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(Circulation. 2002;105:1082.)
© 2002 American Heart Association, Inc.

Clinical Investigation and Reports

Risk and Predictors of Stroke After Myocardial Infarction Among the Elderly

Results From the Cooperative Cardiovascular Project

Judith H. Lichtman, PhD; Harlan M. Krumholz, MD; Yun Wang, MS; Martha J. Radford, MD; Lawrence M. Brass, MD

From the Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven; Section of Chronic Disease Epidemiology, School of Epidemiology and Public Health (J.H.L., H.M.K., L.M.B.), Department of Cardiology (H.M.K., M.J.R.), and Yale Stroke Program, Department of Neurology (L.M.B.), Yale University School of Medicine, New Haven; Qualidigm (H.M.K., Y.W., M.J.R., L.M.B.), Middletown; and Neurology Service (L.M.B.), VA Connecticut Healthcare System, West Haven, Conn.

Correspondence to Judith Lichtman, PhD, CORE, GB 415, 20 York St, New Haven, CT 06504. E-mail Judith.Lichtman{at}yale.edu

	Abstract

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Background— Stroke is an important outcome after acute myocardial infarction. Studies that have examined this relationship have largely excluded older patients, even though half of stroke admissions occur among patients 75 years of age and older.

Methods and Results— Among 111 023 medicare patients discharged with a principal diagnosis of acute myocardial infarction during an 8-month period in 1994 to 1995, we identified hospital admissions for ischemic stroke within 6 months of discharge. The rate of admission was 2.5% within 6 months. Independent predictors of ischemic stroke were age 75 years, black race, no aspirin at discharge, frailty, prior stroke, atrial fibrillation, diabetes, hypertension, and history of peripheral vascular disease. To identify individuals at increased risk for stroke, a risk stratification score was constructed from identified factors. The 6-month stroke admission rate for patients with a score of 4 or higher (20% of the total sample) was 4%.

Conclusions— The risk of stroke after myocardial infarction is substantial, with about 1 in 40 patients suffering an ischemic stroke within 6 months of discharge. Simple clinical factors can predict the risk of stroke and, based on these factors, we identified 20% of older patients who have a 1 in 25 chance of being hospitalized for a stroke within 6 months of discharge.

Key Words: stroke • myocardial infarction • risk factors • aging

	Introduction

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The rate of stroke after a myocardial infarction (MI) has been estimated to be 1% per year.^1–4 When stroke occurs after MI, the outcome is worse⁵ and the cost is greater.⁶ Predictors of stroke after MI include increased age, history of atrial fibrillation, size and location of the infarct, and ventricular dysfunction.^1–5,7 These rates and predictors have been derived largely from clinical studies and cohorts that often exclude older patients and those with more significant comorbid conditions. Determining rates from younger, healthier patients may lead to an underestimation of the frequency of this event in the elderly, because the incidence of stroke is strongly associated with age.^8,9 Among patients hospitalized with a stroke, 77% are 65 years of age or older and half are older than 75 years of age.¹⁰ Acute MI (AMI) accounts for >200 000 hospitalizations annually among patients 65 years of age and older.¹¹ With improved survival after MI and an increasing number of elderly people in the population, stroke after MI will be an increasingly common problem in the coming decades.

Determining the risk and predictors of stroke after MI has important implications for public health, patient management, clinical trials, and resource use,⁶ especially among older patients who have more comorbid disease and are at greatest risk for stroke. The importance of risk stratification after MI and its implications for intensity of interventions are becoming increasingly recognized in guidelines and clinical practice.^12,13

Our objectives were to measure the 6-month stroke admission rate after AMI in a large population-based cohort of elderly patients, to identify independent predictive factors for stroke after AMI, and to develop a risk stratification score to determine the risk of stroke after AMI among individual patients. To address these objectives, we analyzed data from more than 111 000 elderly patients included in the Cooperative Cardiovascular Project (CCP), a large, geographically diverse population-based cohort of patients hospitalized with AMI.

	Methods

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We used data from the CCP, a Health Care Financing Administration quality improvement program focusing on the treatment of patients with AMI. The development and background for the CCP are described in detail in previous publications.¹⁴

The CCP cohort includes Medicare patients hospitalized in nongovernmental, acute-care hospitals in the United States and Puerto Rico with a principal discharge diagnosis of AMI, ICD-9-CM code 410, in 1994 and 1995. Admissions that were not related to the immediate care of an AMI (in which the fifth digit of the ICD-9-CM code is 2) were excluded from the sample. Patients were identified during an 8-month period (varying in each state) using hospital bills (UB-92 claims data) in the Medicare National Claims History File. Stroke outcomes were also determined using the National Claims History File. The file includes all patients treated under fee-for-service plans but does not include all of the patients treated as part of Medicare managed care risk contracts. For patients admitted to hospitals in the CCP pilot states (Alabama, Connecticut, Iowa, and Wisconsin), hospitalizations were sampled from a 4-month period (August 1995 through November 1995).

Data Collection
To obtain the information for this project from the medical records, Health Care Financing Administration established 2 clinical data abstraction centers to abstract records. Trained technicians abstracted predefined variables from copies of the hospital record. Data reliability was monitored by random reabstractions, with overall variable agreement averaging >90%.¹⁵

Outcome Variables
The outcome variable was a hospital admission for ischemic stroke (ICD-9 433 to 437) within 6 months of hospital discharge (MI admission). Strokes that occurred during the index hospitalization were not included as an outcome.

Independent Variables
The independent variables in this study included age, sex, race, admission status, medical history, clinical status, and treatment. Admission status included residence before admission. A frailty measure was created from 3 core domains of functioning, cognitive status, mobility, and urinary continence. Clinical characteristics included location of infarct, creatinine phosphokinase, systolic and diastolic blood pressure at admission, left ventricular ejection fraction, atrial fibrillation, and Killip class at admission. Laboratory variables included albumin, hematocrit level, and a composite blood urea nitrogen (BUN) and creatinine variable (BUN >40 or creatinine >2.5 mg/dL) to assess renal dysfunction. Comorbidities included prior stroke, previous bypass surgery, hypertension, diabetes, prior MI, heart failure (based on past medical history or admission chest x-ray), peripheral vascular disease, and present smoking status. Medication variables included aspirin, ß blockers, thrombolytic therapy, ACE inhibitors, calcium blockers, warfarin, insulin, and statins.

Statistical Analyses
Bivariate associations of demographic and clinical variables with 6-month stroke admission rates were performed with the ² test for categorical variables. Variables considered in a Cox proportional hazards model (backward stepwise selection), selected based on clinical judgment and prior reports, were present in 5% of the sample or were significant based on bivariate associations. Time to 6-month stroke admission was the dependent variable, with censoring for deaths. The assumption of proportionality was met in the analysis. The rate of stroke admission was calculated using the Kaplan-Meier survival analysis with censoring for stroke admissions and deaths. A risk stratification score was developed based on the selected variables from the Cox proportional hazards model. Weighting was based on the coefficients, but because the effect sizes were similar for the included variables, all variables were given equal weight in the final risk scale. All calculations were performed using the software program PC-SAS 6.12 (SAS Corp).

	Results

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The sample consisted of 111 023 patients with a confirmed AMI who were discharged alive and were without a terminal illness (Table 1). The mean age was 76 years. Overall, 2.5% (2532 patients) were admitted with an ischemic stroke within 6 months of discharge.

View this table: [in this window] [in a new window]   Table 1. Study Sample and Exclusions

Older patients, women, patients of black race, and patients with any frailty are at increased risk for a stroke after MI (Table 2). Comorbid conditions associated with higher stroke admission rates included prior stroke, hypertension, diabetes, atrial fibrillation, heart failure, and peripheral vascular disease. A decreased ejection fraction was not among the clinical characteristics associated with stroke. Aspirin and ß blockers during hospitalization and at discharge were associated with reduced stroke admission rates.

View this table: [in this window] [in a new window]   Table 2. Bivariate Associations of Predictor Variables and 6-Month Stroke Admission

Using a backward stepwise selection process, the following factors were independent significant predictors of the risk of ischemic stroke within 6 months (Table 3): age 75 years (risk ratio [RR], 1.29; 95% CI, 1.18 to 1.40), black race (RR, 1.33; 95% CI, 1.16 to 1.53), previous stroke (RR, 1.75; 95% CI, 1.59 to 1.92), atrial fibrillation (RR, 1.52; 95% CI, 1.39 to 1.67), history of peripheral vascular disease (RR, 1.31; 95% CI, 1.18 to 1.47), diabetes (RR, 1.26; 95% CI, 1.16 to 1.37), hypertension (RR, 1.13; 95% CI, 1.04 to 1.23), any frailty (RR, 1.27; 95% CI, 1.16 to 1.38), and aspirin at discharge (RR, 0.86; 95% CI, 0.79 to 0.93).

View this table: [in this window] [in a new window]   Table 3. Cox Proportional Hazards Model Predicting Ischemic Stroke (6 Month) After MI

To identify individuals at increased risk for stroke, a risk stratification score was developed using the 8 demographic and clinical variables selected by the Cox proportional hazards model. To make the risk stratification score easy to use and because the effect sizes were similar, each factor was given equal weight. The rate of 6-month stroke admission among patients without any of these predictors was 0.93% (n=9895, 9% of cohort) and increased with each additional predictor to 3.58% for 4 factors and 4.17% for 5 or more factors (Mantel-Haenszel; P<0.001; Figure). The median score for the cohort was 2 factors. One fifth of the sample had 4 or more factors, with a corresponding 6-month stroke admission rate of 4%, or almost 8% per year for the group at highest risk.

View larger version (22K): [in this window] [in a new window]   Stroke rate by number of scaled factors. Distribution of sample by score: 0, 9%; 1, 22%; 2, 28%; 3, 23%; 4, 12%; 5, 6%.

	Discussion

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The importance of stroke after MI has been underappreciated, especially among older persons.¹⁶ Our results demonstrate that stroke after MI is much more common than previously reported. The 6-month rate of stroke was 2.5%, and assuming a steady rate beyond the first few weeks,¹ the projected annual rate of stroke is 5% per year. This is nearly 5 times greater than previous reports.^1–3 We were able to devise a risk stratification scale that identified a high-risk group in which 1 of every 12 AMI patients will be admitted with a stroke within 1 year.

Recent studies have reported the risk of stroke after MI to be 1% per year.^1–3 Tanne et al³ found a 1-year stroke/transient ischemic attack rate of 1% (48 of 4808) among consecutive AMI patients with an average age of 67 years compared with an average age of 76 years in our sample. Mooe et al² identified an overall event rate of 1.07% per year (124 of 11 620), with a slightly higher event rate for older patients, 1.24% for patients 65 to 69 years of age (35 of 2815), and 1.45% for patients 70 to 74 years of age (51 of 3526). However, they excluded patients older than 74 years of age. Their results were limited by the small number of stroke cases (124 cases), the lack of follow-up beyond the first month, and the exclusion of patients 75 years of age or older. Loh et al¹ found a rate of 1.5% per patient year of follow-up among 2231 AMI patients with left ventricular dysfunction enrolled in the Survival and Ventricular Enlargement trial. The participants were predominantly men (82%), younger (mean of 59 years), and healthier (excluded patients with prior stroke).

Predictors of Stroke
We identified new predictors for stroke after MI, including history of hypertension, diabetes, and peripheral vascular disease. These predictors are generally accepted as risk factors for ischemic stroke^17,18 but have not been previously identified as predictors of stroke after MI. Several factors identified by other studies were not associated with an increased risk of stroke in our sample, including left ventricular ejection fraction, ST-segment elevation, and size and anterior location of MI.^1–4,7,19 Our findings suggest that the characteristics of the MI itself may be less important in the elderly than more general stroke risk factors.

The mechanism of stroke after AMI may change with increased duration from the AMI,²⁰ and age may also account for some of these differences.²¹ Risk factors for stroke after MI in the elderly may be different from those previously found for younger cohorts. This changing pathophysiology of stroke risk with age has also been shown for coronary disease risk factors, such as total cholesterol and systolic blood pressure.^22,23 Studies reporting location of infarction as a predictor have generally focused on the acute phase of the MI and short-term follow-up.^2,3,19 Studies that have looked at a longer postdischarge period have corroborated our finding that MI location may be a less important risk factor for stroke after hospital discharge.²⁴

As expected, patients prescribed aspirin at discharge had a lower rate of stroke after MI.^1,25–27 In clinical trials, warfarin has reduced the risk of clinically important end points (including stroke) after MI.^25–29 Although anticoagulation therapy was associated with lower stroke risk in bivariate analyses, this was not sustained in the Cox-model analyses, even with a reduced significance threshold. Bodenheimer et al²⁴ reported a similar lack of association between warfarin and stroke prevention after MI.

Risk Stratification
A risk stratification score allows identification of individuals at greatest risk for stroke after AMI. The risk of stroke among the 20% of patients who had at least 4 of the 8 identified factors was 4 times higher than patients with none of these factors. Patients in this group had a 1 in 25 chance of being hospitalized for a stroke in the 6 months after discharge. Tanne et al³ also created a risk score from identified predictors; however, their analysis was based on only 48 strokes.

Strategies that stratify stroke risk based on individual patient characteristics offer important opportunities to target prevention strategies for patients and influence the choice of therapy in terms of safety and cost.¹³ Patients at low risk of stroke may be ideally suited for low-risk therapies, such as aspirin, whereas patients at high risk may justify the use of more aggressive or expensive therapies, such as anticoagulation. The importance of risk stratification is widely recognized for stroke prevention in selecting therapies for patients with atrial fibrillation,^30,31 carotid stenosis,³² and hypertension.³³

Our results illustrate the importance of using representative cohorts (especially with regard to age and comorbid disease) when estimating stroke risk. Stroke incidence is strongly associated with age.^8,9 It is highest for patients 75 years of age or older, who are underrepresented in many clinical studies but account for half of all strokes. Studies that do not account for age and comorbid disease not only underestimate the risk of stroke in the individual, but they also shift attention away from the public health importance of cerebrovascular diseases and opportunities for enhancing preventive strategies.

Finally, identifying patients at high risk will allow for targeted patient counseling after MI. This should include information about the signs and symptoms of a stroke and the appropriate action if these signs are present. Studies have shown that most patients do not recognize the signs and symptoms of a stroke, which results in delays that limit the ability to use time-dependent acute therapies.^34,35

Our study does have some limitations. The data were based on a retrospective chart review. Some patient characteristics may not have been documented in the medical record. We were limited in our study to patients older than age 65 years; however, given the strongly age-dependent incidence of stroke, we feel the present results provide important insight into the risk of stroke after MI in the general community. Because an ischemic stroke was defined by a hospital admission, minor strokes that were not referred for hospitalization are not included in these analyses. Our results can be interpreted as a conservative estimate of stroke incidence after MI. Concern has been raised about the use of ICD-9-CM coding for ischemic stroke³⁶; however, the rates we found cannot be attributed to possible overreporting of stroke in this population.

Despite these limitations, this study includes the largest and most geographically diverse sample of older MI patients who have not been excluded from the study based on comorbid illness or older age. For this reason, the present results provide the most accurate estimates of stroke after MI among elderly patients in the community.

The incidence of stroke after MI in older patients is higher than previously estimated, and the risk of stroke after MI increases proportionally with the number of identified risk factors present. Previous reports, derived largely from younger samples and controlled clinical trials, significantly underestimate true risk in the elderly community. Emphasis should be placed on raising physician and patient awareness of the risk of stroke after MI and the need to identify and effectively treat patients with preventive therapies. By improving patient education and providing effective and appropriate preventive therapies, we have the opportunity to lessen the physical and economic consequences of stroke in the community.

	Acknowledgments

 
This study was supported in part by grant DF96-167 from the Patrick and Catherine Weldon Donaghue Medical Research Foundation, Hartford, Conn. Dr Lichtman is a Goddess Fund Career Development Scholar.

	Footnotes

 
The analyses on which this publication is based were performed under Contract Number 500-99-CT01, titled Utilization and Quality Control Peer Review Organization for the State of Connecticut, sponsored by the Centers for Medicare and Medicaid Services, US Department of Health and Human Services. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the United States government. The authors assume full responsibility for the accuracy and completeness of the ideas presented. This article is a direct result of the Health Care Quality Improvement Program initiated by the Centers for Medicare and Medicaid Services, which has encouraged identification of quality improvement projects derived from analysis of patterns of care, and therefore the study required no special funding on the part of this contractor. Ideas and contributions to the authors concerning experience in engaging with issues presented are welcomed.

Received March 20, 2001; revision received December 21, 2001; accepted December 21, 2001.

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This Article Abstract Full Text (PDF) All Versions of this Article: 105/9/1082    most recent hc0902.104708v1 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 Lichtman, J. H. Articles by Brass, L. M. Search for Related Content PubMed PubMed Citation Articles by Lichtman, J. H. Articles by Brass, L. M. Pubmed/NCBI databases Medline Plus Health Information Heart Attack Stroke Related Collections Acute myocardial infarction Acute Cerebral Infarction