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Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status

BACKGROUND: Cachexia is one of the most common complications affecting lung cancer patients that seriously affects their quality-of-life and survival time. This study aimed to analyze the predictors and prognostic factors of lung cancer cachexia as well as to develop a convenient and accurate clinic...

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Autores principales: Liu, Chen-An, Zhang, Qi, Ruan, Guo-Tian, Shen, Liu-Yi, Xie, Hai-Lun, Liu, Tong, Tang, Meng, Zhang, Xi, Yang, Ming, Hu, Chun-Lei, Zhang, Kang-Ping, Liu, Xiao-Yue, Shi, Han-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309732/
https://www.ncbi.nlm.nih.gov/pubmed/35898878
http://dx.doi.org/10.3389/fonc.2022.890745
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author Liu, Chen-An
Zhang, Qi
Ruan, Guo-Tian
Shen, Liu-Yi
Xie, Hai-Lun
Liu, Tong
Tang, Meng
Zhang, Xi
Yang, Ming
Hu, Chun-Lei
Zhang, Kang-Ping
Liu, Xiao-Yue
Shi, Han-Ping
author_facet Liu, Chen-An
Zhang, Qi
Ruan, Guo-Tian
Shen, Liu-Yi
Xie, Hai-Lun
Liu, Tong
Tang, Meng
Zhang, Xi
Yang, Ming
Hu, Chun-Lei
Zhang, Kang-Ping
Liu, Xiao-Yue
Shi, Han-Ping
author_sort Liu, Chen-An
collection PubMed
description BACKGROUND: Cachexia is one of the most common complications affecting lung cancer patients that seriously affects their quality-of-life and survival time. This study aimed to analyze the predictors and prognostic factors of lung cancer cachexia as well as to develop a convenient and accurate clinical prediction tool for oncologists. METHODS: In this multicenter cohort study, 4022 patients with lung cancer were retrospectively analyzed. The patients were randomly categorized into training and verification sets (7:3 ratio). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of cachexia in patients with lung cancer. Cox regression analysis was applied to determine independent prognostic factors in the patients with lung cancer cachexia. Meanwhile, two nomograms were established and evaluated by time-dependent receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: Stage, serum albumin, ALI, anemia, and surgery were independent risk factors for cachexia in patients with lung cancer. Patients with lung cancer cachexia have a shorter survival time. Sex, stage, serum albumin, ALI, KPS score, and surgery served as independent prognostic factors for patients with lung cancer cachexia. The area under the curves (AUCs) of diagnostic nomogram in the training and validation sets were 0.702 and 0.688, respectively, the AUCs of prognostic nomogram in the training set for 1-, 3-, and 5-year were 0.70, 0.72, and 0.75, respectively, while in the validation set the AUCs were 0.71, 0.75, and 0.79, respectively. The calibration curves and DCA of the two nomograms were consistent and the clinical benefit rate was high. CONCLUSION: Cachexia brings an additional economic burden and worsens the prognosis of lung cancer patients. The two nomograms can accurately screen and predict the probability of occurrence of cachexia in lung cancer and the prognosis of patients with lung cancer cachexia, and guide clinical work.
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spelling pubmed-93097322022-07-26 Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status Liu, Chen-An Zhang, Qi Ruan, Guo-Tian Shen, Liu-Yi Xie, Hai-Lun Liu, Tong Tang, Meng Zhang, Xi Yang, Ming Hu, Chun-Lei Zhang, Kang-Ping Liu, Xiao-Yue Shi, Han-Ping Front Oncol Oncology BACKGROUND: Cachexia is one of the most common complications affecting lung cancer patients that seriously affects their quality-of-life and survival time. This study aimed to analyze the predictors and prognostic factors of lung cancer cachexia as well as to develop a convenient and accurate clinical prediction tool for oncologists. METHODS: In this multicenter cohort study, 4022 patients with lung cancer were retrospectively analyzed. The patients were randomly categorized into training and verification sets (7:3 ratio). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of cachexia in patients with lung cancer. Cox regression analysis was applied to determine independent prognostic factors in the patients with lung cancer cachexia. Meanwhile, two nomograms were established and evaluated by time-dependent receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: Stage, serum albumin, ALI, anemia, and surgery were independent risk factors for cachexia in patients with lung cancer. Patients with lung cancer cachexia have a shorter survival time. Sex, stage, serum albumin, ALI, KPS score, and surgery served as independent prognostic factors for patients with lung cancer cachexia. The area under the curves (AUCs) of diagnostic nomogram in the training and validation sets were 0.702 and 0.688, respectively, the AUCs of prognostic nomogram in the training set for 1-, 3-, and 5-year were 0.70, 0.72, and 0.75, respectively, while in the validation set the AUCs were 0.71, 0.75, and 0.79, respectively. The calibration curves and DCA of the two nomograms were consistent and the clinical benefit rate was high. CONCLUSION: Cachexia brings an additional economic burden and worsens the prognosis of lung cancer patients. The two nomograms can accurately screen and predict the probability of occurrence of cachexia in lung cancer and the prognosis of patients with lung cancer cachexia, and guide clinical work. Frontiers Media S.A. 2022-07-11 /pmc/articles/PMC9309732/ /pubmed/35898878 http://dx.doi.org/10.3389/fonc.2022.890745 Text en Copyright © 2022 Liu, Zhang, Ruan, Shen, Xie, Liu, Tang, Zhang, Yang, Hu, Zhang, Liu and Shi https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Liu, Chen-An
Zhang, Qi
Ruan, Guo-Tian
Shen, Liu-Yi
Xie, Hai-Lun
Liu, Tong
Tang, Meng
Zhang, Xi
Yang, Ming
Hu, Chun-Lei
Zhang, Kang-Ping
Liu, Xiao-Yue
Shi, Han-Ping
Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status
title Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status
title_full Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status
title_fullStr Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status
title_full_unstemmed Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status
title_short Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status
title_sort novel diagnostic and prognostic tools for lung cancer cachexia: based on nutritional and inflammatory status
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309732/
https://www.ncbi.nlm.nih.gov/pubmed/35898878
http://dx.doi.org/10.3389/fonc.2022.890745
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