New models for prediction of postoperative pulmonary complications in lung resection candidates
Status PubMed-not-MEDLINE Language English Country Great Britain, England Media electronic-ecollection
Document type Journal Article
PubMed
39286055
PubMed Central
PMC11403594
DOI
10.1183/23120541.00978-2023
PII: 00978-2023
Knihovny.cz E-resources
- Publication type
- Journal Article MeSH
INTRODUCTION: In recent years, ventilatory efficiency (minute ventilation (V'E)/carbon dioxide production (V'CO2 ) slope) and partial pressure of end-tidal carbon dioxide (P ETCO2 ) 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 ETCO2 (for patients with no available CPET data), the second used V'E/ V'CO2 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 ETCO2 , while the second model used V'E/V'CO2 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.
1st Department of Surgery St Anne's University Hospital Brno Czech Republic
Department of Anesthesiology and Intensive Care St Anne's University Hospital Brno Czech Republic
Department of Cardiovascular Diseases Mayo Clinic Rochester MN USA
Department of Respiratory Diseases University Hospital Brno Brno Czech Republic
Department of Sports Medicine and Rehabilitation St Anne's University Hospital Brno Czech Republic
Department of Surgery University Hospital Brno Brno Czech Republic
Faculty of Medicine Masaryk University Brno Czech Republic
Institute of Biostatistics and Analyses Ltd Brno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Czech Republic
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