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Comparison of seven comorbidity scores on four-month survival of lung cancer patients
BACKGROUND: The comorbidity burden has a negative impact on lung-cancer survival. Several comorbidity scores have been described and are currently used. The current challenge is to select the comorbidity score that best reflects their impact on survival. Here, we compared seven usable comorbidity sc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623755/ https://www.ncbi.nlm.nih.gov/pubmed/37923993 http://dx.doi.org/10.1186/s12874-023-01994-6 |
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author | Pluchart, Hélène Bailly, Sébastien Chanoine, Sébastien Moro-Sibilot, Denis Bedouch, Pierrick Toffart, Anne-Claire |
author_facet | Pluchart, Hélène Bailly, Sébastien Chanoine, Sébastien Moro-Sibilot, Denis Bedouch, Pierrick Toffart, Anne-Claire |
author_sort | Pluchart, Hélène |
collection | PubMed |
description | BACKGROUND: The comorbidity burden has a negative impact on lung-cancer survival. Several comorbidity scores have been described and are currently used. The current challenge is to select the comorbidity score that best reflects their impact on survival. Here, we compared seven usable comorbidity scores (Charlson Comorbidity Index, Age adjusted Charlson Comorbidity Index, Charlson Comorbidity Index adapted to lung cancer, National Cancer Institute combined index, National Cancer Institute combined index adapted to lung cancer, Elixhauser score, and Elixhauser adapted to lung cancer) with coded administrative data according to the tenth revision of the International Statistical Classification of Diseases and Related Health Problems to select the best prognostic index for predicting four-month survival. MATERIALS AND METHODS: This cohort included every patient with a diagnosis of lung cancer hospitalized for the first time in the thoracic oncology unit of our institution between 2011 and 2015. The seven scores were calculated and used in a Cox regression method to model their association with four-month survival. Then, parameters to compare the relative goodness-of-fit among different models (Akaike Information Criteria, Bayesian Information Criteria), and discrimination parameters (the C-statistic and Harrell’s c-statistic) were calculated. A sensitivity analysis of these parameters was finally performed using a bootstrap method based on 1,000 samples. RESULTS: In total, 633 patients were included. Male sex, histological type, metastatic status, CCI, CCI-lung, Elixhauser score, and Elixhauser-lung were associated with poorer four-month survival. The Elixhauser score had the lowest AIC and BIC and the highest c-statistic and Harrell’s c-statistic. These results were confirmed in the sensitivity analysis, in which these discrimination parameters for the Elixhauser score were significantly different from the other scores. CONCLUSIONS: Based on this cohort, the Elixhauser score is the best prognostic comorbidity score for predicting four-month survival for hospitalized lung cancer patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01994-6. |
format | Online Article Text |
id | pubmed-10623755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106237552023-11-04 Comparison of seven comorbidity scores on four-month survival of lung cancer patients Pluchart, Hélène Bailly, Sébastien Chanoine, Sébastien Moro-Sibilot, Denis Bedouch, Pierrick Toffart, Anne-Claire BMC Med Res Methodol Research BACKGROUND: The comorbidity burden has a negative impact on lung-cancer survival. Several comorbidity scores have been described and are currently used. The current challenge is to select the comorbidity score that best reflects their impact on survival. Here, we compared seven usable comorbidity scores (Charlson Comorbidity Index, Age adjusted Charlson Comorbidity Index, Charlson Comorbidity Index adapted to lung cancer, National Cancer Institute combined index, National Cancer Institute combined index adapted to lung cancer, Elixhauser score, and Elixhauser adapted to lung cancer) with coded administrative data according to the tenth revision of the International Statistical Classification of Diseases and Related Health Problems to select the best prognostic index for predicting four-month survival. MATERIALS AND METHODS: This cohort included every patient with a diagnosis of lung cancer hospitalized for the first time in the thoracic oncology unit of our institution between 2011 and 2015. The seven scores were calculated and used in a Cox regression method to model their association with four-month survival. Then, parameters to compare the relative goodness-of-fit among different models (Akaike Information Criteria, Bayesian Information Criteria), and discrimination parameters (the C-statistic and Harrell’s c-statistic) were calculated. A sensitivity analysis of these parameters was finally performed using a bootstrap method based on 1,000 samples. RESULTS: In total, 633 patients were included. Male sex, histological type, metastatic status, CCI, CCI-lung, Elixhauser score, and Elixhauser-lung were associated with poorer four-month survival. The Elixhauser score had the lowest AIC and BIC and the highest c-statistic and Harrell’s c-statistic. These results were confirmed in the sensitivity analysis, in which these discrimination parameters for the Elixhauser score were significantly different from the other scores. CONCLUSIONS: Based on this cohort, the Elixhauser score is the best prognostic comorbidity score for predicting four-month survival for hospitalized lung cancer patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01994-6. BioMed Central 2023-11-03 /pmc/articles/PMC10623755/ /pubmed/37923993 http://dx.doi.org/10.1186/s12874-023-01994-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Pluchart, Hélène Bailly, Sébastien Chanoine, Sébastien Moro-Sibilot, Denis Bedouch, Pierrick Toffart, Anne-Claire Comparison of seven comorbidity scores on four-month survival of lung cancer patients |
title | Comparison of seven comorbidity scores on four-month survival of lung cancer patients |
title_full | Comparison of seven comorbidity scores on four-month survival of lung cancer patients |
title_fullStr | Comparison of seven comorbidity scores on four-month survival of lung cancer patients |
title_full_unstemmed | Comparison of seven comorbidity scores on four-month survival of lung cancer patients |
title_short | Comparison of seven comorbidity scores on four-month survival of lung cancer patients |
title_sort | comparison of seven comorbidity scores on four-month survival of lung cancer patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623755/ https://www.ncbi.nlm.nih.gov/pubmed/37923993 http://dx.doi.org/10.1186/s12874-023-01994-6 |
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