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Multivariate prediction of in-hospital mortality associated with surgical procedures
G De Ritis1, C Giovannini, S Picardo
1Department of Anesthesiology and Intensive Care, University La Sapienza of Rome.
Background:
The aims of this prospective multicenter study were to identify variables associated with in-hospital mortality among patients undergoing surgical procedures, to develop a prediction rule, and to statistically validate its reliability.
Methods:
Data from 24,654 consecutive informed patients over 15 years of age were collected from 22 surgical centers between January 1989 and December 1990. Using logistic regression analysis separate models were fit for seven surgical disciplines to predict the risk of 30-day in hospital mortality. Variables used to construct the regression models included age, sex, systolic blood pressure, renal dysfunction, hepatic dysfunction, concomitant diseases, severity of surgery, priority of surgery and duration of anesthesia. The performance of the prediction rule was evaluated by computing sensitivity, specificity and predictive values, analyzing the ROC curve and comparing observed with expected deaths.
Results:
The significance of the independent variables varied within each model. All models significantly predicted the occurrence of in-hospital mortality. At a 0.5 cuptoint of predicted risk sensitivity of prediction rule was 99.89%, positive predictive value 98.51%, and overall predictive value 98.41%, whereas specificity was 7.92% and negative value slightly higher than 50%. The area under the ROC curve was 0.80 (perfect, 1.0). The correlation between observed and expected deaths was 0.99.
Conclusion:
This prediction rule, developed using multicenter data, is characterized by the following advantages: includes only nine variables; can be utilized by seven different surgical disciplines; is highly accurate, and is easily available to clinicals with access to a microcomputer or programmable calculator. This validated multivariate prediction rule would be useful both to calculate the risk of mortality for an individual surgical patient and to contrast observed and expected mortality rates for an institution or a particular clinician.