The evolving clinical status of patients after a myocardial infarction: The importance of post-hospital data for mortality prediction

Abstract

Studies predicting mortality after myocardial infarction (MI) usually rely on in-hospital data, and combine patients admitted for the first MI with recurrent MI patients. Since treatment decisions are often made or modified at the first outpatient clinic visit, this study was designed to evaluate the importance of post-hospital data on mortality prediction after a first myocardial infarction (MI). An inception cohort of patients enrolled in the Beta-Blocker in Heart Attack Trial ( n = 2830) was included. Forty-three variables (including in-hospital and post-hospital data) were evaluated using stepwise logistic regression. Ten variables were independently associated with 1-year mortality: five used in-hospital data (history of hypertension, hypercholesterolemia, congestive heart failure [CHF], ventricular tachycardia, and age); and five variables depended on post-hospital data collected at the first outpatient visit (CHF after discharge, New York Heart Association functional class, heart rate, pulmonary rales, and smoking). Two predictive systems were developed that partitioned patients into one of four classes with distinct mortality risks: a composite system using the 10 in- and post-hospital variables, and a system using only the 5 in-hospital variables. Mortality risk for the composite system classes ranged from 0.6 to 20.0% (I [ n = 861], 0.6%; II [ n = 1151], 2.3%; III [ n = 698], 4.3%; IV [ n = 120], 20.0%). In contrast, the range of mortality risk using the in-hospital data only system was less (1 to 8.3%). Most importantly, a distinct gradient within each class of the in-hospital data only system was created by the addition of the post-hospital data. This study demonstrates that risk stratification after an acute first MI is improved by the addition of post-hospital data.