New models for prediction of postoperative pulmonary
complications in lung resection candidates

J 2024

New models for prediction of postoperative 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 postoperative pulmonary complications in lung resection candidates

Autoři

SVOBODA, Michal (203 Česká republika, domácí), Ivan ČUNDRLE (203 Česká republika, domácí), Marek PLUTINSKÝ (703 Slovensko, domácí), Pavel HOMOLKA (203 Česká republika), Ladislav MITÁŠ (203 Česká republika, domácí), Zdeněk CHOVANEC (203 Česká republika, domácí), Lyle J OLSON a Kristián BRAT (703 Slovensko, domácí)

Vydání

ERJ open research, SHEFFIELD, EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024, 2312-0541

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30203 Respiratory systems

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.300 v roce 2023

Kód RIV

RIV/00216224:14110/24:00137805

Organizační jednotka

Lékařská fakulta

DOI

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

UT WoS

001340127300002

Klíčová slova anglicky

postoperative pulmonary complications; lung resection

Štítky

14110120, 14110122, 14110215, 14110223, 14119612, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 11. 2024 10:19, Mgr. Tereza Miškechová

Anotace

V originále

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.

Citovat

SVOBODA, Michal, Ivan ČUNDRLE, Marek PLUTINSKÝ, Pavel HOMOLKA, Ladislav MITÁŠ, Zdeněk CHOVANEC, Lyle J OLSON a Kristián BRAT. New models for prediction of postoperative pulmonary complications in lung resection candidates. ERJ open research. SHEFFIELD: EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024, roč. 10, č. 4, s. 1-11. ISSN 2312-0541. Dostupné z: https://dx.doi.org/10.1183/23120541.00978-2023.

@article{2455464,
   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},
   article_number = {4},
   doi = {http://dx.doi.org/10.1183/23120541.00978-2023},
   keywords = {postoperative pulmonary complications; lung resection},
   language = {eng},
   issn = {2312-0541},
   journal = {ERJ open research},
   title = {New models for prediction of postoperative pulmonary complications in lung resection candidates},
   url = {https://publications.ersnet.org/content/erjor/10/4/00978-2023},
   volume = {10},
   year = {2024}
}

TY  - JOUR
ID  - 2455464
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 postoperative pulmonary complications in lung resection candidates
JF  - ERJ open research
VL  - 10
IS  - 4
SP  - 1-11
EP  - 1-11
PB  - EUROPEAN RESPIRATORY SOC JOURNALS LTD
SN  - 23120541
KW  - postoperative pulmonary complications
KW  - lung resection
UR  - https://publications.ersnet.org/content/erjor/10/4/00978-2023
N2  - 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.
ER  -

SVOBODA, Michal, Ivan ČUNDRLE, Marek PLUTINSKÝ, Pavel HOMOLKA, Ladislav MITÁŠ, Zdeněk CHOVANEC, Lyle J OLSON a Kristián BRAT. New models for prediction of postoperative pulmonary complications in lung resection candidates. \textit{ERJ open research}. SHEFFIELD: EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024, roč.~10, č.~4, s.~1-11. ISSN~2312-0541. Dostupné z: https://dx.doi.org/10.1183/23120541.00978-2023.

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