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.

Basic information

Original name

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

Authors

SVOBODA, Michal (203 Czech Republic, belonging to the institution); Ivan ČUNDRLE (203 Czech Republic, belonging to the institution); Marek PLUTINSKÝ (703 Slovakia, belonging to the institution); Pavel HOMOLKA (203 Czech Republic); Ladislav MITÁŠ (203 Czech Republic, belonging to the institution); Zdeněk CHOVANEC (203 Czech Republic, belonging to the institution); Lyle J OLSON and Kristián BRAT (703 Slovakia, belonging to the institution)

Edition

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

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

30203 Respiratory systems

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Impact factor

Impact factor: 4.300 in 2023

RIV identification code

RIV/00216224:14110/24:00137805

Organization unit

Faculty of Medicine

DOI

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

UT WoS

001340127300002

EID Scopus

2-s2.0-85205262631

Keywords in English

postoperative pulmonary complications; lung resection

Abstract

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.

Cite

SVOBODA, Michal; Ivan ČUNDRLE; Marek PLUTINSKÝ; Pavel HOMOLKA; Ladislav MITÁŠ; Zdeněk CHOVANEC; Lyle J OLSON and Kristián BRAT. New models for prediction of postoperative pulmonary complications in lung resection candidates. ERJ open research. SHEFFIELD: EUROPEAN RESPIRATORY SOC JOURNALS LTD, 2024, vol. 10, No 4, p. 1-11. ISSN 2312-0541. Available from: 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 and 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, vol.~10, No~4, p.~1-11. ISSN~2312-0541. Available from: https://dx.doi.org/10.1183/23120541.00978-2023.

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