Background: The acute physiology and chronic health evaluation (APACHE) scoring system has been validated in many different patient populations, however, patients with myocardial infarction (MI) were not included in the original data base. To evaluate the ability of APACHE scoring system in predicting in-hospital mortality, 694 patients with MI were studied.
Methods: Data had been collected prospectively in an ICU computer database in the past 3 years. Patients admitted in coronary care unit with acute MI or acute coronary syndrome who had previous history of MI were all included. Patients were divided into survivor and non-survivor data sets. Multiple logistic regression analysis was evaluated on the variables of APACHE II score to determine which variables could predict in-hospital mortality. A logistic regression model was used to study the mortality curves. The differences of APACHE II scores between survivors and non-survivors were compared. Correlation between observed and predicted mortality was also assessed.
Results: According to the statistical analysis, the non-survivors tended to have significantly greater APACHE II scores than those of survivors. The APACHE II values of non-survivors and survivors were 23.64 +/- 9.41 versus 13.35 +/- 7.14 (p < 0.001), respectively. Using multiple logistic regression analysis, we found that age, creatinine, coma scale, sodium and APACHE II score were capable of predicting the in-hospital mortality (p < 0.05). With use of the logistic model, a good correlation of predicted mortality rate to observed mortality rate was found (r = 0.992). This study demonstrated that lower APACHE II scores predicted survival while high scores predicted mortality. Mortality rate increased significantly when APACHE II score was > 25. An APACHE II score greater than 28.25 predicted a more than 50% in-hospital mortality.
Conclusions: This study demonstrates that the APACHE II scoring system is capable of predicting mortality in patients with MI, which makes this modality more applicable in the busy intensive care unit.
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