New Models for Prediction of Post-Operative Pulmonary Complications in Lung Resection Candidates

Introduction In recent years, ventilatory efficiency (minute ventilation ( V ′ E )/carbon dioxide production ( V ′ CO 2 ) slope) and partial pressure of end-tidal carbon dioxide ( P ETCO 2 ) have emerged as independent predictors of postoperative pulmonary complications (PPC). Single parameters may give only partial information regarding periprocedural hazards. Accordingly, our aim was to create prediction models with improved ability to stratify PPC risk in patients scheduled for elective lung resection surgery. Methods This post hoc analysis was comprised of consecutive lung resection candidates from two prior prospective trials. All individuals completed pulmonary function tests and cardiopulmonary exercise testing (CPET). Logistic regression analyses were used for identification of risk factors for PPC that were entered into the final risk prediction models. Two risk models were developed; the first used rest P ETCO 2 (for patients with no available CPET data), the second used V ′ E / V ′ CO 2 slope (for patients with available CPET data). Receiver operating characteristic analysis with the De-Long test and area under the curve (AUC) were used for comparison of models. Results The dataset from 423 patients was randomly split into the derivation (n=310) and validation (n=113) cohorts. Two final models were developed, both including sex, thoracotomy, “atypical” resection and forced expiratory volume in 1 s/forced vital capacity ratio as risk factors. In addition, the first model also included rest P ETCO 2 , while the second model used V ′ E / V ′ CO 2 slope from CPET. AUCs of risk scores were 0.795 (95% CI: 0.739–0.851) and 0.793 (95% CI: 0.737–0.849); both p<0.001. No differences in AUCs were found between the derivation and validation cohorts. Conclusions We created two multicomponental models for PPC risk prediction, both having excellent predictive properties.