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Risk Factors for Incident Stroke Among Patients with End-Stage Renal Disease

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Risk Factors for Incident Stroke Among Patients with End-Stage Renal Disease J Am Soc Nephrol 14: 2623–2631, 2003 Risk Factors for Incident Stroke among Patients with End-Stage Renal Disease STEPHEN L. SELIGER,* DANIEL L. GILLEN,† DAVID TIRSCHWELL,‡ HAIMANOT WASSE,* BRYAN R. KESTENBAUM,* and CATHERINE O. STEHMAN-BREEN§ *Division of Nephrology, University of Washington, Seattle, Washington; †Department of Biostatistics, University of Washington School of Public Health and Community Medicine, Seattle, Washington; ‡Department of Neurology, University of Washington, Seattle, Washington; and §Division of Nephrology, Veterans Affairs Puget Sound Health Care System, University of Washington, Seattle, Washington Abstract. Although patients with ESRD experience markedly were associated with the risk of stroke—serum albumin (per higher rates of stroke, no studies in the US have identified 1 g/dl decrease, hazard ratio [HR] ϭ 1.43), height-adjusted risk factors associated with stroke in this population. It was body weight (per 25% decrease, HR ϭ 1.09), and a subjec- hypothesized that black race, malnutrition, and elevated BP tive assessment of undernourishment (HR ϭ 1.27)—as was would be associated with the risk of stroke among patients higher mean BP (per 10 mmHg, HR ϭ 1.11). The associa- with ESRD. Data from the United States Renal Data Sys- tion between black race varied by cardiac disease status, tems were used. Adult Medicare-insured hemodialysis and with blacks estimated to be at lower risk than whites among peritoneal dialysis patients without a history of stroke or individuals with cardiac disease (HR ϭ 0.74), but at higher transient ischemic attack (TIA) were considered for analy- risk among individuals without cardiac disease (HR ϭ sis. The primary outcome was hospitalized or fatal stroke. 1.24). This study confirms the extraordinarily high rates of Cox proportional hazards models were used to determine the stroke in ESRD patients on dialysis and identifies high mean associations between the primary predictor variables and BP and malnutrition as potentially modifiable risk factors. stroke. The rate of incident stroke was 33/1,000 person- The association between black race and stroke differs by years in the study sample. After adjustment for age and cardiac disease status; the reasons for this differing effect of other patient characteristics, three markers of malnutrition race deserve further investigation. Patients with ESRD experience markedly advanced atheroscle- not be generalizable to the US dialysis population. In addition, rotic disease of the cerebral vasculature (1–4). Although we although blacks have much higher rates of stroke in the general recently reported a 5- to 10-fold risk of hospitalized ischemic population, no previous studies have examined black race as a and hemorrhagic stroke among ESRD patients compared with risk factor for incident stroke in the dialysis population. We non-ESRD individuals (5), little is known regarding potential used data collected by the United States Renal Data System stroke risk factors among the ESRD population. Risk factors (USRDS) to identify patient characteristics associated with the for stroke in this population are likely to differ from those in risk of hospitalized or fatal stroke in dialysis patients, with the non-ESRD population. For example, elevated BP and body specific focus on black race, hypertension, and malnutrition as mass index are risk factors for stroke in the general population, risk factors. whereas in the dialysis population, they are associated with a lower risk of adverse outcomes such as all-cause and cardiac Materials and Methods death (6–9). Only one small study, conducted in a Japanese Study Population dialysis population, has examined risk factors for stroke (10), We used data from the USRDS Dialysis Morbidity and Mortality identifying hypertension as the only significant predictor. Studies, Waves 2 to 4 (DMMS-2 to -4). Details of the studies per- However, the study had limited power, and results from the formed by the USRDS are described elsewhere (12). Briefly, the younger and healthier Japanese dialysis population (11) may USRDS collects demographic and clinical data on all patients who have survived Ͼ90 d of renal replacement therapy for ESRD. DMMS-2 was a prospective observational study of a sample of adult Received January 27, 2003. Accepted July 15, 2003. patients who initiated dialysis in 1996 and early 1997, with deliberate Correspondence to Dr. Stephen L. Seliger, Division of Nephrology, Box oversampling of patients on peritoneal dialysis. DMMS-3 and -4 were 356521, University of Washington, Seattle, WA 98102. Phone: 206-543-3792; retrospective studies of random samples of hemodialysis patients who Fax: 206-685-8661; E-mail: [email protected] were alive on December 31, 1993. Data collection techniques and 1046-6673/1410-2623 content were kept consistent across the three studies, allowing them to Journal of the American Society of Nephrology be combined for use in epidemiologic research. The study population Copyright © 2003 by the American Society of Nephrology for this analysis included all patients who were in DMMS-2 to -4 and DOI: 10.1097/01.ASN.0000088722.56342.A8 treated with dialysis and were Medicare-insured with no previous 2624 Journal of the American Society of Nephrology J Am Soc Nephrol 14: 2623–2631, 2003 history of stroke or transient ischemic attack. Following the recom- Statistical Analyses mendations of the USRDS for studies of hospitalization rates (13), The Cox proportional hazards model for censored survival data was patients for whom fee-for-service Medicare was not the primary used to assess the association between the primary risk factors of insurer were excluded from our analysis, because hospitalization data interest and incident stroke after adjustment for potential confounders. for these patients are incomplete in the USRDS database (see the Primary predictor variables of interest included race (white, black, Outcome section). Asian, other), mean BP (MBP; calculated as [systolic BP ϩ 2*dia- stolic BP]/3), and markers of malnutrition (serum albumin, body weight, and a subjective assessment of undernourishment). Adjust- Ascertainment of Baseline Patient Characteristics ment variables to control confounding were a priori chosen on the Baseline patient data were abstracted by dialysis facility personnel basis of their potential relationship with the outcome of interest. from each patient’s medical record and through patient interview. Cigarette smoking was not included as an adjustment covariate in the Patient characteristics ascertained included demographic (age, gender, primary model because of the high degree of missing information and race), laboratory (albumin, cholesterol, hemoglobin, calcium, (15%) for this variable in the USRDS; rather, the potential confound- parathyroid hormone, and phosphorous), clinical (cause of renal dis- ing effect of smoking was assessed in an exploratory analysis. Renal ease, history of cardiovascular disease (CVD), history of stroke or replacement modality (hemodialysis, peritoneal dialysis, transplant) transient ischemic attack (TIA), dialysis vintage, smoking status, and was modeled as time-dependent covariates, allowing patients to a subjective assessment of undernourishment), and other measure- switch risk groups over the course of follow-up. All models were ments (height, weight, and BP). Previous CVD was defined as any further adjusted for DMMS study via stratification. diagnosis of coronary artery disease, myocardial infarction, coronary Following recommendations from the USRDS (13), incident pa- artery bypass, angioplasty, cardiac arrest, or congestive heart failure. tients were not considered at risk for hospitalizations until day 90 of The average of up to three measurements of BP during three consec- ESRD. Patients were followed from day 90 of ESRD or (for prevalent utive dialysis sessions, recorded either before dialysis (in hemodial- patients) from the DMMS study start date and were censored at loss ysis patients) or randomly (in peritoneal dialysis patients), were used to follow-up, nonstroke death, or the end of the study period (Decem- in this analysis. For DMMS-2 patients, only single measurements of ber 31, 1999). laboratory variables were available. For patients in DMMS-3 and -4, Formal and graphic techniques were used to confirm the presence multiple values for laboratory measurements were available for up to of proportional hazards and to identify potential outliers. We hypoth- 3 mo preceding the start of the study; for these patients, all available esized that all continuous covariates would be linearly related to the values were averaged before inclusion into a statistical model. outcome of interest; however, exploratory residual analyses were performed to investigate functional form further. In particular, we explored linear and nonlinear forms of MBP to determine whether Outcome there was a “U”-or“J”-shaped relationship between BP and stroke. The primary outcome was defined as first hospitalized stroke or Effect modification by age, gender, CVD, and DMMS wave was fatal nonhospitalized stroke. Hospitalization for stroke was deter- explored and tested via stratification and the use of multiplicative mined by linking Medicare hospital billing records to each patient interactions. through unique identifier codes supplied by the USRDS. Diagnosis of stroke was based on the International Classification of Diseases, 9th Results Revision, Clinical Modification (ICD-9-CM) diagnosis codes con- Study Population tained in these billing
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