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The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer

PURPOSE: The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lu...

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Autores principales: Chae, Kum Ju, Choi, Hyemi, Jeong, Won Gi, Kim, Jinheum
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Cancer Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582478/
https://www.ncbi.nlm.nih.gov/pubmed/34809414
http://dx.doi.org/10.4143/crt.2021.902
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author Chae, Kum Ju
Choi, Hyemi
Jeong, Won Gi
Kim, Jinheum
author_facet Chae, Kum Ju
Choi, Hyemi
Jeong, Won Gi
Kim, Jinheum
author_sort Chae, Kum Ju
collection PubMed
description PURPOSE: The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model. MATERIALS AND METHODS: We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model. RESULTS: The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model. CONCLUSION: Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients.
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spelling pubmed-95824782022-10-26 The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer Chae, Kum Ju Choi, Hyemi Jeong, Won Gi Kim, Jinheum Cancer Res Treat Original Article PURPOSE: The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model. MATERIALS AND METHODS: We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model. RESULTS: The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model. CONCLUSION: Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients. Korean Cancer Association 2022-10 2021-11-19 /pmc/articles/PMC9582478/ /pubmed/34809414 http://dx.doi.org/10.4143/crt.2021.902 Text en Copyright © 2022 by the Korean Cancer Association https://creativecommons.org/licenses/by-nc/4.0/This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Chae, Kum Ju
Choi, Hyemi
Jeong, Won Gi
Kim, Jinheum
The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
title The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
title_full The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
title_fullStr The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
title_full_unstemmed The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
title_short The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
title_sort value of the illness-death model for predicting outcomes in patients with non–small cell lung cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582478/
https://www.ncbi.nlm.nih.gov/pubmed/34809414
http://dx.doi.org/10.4143/crt.2021.902
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