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

J 2024

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

SVOBODA, Michal, Ivan ČUNDRLE, Marek PLUTINSKÝ, Pavel HOMOLKA, Ladislav MITÁŠ et. al.

Základní údaje

Originální název

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

Autoři

SVOBODA, Michal, Ivan ČUNDRLE, Marek PLUTINSKÝ, Pavel HOMOLKA, Ladislav MITÁŠ, Zdeněk CHOVANEC, Lyle J OLSON a Kristián BRAT

Vydání

ERJ Open Research, SHEFFIELD, EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024, 2312-0541

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.600 v roce 2022

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1183/23120541.00978-2023

UT WoS

999

Štítky

14110120, 14110122, 14110215, 14110223, 14119612

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 19. 9. 2024 13:09, Mgr. Tereza Miškechová

Anotace

V originále

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.

Citovat

SVOBODA, Michal, Ivan ČUNDRLE, Marek PLUTINSKÝ, Pavel HOMOLKA, Ladislav MITÁŠ, Zdeněk CHOVANEC, Lyle J OLSON a Kristián BRAT. New Models for Prediction of Post-Operative Pulmonary Complications in Lung Resection Candidates. ERJ Open Research. SHEFFIELD: EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024. ISSN 2312-0541. Dostupné z: https://dx.doi.org/10.1183/23120541.00978-2023.

@article{2434538,
   author = {Svoboda, Michal and Čundrle, Ivan and Plutinský, Marek and Homolka, Pavel and Mitáš, Ladislav and Chovanec, Zdeněk and Olson, Lyle J and Brat, Kristián},
   article_location = {SHEFFIELD},
   doi = {http://dx.doi.org/10.1183/23120541.00978-2023},
   language = {eng},
   issn = {2312-0541},
   journal = {ERJ Open Research},
   title = {New Models for Prediction of Post-Operative Pulmonary Complications in Lung Resection Candidates},
   url = {https://openres.ersjournals.com/content/early/2024/04/19/23120541.00978-2023},
   year = {2024}
}

TY  - JOUR
ID  - 2434538
AU  - Svoboda, Michal - Čundrle, Ivan - Plutinský, Marek - Homolka, Pavel - Mitáš, Ladislav - Chovanec, Zdeněk - Olson, Lyle J - Brat, Kristián
PY  - 2024
TI  - New Models for Prediction of Post-Operative Pulmonary Complications in Lung Resection Candidates
JF  - ERJ Open Research
PB  - EUROPEAN RESPIRATORY SOC JOURNALS LTD
SN  - 23120541
UR  - https://openres.ersjournals.com/content/early/2024/04/19/23120541.00978-2023
N2  - 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.
ER  -

SVOBODA, Michal, Ivan ČUNDRLE, Marek PLUTINSKÝ, Pavel HOMOLKA, Ladislav MITÁŠ, Zdeněk CHOVANEC, Lyle J OLSON a Kristián BRAT. New Models for Prediction of Post-Operative Pulmonary Complications in Lung Resection Candidates. \textit{ERJ Open Research}. SHEFFIELD: EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024. ISSN~2312-0541. Dostupné z: https://dx.doi.org/10.1183/23120541.00978-2023.

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