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- Sun, Sun, et al.
(author)
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Mapping the obesity problems scale to the SF-6D : results based on the Scandinavian Obesity Surgery Registry (SOReg)
- 2023
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In: European Journal of Health Economics. - : Springer. - 1618-7598 .- 1618-7601. ; 24:2, s. 279-292
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Journal article (peer-reviewed)abstract
- BACKGROUND: Obesity Problem Scale (OP) is a widely applied instrument for obesity, however currently calculation of health utility based on OP is not feasible as it is not a preference-based measure. Using data from the Scandinavian Obesity Surgery Registry (SOReg), we sought to develop a mapping algorithm to estimate SF-6D utility from OP. Furthermore, to test whether the mapping algorithm is robust to the effect of surgery.METHOD: The source data SOReg (n = 36 706) contains both OP and SF-36, collected at pre-surgery and at 1, 2 and 5 years post-surgery. The Ordinary Least Square (OLS), beta-regression and Tobit regression were used to predict the SF-6D utility for different time points respectively. Besides the main effect model, different combinations of patient characteristics (age, sex, Body Mass Index, obesity-related comorbidities) were tested. Both internal validation (split-sample validation) and validation with testing the mapping algorithm on a dataset from other time points were carried out. A multi-stage model selection process was used, accessing model consistency, parsimony, goodness-of-fit and predictive accuracy. Models with the best performance were selected as the final mapping algorithms.RESULTS: The final mapping algorithms were based on OP summary score using OLS models, for pre- and post-surgery respectively. Mapping algorithms with different combinations of patients' characteristics were presented, to satisfy the user with a different need.CONCLUSION: This study makes available algorithms enabling crosswalk from the Obesity Problem Scale to the SF-6D utility. Different mapping algorithms are recommended for the mapping of pre- and post-operative data.
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| 2. |
- Sun, Sun, et al.
(author)
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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
- 2023
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In: Obesity Surgery. - : Springer. - 0960-8923 .- 1708-0428. ; 33:8, s. 2452-2462
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Journal article (peer-reviewed)abstract
- 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 R2 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.
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