New Models for Prediction of Postoperative Pulmonary Complications in Lung Resection Candidates
The result's identifiers
Result code in 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>
Alternative codes found
RIV/00216224:14110/24:00137805 RIV/65269705:_____/24:00080366
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
New Models for Prediction of Postoperative Pulmonary Complications in Lung Resection Candidates
Original language description
Introduction In recent years, ventilatory efficiency (minute ventilation (V'E)/carbon dioxide production (V'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'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 PETCO2, 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. (C) The authors 2024.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30203 - Respiratory systems
Result continuities
Project
<a href="/en/project/NU21-06-00086" target="_blank" >NU21-06-00086: High intensity respiratory muscle training as a pre-habilitation in lung surgery candidates</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ERJ Open Research
ISSN
2312-0541
e-ISSN
2312-0541
Volume of the periodical
10
Issue of the periodical within the volume
4
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
00978-2023
UT code for WoS article
001340127300002
EID of the result in the Scopus database
2-s2.0-85205262631