New Models for Prediction of Postoperative Pulmonary Complications in Lung Resection Candidates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00080366" target="_blank" >RIV/00159816:_____/24:00080366 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/24:00137805 RIV/65269705:_____/24:00080366

Výsledek na webu

<a href="https://openres.ersjournals.com/content/early/2024/04/19/23120541.00978-2023" target="_blank" >https://openres.ersjournals.com/content/early/2024/04/19/23120541.00978-2023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1183/23120541.00978-2023" target="_blank" >10.1183/23120541.00978-2023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

New Models for Prediction of Postoperative Pulmonary Complications in Lung Resection Candidates

Popis výsledku v původním jazyce

Introduction In recent years, ventilatory efficiency (minute ventilation (V&apos;E)/carbon dioxide production (V&apos;CO2) slope) and partial pressure of end-tidal carbon dioxide (PETCO2) 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 PETCO2 (for patients with no available CPET data), the second used V&apos;E/ V&apos;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, &quot;atypical&quot; resection and forced expiratory volume in 1 s/forced vital capacity ratio as risk factors. In addition, the first model also included rest PETCO2, while the second model used V&apos;E/V&apos;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&lt;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. (C) The authors 2024.

Název v anglickém jazyce

New Models for Prediction of Postoperative Pulmonary Complications in Lung Resection Candidates

Popis výsledku anglicky

Introduction In recent years, ventilatory efficiency (minute ventilation (V&apos;E)/carbon dioxide production (V&apos;CO2) slope) and partial pressure of end-tidal carbon dioxide (PETCO2) 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 PETCO2 (for patients with no available CPET data), the second used V&apos;E/ V&apos;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, &quot;atypical&quot; resection and forced expiratory volume in 1 s/forced vital capacity ratio as risk factors. In addition, the first model also included rest PETCO2, while the second model used V&apos;E/V&apos;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&lt;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. (C) The authors 2024.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30203 - Respiratory systems

Projekt

<a href="/cs/project/NU21-06-00086" target="_blank" >NU21-06-00086: Trénink dechových svalů jako způsob pre-habilitace před plicním resekčním zákrokem</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ERJ Open Research

ISSN

2312-0541

e-ISSN

2312-0541

Svazek periodika

10

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

00978-2023

Kód UT WoS článku

001340127300002

EID výsledku v databázi Scopus

2-s2.0-85205262631