A scoring system predicting the risk for intensive care unit admission for complications after major lung resection: a multicenter analysis

Abstract

Background: We aimed to develop and validate a scoring system to predict intensive care unit (ICU) admission for complications after major lung resection for purposes of optimizing planning of resources for patient care.

Methods: Patients undergoing major lung resections performed between 2000 and 2006 at three thoracic surgery units were analyzed for unplanned admission to the ICU for complications. Variables were initially screened by univariate analysis. Selected variables were used in a stepwise logistic regression analysis that was validated by bootstrap analysis. The scoring system was developed by proportional weighting of the significant and reliable predictors estimates and validated on patients operated on in a different center.

Results: In the derivation set of 1297 patients, 82 (6.3%) had ICU admission for complications, and 30 died (associated mortality rate, 36.5%). Predictive variables and their scores were pneumonectomy, 2 points; and 1 point each for age older than 65, predicted postoperative forced expiratory volume in 1 second below 65%, predicted postoperative carbon monoxide lung diffusion capacity below 50%, and cardiac comorbidity. Patients were grouped into three risk classes by their scores, which were significantly associated with incremental risk of ICU admission in the validation set of 349 patients.

Conclusions: This scoring system predicts incremental risk of ICU admission for complications after major lung resection. This system may help in assessing the need for additional postoperative resources and in modifying indicators used to determine the appropriateness of initial transfer of postoperative patients from ICU or stepdown status and in developing criteria for future cost-effectiveness trials.