Publication details
New Models for Prediction of Post-Operative Pulmonary Complications in Lung Resection Candidates
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | ERJ Open Research |
| MU Faculty or unit | |
| Citation | |
| Web | https://openres.ersjournals.com/content/early/2024/04/19/23120541.00978-2023 |
| Doi | http://dx.doi.org/10.1183/23120541.00978-2023 |
| Description | Introduction In recent years, ventilatory efficiency (VE/VCO2 slope) and partial pressure of end-tidal carbon dioxide (PETCO2) emerged as independent predictors of post-operative pulmonary complications (PPC). Single parameters may give only partial information regarding peri-procedural 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 PETCO2 (for patients with no available CPET data), the second used VE/VCO2 slope (for patients with available CPET data). ROC 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 FEV1/FVC ratio as risk factors. In addition, the first model also included rest PETCO2, while the second model used VE/VCO2 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. |