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Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

INTRODUCTION: Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. METHODS: Prospective registry and study data were analysed using Cox...

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Autores principales: Alexander, Marliese, Wolfe, Rory, Ball, David, Conron, Matthew, Stirling, Robert G, Solomon, Benjamin, MacManus, Michael, Officer, Ann, Karnam, Sameer, Burbury, Kate, Evans, Sue M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572183/
https://www.ncbi.nlm.nih.gov/pubmed/28728168
http://dx.doi.org/10.1038/bjc.2017.232
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author Alexander, Marliese
Wolfe, Rory
Ball, David
Conron, Matthew
Stirling, Robert G
Solomon, Benjamin
MacManus, Michael
Officer, Ann
Karnam, Sameer
Burbury, Kate
Evans, Sue M
author_facet Alexander, Marliese
Wolfe, Rory
Ball, David
Conron, Matthew
Stirling, Robert G
Solomon, Benjamin
MacManus, Michael
Officer, Ann
Karnam, Sameer
Burbury, Kate
Evans, Sue M
author_sort Alexander, Marliese
collection PubMed
description INTRODUCTION: Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. METHODS: Prospective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell’s c-statistic for discrimination and Cox–Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284). RESULTS: The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score⩽9); 61, 61, and 42% (LCPI 2: score 10–13); 33, 32, and 14% (LCPI 3: score 14–16); 7, 16, and 5% (LCPI 4: score ⩾15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. CONCLUSIONS: The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses.
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spelling pubmed-55721832018-08-22 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer Alexander, Marliese Wolfe, Rory Ball, David Conron, Matthew Stirling, Robert G Solomon, Benjamin MacManus, Michael Officer, Ann Karnam, Sameer Burbury, Kate Evans, Sue M Br J Cancer Epidemiology INTRODUCTION: Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. METHODS: Prospective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell’s c-statistic for discrimination and Cox–Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284). RESULTS: The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score⩽9); 61, 61, and 42% (LCPI 2: score 10–13); 33, 32, and 14% (LCPI 3: score 14–16); 7, 16, and 5% (LCPI 4: score ⩾15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. CONCLUSIONS: The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. Nature Publishing Group 2017-08-22 2017-07-20 /pmc/articles/PMC5572183/ /pubmed/28728168 http://dx.doi.org/10.1038/bjc.2017.232 Text en Copyright © 2017 Cancer Research UK http://creativecommons.org/licenses/by-nc-sa/4.0/ From twelve months after its original publication, this work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
spellingShingle Epidemiology
Alexander, Marliese
Wolfe, Rory
Ball, David
Conron, Matthew
Stirling, Robert G
Solomon, Benjamin
MacManus, Michael
Officer, Ann
Karnam, Sameer
Burbury, Kate
Evans, Sue M
Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
title Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
title_full Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
title_fullStr Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
title_full_unstemmed Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
title_short Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
title_sort lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572183/
https://www.ncbi.nlm.nih.gov/pubmed/28728168
http://dx.doi.org/10.1038/bjc.2017.232
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