Specific comorbidity risk adjustment was a better predictor of 5-year acute myocardial infarction mortality than general methods

被引:44
作者
Grunau, GL [1 ]
Sheps, S
Goldner, EM
Ratner, PA
机构
[1] Univ British Columbia, Dept Hlth Care & Epidemiol, Western Reg Training Ctr Hlth Serv Res, Vancouver, BC V6T 1W5, Canada
[2] Univ British Columbia, Dept Psychiat, Mental Hlth Evaluat & Community Consultat Unit, Vancouver, BC V6T 1W5, Canada
[3] Univ British Columbia, Sch Nursing, Vancouver, BC V5Z 1M9, Canada
关键词
cardiovascular disease; cohort studies; logistic models; risk adjustment; ROC curve; comorbidity;
D O I
10.1016/j.jclinepi.2005.08.007
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objective: To compare methods of risk adjustment in a population of individuals with acute myocardial infarction (AMI), in order to assist clinicians in assessing patient prognosis. Study Design and Setting: A historical inception cohort design was established, with follow-up of <= 5 years. A province-wide population-based administrative dataset from British Columbia, Canada, was used to select the cohort and construct variables. All individuals aged >= 66 years who had an AMI in 1994 or 1995 were selected (n = 4,874). The three risk-adjustment methods were the Ontario AMI prediction rule (OAMIPR), the D'Hoore adaptation of the Charlson Index, and the total number of distinct comorbidities. Logistic regression models were built including each of the adjustment methods, age, sex, socioeconomic status, previous AMI, and cardiac procedures at time of AMI. Results: The OAMIPR had the highest C-statistic and R-2. Conclusion: Clinicians are advised to consider the specific comorbidities that are present, not merely their number, and those that emerge over time, not merely those present at the time of the infarct. (C) 2006 Elsevier Inc. All rights reserved.
引用
收藏
页码:274 / 280
页数:7
相关论文
共 18 条
[1]  
*BRIT COL MIN HLTH, 1998, RESP SEN HEART STROK
[2]   A NEW METHOD OF CLASSIFYING PROGNOSTIC CO-MORBIDITY IN LONGITUDINAL-STUDIES - DEVELOPMENT AND VALIDATION [J].
CHARLSON, ME ;
POMPEI, P ;
ALES, KL ;
MACKENZIE, CR .
JOURNAL OF CHRONIC DISEASES, 1987, 40 (05) :373-383
[3]   Practical considerations on the use of the Charlson comorbidity index with administrative data bases [J].
DHoore, W ;
Bouckaert, A ;
Tilquin, C .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 1996, 49 (12) :1429-1433
[4]   The evolving clinical status of patients after a myocardial infarction: The importance of post-hospital data for mortality prediction [J].
Elmore, JG ;
Viscoli, CM ;
Horwitz, RI .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 1996, 49 (11) :1233-1238
[5]   Wide range of socioeconomic factors associated with mortality among cities in Japan [J].
Fukuda, Y ;
Nakamura, K ;
Takano, T .
HEALTH PROMOTION INTERNATIONAL, 2004, 19 (02) :177-187
[6]  
Hosmer D. W., 1989, APPL LOGISTIC REGRES, DOI DOI 10.1097/00019514-200604000-00003
[7]  
Lin CC, 2003, ETHNIC DIS, V13, P240
[8]   Widening socioeconomic inequalities in mortality in six Western European countries [J].
Mackenbach, JP ;
Bos, V ;
Andersen, O ;
Cardano, M ;
Costa, G ;
Harding, S ;
Reid, A ;
Hemström, Ö ;
Valkonen, T ;
Kunst, AE .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2003, 32 (05) :830-837
[9]   A NOTE ON A GENERAL DEFINITION OF THE COEFFICIENT OF DETERMINATION [J].
NAGELKERKE, NJD .
BIOMETRIKA, 1991, 78 (03) :691-692
[10]  
Naylor CD, 1999, CARDIOVASCULAR HLTH