A machine-learned model for predicting weight loss success using weight change features early in treatment
Abstract Stepped-care obesity treatments aim to improve efficiency by early identification of non-responders and adjusting interventions but lack validated models. We trained a random forest classifier to improve the predictive utility of a clinical decision rule (>0.5 lb weight loss/week) that i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01299-y |
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| _version_ | 1846147403510448128 |
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| author | Farzad Shahabi Samuel L. Battalio Angela Fidler Pfammatter Donald Hedeker Bonnie Spring Nabil Alshurafa |
| author_facet | Farzad Shahabi Samuel L. Battalio Angela Fidler Pfammatter Donald Hedeker Bonnie Spring Nabil Alshurafa |
| author_sort | Farzad Shahabi |
| collection | DOAJ |
| description | Abstract Stepped-care obesity treatments aim to improve efficiency by early identification of non-responders and adjusting interventions but lack validated models. We trained a random forest classifier to improve the predictive utility of a clinical decision rule (>0.5 lb weight loss/week) that identifies non-responders in the first 2 weeks of a stepped-care weight loss trial (SMART). From 2009 to 2021, 1058 individuals with obesity participated in three studies: SMART, Opt-IN, and ENGAGED. The model was trained on 80% of the SMART data (224 participants), and its in-distribution generalizability was tested on the remaining 20% (remaining 57 participants). The out-of-distribution generalizability was tested on the ENGAGED and Opt-IN studies (472 participants). The model predicted weight loss at month 6 with an 84.5% AUROC and an 86.3% AUPRC. SHAP identified predictive features: weight loss at week 2, ranges/means and ranges of weight loss, slope, and age. The SMART-trained model showed generalizable performance with no substantial difference across studies. |
| format | Article |
| id | doaj-art-41e4ba0cb0474a50b6071a40354e6233 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-41e4ba0cb0474a50b6071a40354e62332024-12-01T12:46:08ZengNature Portfolionpj Digital Medicine2398-63522024-11-017111010.1038/s41746-024-01299-yA machine-learned model for predicting weight loss success using weight change features early in treatmentFarzad Shahabi0Samuel L. Battalio1Angela Fidler Pfammatter2Donald Hedeker3Bonnie Spring4Nabil Alshurafa5Department of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityCollege of Education, Health, and Human Sciences, University of TennesseeDepartment of Public Health Sciences, The University of ChicagoDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityAbstract Stepped-care obesity treatments aim to improve efficiency by early identification of non-responders and adjusting interventions but lack validated models. We trained a random forest classifier to improve the predictive utility of a clinical decision rule (>0.5 lb weight loss/week) that identifies non-responders in the first 2 weeks of a stepped-care weight loss trial (SMART). From 2009 to 2021, 1058 individuals with obesity participated in three studies: SMART, Opt-IN, and ENGAGED. The model was trained on 80% of the SMART data (224 participants), and its in-distribution generalizability was tested on the remaining 20% (remaining 57 participants). The out-of-distribution generalizability was tested on the ENGAGED and Opt-IN studies (472 participants). The model predicted weight loss at month 6 with an 84.5% AUROC and an 86.3% AUPRC. SHAP identified predictive features: weight loss at week 2, ranges/means and ranges of weight loss, slope, and age. The SMART-trained model showed generalizable performance with no substantial difference across studies.https://doi.org/10.1038/s41746-024-01299-y |
| spellingShingle | Farzad Shahabi Samuel L. Battalio Angela Fidler Pfammatter Donald Hedeker Bonnie Spring Nabil Alshurafa A machine-learned model for predicting weight loss success using weight change features early in treatment npj Digital Medicine |
| title | A machine-learned model for predicting weight loss success using weight change features early in treatment |
| title_full | A machine-learned model for predicting weight loss success using weight change features early in treatment |
| title_fullStr | A machine-learned model for predicting weight loss success using weight change features early in treatment |
| title_full_unstemmed | A machine-learned model for predicting weight loss success using weight change features early in treatment |
| title_short | A machine-learned model for predicting weight loss success using weight change features early in treatment |
| title_sort | machine learned model for predicting weight loss success using weight change features early in treatment |
| url | https://doi.org/10.1038/s41746-024-01299-y |
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