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Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register
PURPOSE: To investigate whether the quality-adjusted life years (QALYs) of the patients who underwent bariatric surgery could be predicted using their baseline information. MATERIALS AND METHODS: All patients who received bariatric surgery in Sweden between January 1, 2011 and March 31, 2019 were ob...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10345068/ https://www.ncbi.nlm.nih.gov/pubmed/37322243 http://dx.doi.org/10.1007/s11695-023-06685-1 |
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author | Sun, Sun Stenberg, Erik Lindholm, Lars Salén, Klas-Göran Franklin, Karl A. Luo, Nan Cao, Yang |
author_facet | Sun, Sun Stenberg, Erik Lindholm, Lars Salén, Klas-Göran Franklin, Karl A. Luo, Nan Cao, Yang |
author_sort | Sun, Sun |
collection | PubMed |
description | PURPOSE: To investigate whether the quality-adjusted life years (QALYs) of the patients who underwent bariatric surgery could be predicted using their baseline information. MATERIALS AND METHODS: All patients who received bariatric surgery in Sweden between January 1, 2011 and March 31, 2019 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Baseline information included patients’ sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. QALYs were assessed by the SF-6D at follow-up years 1 and 2. The general and regularized linear regression models were used to predict postoperative QALYs. RESULTS: All regression models demonstrated satisfactory and comparable performance in predicting QALYs at follow-up year 1, with R(2) and relative root mean squared error (RRMSE) values of about 0.57 and 9.6%, respectively. The performance of the general linear regression model increased with the number of variables; however, the improvement was ignorable when the number of variables was more than 30 and 50 for follow-up years 1 and 2, respectively. Although minor L1 and L2 regularization provided better prediction ability, the improvement was negligible when the number of variables was more than 20. All the models showed poorer performance for predicting QALYs at follow-up year 2. CONCLUSIONS: Patient characteristics before bariatric surgery including health related quality of life, age, sex, BMI, postoperative complications within six weeks, and smoking status, may be adequate in predicting their postoperative QALYs after one year. Understanding these factors can help identify individuals who require more personalized and intensive support before, during, and after surgery. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11695-023-06685-1. |
format | Online Article Text |
id | pubmed-10345068 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-103450682023-07-15 Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register Sun, Sun Stenberg, Erik Lindholm, Lars Salén, Klas-Göran Franklin, Karl A. Luo, Nan Cao, Yang Obes Surg Original Contributions PURPOSE: To investigate whether the quality-adjusted life years (QALYs) of the patients who underwent bariatric surgery could be predicted using their baseline information. MATERIALS AND METHODS: All patients who received bariatric surgery in Sweden between January 1, 2011 and March 31, 2019 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Baseline information included patients’ sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. QALYs were assessed by the SF-6D at follow-up years 1 and 2. The general and regularized linear regression models were used to predict postoperative QALYs. RESULTS: All regression models demonstrated satisfactory and comparable performance in predicting QALYs at follow-up year 1, with R(2) and relative root mean squared error (RRMSE) values of about 0.57 and 9.6%, respectively. The performance of the general linear regression model increased with the number of variables; however, the improvement was ignorable when the number of variables was more than 30 and 50 for follow-up years 1 and 2, respectively. Although minor L1 and L2 regularization provided better prediction ability, the improvement was negligible when the number of variables was more than 20. All the models showed poorer performance for predicting QALYs at follow-up year 2. CONCLUSIONS: Patient characteristics before bariatric surgery including health related quality of life, age, sex, BMI, postoperative complications within six weeks, and smoking status, may be adequate in predicting their postoperative QALYs after one year. Understanding these factors can help identify individuals who require more personalized and intensive support before, during, and after surgery. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11695-023-06685-1. Springer US 2023-06-15 2023 /pmc/articles/PMC10345068/ /pubmed/37322243 http://dx.doi.org/10.1007/s11695-023-06685-1 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/) . |
spellingShingle | Original Contributions Sun, Sun Stenberg, Erik Lindholm, Lars Salén, Klas-Göran Franklin, Karl A. Luo, Nan Cao, Yang Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register |
title | Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register |
title_full | Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register |
title_fullStr | Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register |
title_full_unstemmed | Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register |
title_short | Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register |
title_sort | prediction of quality-adjusted life years (qalys) after bariatric surgery using regularized linear regression models: results from a swedish nationwide quality register |
topic | Original Contributions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10345068/ https://www.ncbi.nlm.nih.gov/pubmed/37322243 http://dx.doi.org/10.1007/s11695-023-06685-1 |
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