Background
Acute lung injury (ALI) is a serious postoperative complication with limited treatment options. A preoperative risk prediction model would assist both clinicians and scientists interested in ALI. The objective of this investigation was to develop a surgical lung injury prediction (SLIP) model to predict risk of postoperative ALI based on readily available preoperative risk factors.
Methods
This is secondary analysis of a prospective cohort investigation including adult patients undergoing high-risk surgery. Preoperative risk factors for postoperative ALI were identified and evaluated for inclusion in the SLIP model. Multivariate logistic regression was used to develop the model. Model performance was assessed with the area under the Receiver Operating Characteristics Curve and the Hosmer and Lemeshow Goodness-of-fit test.
Results
Out of 4,366 patients, 113 (2.6%) developed early postoperative ALI. Predictors of postoperative ALI in multivariate analysis which were maintained in the final SLIP model included high-risk cardiac, vascular, and thoracic surgery, diabetes mellitus, chronic obstructive pulmonary disease, gastroesophageal reflux disease, and alcohol abuse. The SLIP score discriminated patients who developed early postoperative ALI from those who did not with an area under the Receiver Operating Characteristic Curve (95% CI) of 0.82 (0.78 – 0.86) and was well calibrated (Hosmer Lemeshow p = 0.55). Internal validation using 10-fold cross-validation noted minimal loss of diagnostic accuracy with a mean +/− standard deviation area under the Receiver Operating Characteristic Curve of 0.79 +/− 0.08.
Conclusions
Using readily available preoperative risk factors, we developed the SLIP scoring system to predict risk of developing early postoperative ALI.