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Risk Stratification for Lung Cancer Patients
A comprehensive review of relevant clinical literature on evidence-based recommendations and existing prediction models specific to lung cancer surgery was undertaken. Preoperative risk assessment parameters such as pulmonary function tests (PFT), cardiopulmonary exercise testing (CPET), Brunelli mo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cureus
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683799/ https://www.ncbi.nlm.nih.gov/pubmed/36439594 http://dx.doi.org/10.7759/cureus.30643 |
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author | Jain, Anchal Philip, Bejoy Begum, Munira Wang, William Ogunjimi, Michael Harky, Amer |
author_facet | Jain, Anchal Philip, Bejoy Begum, Munira Wang, William Ogunjimi, Michael Harky, Amer |
author_sort | Jain, Anchal |
collection | PubMed |
description | A comprehensive review of relevant clinical literature on evidence-based recommendations and existing prediction models specific to lung cancer surgery was undertaken. Preoperative risk assessment parameters such as pulmonary function tests (PFT), cardiopulmonary exercise testing (CPET), Brunelli models, Thoracoscore and frailty were analyzed for predicting postoperative risk of complications. When assessing fitness for surgery, the primarily used PFT parameters such as predictive postoperative forced expiratory volume in one second (FEV1) and diffusion capacity for carbon monoxide (DLCO ) showed conflicting evidence in determining a positive correlation with postoperative mortality. CPET variables predicted higher complication risk when VO2peak < 10ml/kg/min, AT < 11ml/kg/min and ventilation/carbon dioxide production (VE/VCO2) was in range of 34-40. While a cardiac risk index like the Thoracic Revised Cardiac Risk Index (ThRCRI) predicted major cardiovascular compromise, a thoracic risk index like Thoracoscore proved imprecise. Lastly, frailty is used to risk stratify patients in clinical practice but a recognized validated model specific to thoracic surgery is non-existent. When considering patients for lung cancer surgery, some dilemma exists regarding the accuracy of clinical prediction models and their external validation. There is a pressing need for the development of a consolidated clinically robust risk stratification model to predict complications after thoracic resections. |
format | Online Article Text |
id | pubmed-9683799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cureus |
record_format | MEDLINE/PubMed |
spelling | pubmed-96837992022-11-25 Risk Stratification for Lung Cancer Patients Jain, Anchal Philip, Bejoy Begum, Munira Wang, William Ogunjimi, Michael Harky, Amer Cureus Anesthesiology A comprehensive review of relevant clinical literature on evidence-based recommendations and existing prediction models specific to lung cancer surgery was undertaken. Preoperative risk assessment parameters such as pulmonary function tests (PFT), cardiopulmonary exercise testing (CPET), Brunelli models, Thoracoscore and frailty were analyzed for predicting postoperative risk of complications. When assessing fitness for surgery, the primarily used PFT parameters such as predictive postoperative forced expiratory volume in one second (FEV1) and diffusion capacity for carbon monoxide (DLCO ) showed conflicting evidence in determining a positive correlation with postoperative mortality. CPET variables predicted higher complication risk when VO2peak < 10ml/kg/min, AT < 11ml/kg/min and ventilation/carbon dioxide production (VE/VCO2) was in range of 34-40. While a cardiac risk index like the Thoracic Revised Cardiac Risk Index (ThRCRI) predicted major cardiovascular compromise, a thoracic risk index like Thoracoscore proved imprecise. Lastly, frailty is used to risk stratify patients in clinical practice but a recognized validated model specific to thoracic surgery is non-existent. When considering patients for lung cancer surgery, some dilemma exists regarding the accuracy of clinical prediction models and their external validation. There is a pressing need for the development of a consolidated clinically robust risk stratification model to predict complications after thoracic resections. Cureus 2022-10-24 /pmc/articles/PMC9683799/ /pubmed/36439594 http://dx.doi.org/10.7759/cureus.30643 Text en Copyright © 2022, Jain et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Anesthesiology Jain, Anchal Philip, Bejoy Begum, Munira Wang, William Ogunjimi, Michael Harky, Amer Risk Stratification for Lung Cancer Patients |
title | Risk Stratification for Lung Cancer Patients |
title_full | Risk Stratification for Lung Cancer Patients |
title_fullStr | Risk Stratification for Lung Cancer Patients |
title_full_unstemmed | Risk Stratification for Lung Cancer Patients |
title_short | Risk Stratification for Lung Cancer Patients |
title_sort | risk stratification for lung cancer patients |
topic | Anesthesiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683799/ https://www.ncbi.nlm.nih.gov/pubmed/36439594 http://dx.doi.org/10.7759/cureus.30643 |
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