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To use data from four independent prospective studies to develop a clinical prediction rule for postoperative pulmonary complication risk, stratified in terms of the underlying patient condition. First, we estimated the study-specific mean absolute risk (ARM) of developing pulmonary complications for different patient characteristics and outcomes using logistic regression and the generalized linear model. We then built prediction rules by using the ARM and its estimates of the regression coefficients, the covariate effects and the covariate distribution across the populations. By fitting the rules using the data from all four studies, we aimed at increasing the power to detect important predictors and at avoiding the issue of separating the effects of true predictors and the omitted effect of other factors, such as demographics and length of stay. Data from 16,297 subjects were included from four independent prospective studies. Individual risk estimates were predicted and rules were constructed by using the data from all four studies. To avoid inappropriate extrapolation, we focused only on the events that occurred during the first three postoperative days. We constructed five rules that were stratified by the underlying patient condition (surgical procedure, cardiopulmonary disease or other). All rules were highly predictive, with areas under the receiver operating characteristic curve ranging from 0.81 to 0.87, and were adjusted for important clinical confounders. Our methods allow for developing and comparing rules with high predictive accuracy and the ability to identify patients at high risk of postoperative pulmonary complications.d) -3
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