blood, December 15, 2007; 110(13): 4606 - 4613. [Abstract] [Full Text] [PDF]

				G. Saposnik, A. Baibergenova, M. O'Donnell, M. D. Hill, M. K. Kapral, V. Hachinski, and On behalf of the Stroke Outcome Research Canada (S Hospital volume and stroke outcome: Does it matter? Neurology, September 11, 2007; 69(11): 1142 - 1151. [Abstract] [Full Text] [PDF]

				E. Onukwugha and C. D. Mullins Racial Differences in Hospital Discharge Disposition among Stroke Patients in Maryland Med Decis Making, May 1, 2007; 27(3): 233 - 242. [Abstract] [PDF]

				G. Saposnik, A. Baibergenova, N. Bayer, and V. Hachinski Weekends: A Dangerous Time for Having a Stroke? Stroke, April 1, 2007; 38(4): 1211 - 1215. [Abstract] [Full Text] [PDF]

				S. Kumar, S. Savitz, G. Schlaug, L. Caplan, and M. Selim Antiplatelets, ACE inhibitors, and statins combination reduces stroke severity and tissue at risk Neurology, April 25, 2006; 66(8): 1153 - 1158. [Abstract] [Full Text] [PDF]

				M. L. Sorror, M. B. Maris, R. Storb, F. Baron, B. M. Sandmaier, D. G. Maloney, and B. Storer Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT Blood, October 15, 2005; 106(8): 2912 - 2919. [Abstract] [Full Text] [PDF]

				M. K. Kapral, J. Fang, M. D. Hill, F. Silver, J. Richards, C. Jaigobin, A. M. Cheung, and for the Investigators of the Registry of the Canad Sex Differences in Stroke Care and Outcomes: Results From the Registry of the Canadian Stroke Network Stroke, April 1, 2005; 36(4): 809 - 814. [Abstract] [Full Text] [PDF]

				D. B. Matchar and A. G. Rudd Health Policy and Outcomes Research 2004 Stroke, February 1, 2005; 36(2): 225 - 227. [Full Text] [PDF]

This Article Abstract Full Text (PDF) All Versions of this Article: 35/8/1941    most recent 01.STR.0000135225.80898.1cv1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Goldstein, L. B. Articles by Horner, R. D. Search for Related Content PubMed PubMed Citation Articles by Goldstein, L. B. Articles by Horner, R. D. Related Collections Health policy and outcome research Acute Cerebral Infarction