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Predicting the Risk of Weight Loss After Esophageal Cancer Surgery
BACKGROUND: Malnutrition after esophageal cancer surgery is associated with reduced health-related qualify of life. Therefore, a prediction model identifying patients at risk for severe weight loss after surgery was developed. METHODS: Data from a Swedish population-based cohort study, including 616...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611892/ https://www.ncbi.nlm.nih.gov/pubmed/31004297 http://dx.doi.org/10.1245/s10434-019-07352-5 |
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author | Schandl, Anna Kauppila, Joonas H. Anandavadivelan, Poorna Johar, Asif Lagergren, Pernilla |
author_facet | Schandl, Anna Kauppila, Joonas H. Anandavadivelan, Poorna Johar, Asif Lagergren, Pernilla |
author_sort | Schandl, Anna |
collection | PubMed |
description | BACKGROUND: Malnutrition after esophageal cancer surgery is associated with reduced health-related qualify of life. Therefore, a prediction model identifying patients at risk for severe weight loss after surgery was developed. METHODS: Data from a Swedish population-based cohort study, including 616 patients undergoing esophageal cancer surgery in 2001–2005, was used. Candidate predictors included risk factors available before and immediately after surgery. Severe weight loss was defined as ≥ 15% loss of body weight between the time of surgery and 6 months postoperatively. The prediction model was developed using multivariable models. The accuracy of the model was measured by the area under the receiver operating characteristics curve (AUC) with bootstrap validation. The model was externally validated in a hospital-based cohort of 91 surgically treated esophageal cancer patients in the United Kingdom in 2011–2016. Each predictor in the final model was assigned a corresponding risk score. The sum of risk scores was equivalent to an estimated probability for severe weight loss. RESULTS: Among the 351 patients with 6 months follow-up data, 125 (36%) suffered from severe postoperative weight loss. The final prediction model included body mass index at diagnosis, preoperative weight loss, and neoadjuvant therapy. The AUC for the model was 0.78 (95% CI 0.74–0.83). In the validation cohort, the AUC was 0.76. A clinical risk assessment guide was derived from the prediction model. CONCLUSIONS: This prediction model can preoperatively identify individuals with high risk of severe weight loss after esophageal cancer surgery. Intensive nutritional interventions for these patients are recommended. |
format | Online Article Text |
id | pubmed-6611892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-66118922019-07-23 Predicting the Risk of Weight Loss After Esophageal Cancer Surgery Schandl, Anna Kauppila, Joonas H. Anandavadivelan, Poorna Johar, Asif Lagergren, Pernilla Ann Surg Oncol Thoracic Oncology BACKGROUND: Malnutrition after esophageal cancer surgery is associated with reduced health-related qualify of life. Therefore, a prediction model identifying patients at risk for severe weight loss after surgery was developed. METHODS: Data from a Swedish population-based cohort study, including 616 patients undergoing esophageal cancer surgery in 2001–2005, was used. Candidate predictors included risk factors available before and immediately after surgery. Severe weight loss was defined as ≥ 15% loss of body weight between the time of surgery and 6 months postoperatively. The prediction model was developed using multivariable models. The accuracy of the model was measured by the area under the receiver operating characteristics curve (AUC) with bootstrap validation. The model was externally validated in a hospital-based cohort of 91 surgically treated esophageal cancer patients in the United Kingdom in 2011–2016. Each predictor in the final model was assigned a corresponding risk score. The sum of risk scores was equivalent to an estimated probability for severe weight loss. RESULTS: Among the 351 patients with 6 months follow-up data, 125 (36%) suffered from severe postoperative weight loss. The final prediction model included body mass index at diagnosis, preoperative weight loss, and neoadjuvant therapy. The AUC for the model was 0.78 (95% CI 0.74–0.83). In the validation cohort, the AUC was 0.76. A clinical risk assessment guide was derived from the prediction model. CONCLUSIONS: This prediction model can preoperatively identify individuals with high risk of severe weight loss after esophageal cancer surgery. Intensive nutritional interventions for these patients are recommended. Springer International Publishing 2019-04-19 2019 /pmc/articles/PMC6611892/ /pubmed/31004297 http://dx.doi.org/10.1245/s10434-019-07352-5 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Thoracic Oncology Schandl, Anna Kauppila, Joonas H. Anandavadivelan, Poorna Johar, Asif Lagergren, Pernilla Predicting the Risk of Weight Loss After Esophageal Cancer Surgery |
title | Predicting the Risk of Weight Loss After Esophageal Cancer Surgery |
title_full | Predicting the Risk of Weight Loss After Esophageal Cancer Surgery |
title_fullStr | Predicting the Risk of Weight Loss After Esophageal Cancer Surgery |
title_full_unstemmed | Predicting the Risk of Weight Loss After Esophageal Cancer Surgery |
title_short | Predicting the Risk of Weight Loss After Esophageal Cancer Surgery |
title_sort | predicting the risk of weight loss after esophageal cancer surgery |
topic | Thoracic Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611892/ https://www.ncbi.nlm.nih.gov/pubmed/31004297 http://dx.doi.org/10.1245/s10434-019-07352-5 |
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