Weight gained during treatment predicts 6‐month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning.
Objective: This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program. Methods: Of 368 individuals with anorexia nervosa (209 adolescents and 159 adults), 160 individuals had data...
| Published in: | International Journal of Eating Disorders Vol. 57; no. 8; pp. 1653 - 1668 |
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| Main Authors: | , , , , |
| Format: | Article |
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Wiley-Blackwell
Aug2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=179238601&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 179238601 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02763478 IJD jtl: International Journal of Eating Disorders issn: 02763478 maglogo: Y pubinfo: dt: Aug2024 vid: 57 iid: 8 pid: 480 pub: Wiley-Blackwell artinfo: ui: 179238601 10.1002/eat.24208 ppf: 1653 ppct: 15 formats: tig: atl: Weight gained during treatment predicts 6‐month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning. aug: au: Frank, Guido K. W. Stoddard, Joel J. Brown, Tiffany Gowin, Josh Kaye, Walter H. affil: Department of Psychiatry, University of California San Diego, San Diego California,, USA Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora Colorado,, USA Department of Psychological Sciences, Auburn University, Auburn Alabama,, USA Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora Colorado,, USA su: Patients Hospital admission & discharge Hospital care Anorexia nervosa treatment Random forest algorithms Body mass index Prediction models Research funding Treatment effectiveness Descriptive statistics Discharge planning Longitudinal method Comparative studies Length of stay in hospitals Weight gain sug: subj: Patients Hospital admission & discharge Hospital care Anorexia nervosa treatment Random forest algorithms Body mass index Prediction models Research funding Treatment effectiveness Descriptive statistics Discharge planning Longitudinal method Comparative studies Length of stay in hospitals Weight gain keyword: anorexia nervosa body mass index machine learning weight anorexia nervosa body mass index machine learning weight ab: Objective: This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program. Methods: Of 368 individuals with anorexia nervosa (209 adolescents and 159 adults), 160 individuals had data available for a 6‐month follow‐up analysis. Participants were treated in a 6‐day‐per‐week partial‐hospital program. Participants were assessed for eating disorder‐specific and non‐specific psychopathology. The analyses used established machine learning procedures combined in an ensemble model from support vector machine learning, random forest prediction, and the elastic net regularized regression with an exploration (training; 75%) and confirmation (test; 25%) split of the data. Results: The models predicting body mass index (BMI) at 6‐month follow‐up explained a 28.6% variance in the training set (n = 120). The model had good performance in predicting 6‐month BMI in the test dataset (n = 40), with predicted BMI significantly correlating with actual BMI (r =.51, p = 0.01). The change in BMI from admission to discharge was the most important predictor, strongly correlating with reported BMI at 6‐month follow‐up (r =.55). Behavioral variables were much less predictive of BMI outcome. Results were similar for z‐transformed BMI in the adolescent‐only group. Length of stay was most predictive of weight gain in treatment (r =.56) but did not predict longer‐term BMI. Conclusions: This study, using an agnostic ensemble machine learning approach in the largest to‐date sample of individuals with anorexia nervosa, suggests that achieving weight gain goals in treatment predicts longer‐term weight‐related outcomes. Other potential predictors, personality, mood, or eating disorder‐specific symptoms were relatively much less predictive. Public Significance: The results from this study indicate that the amount of weight gained during treatment predicts BMI 6 months after discharge from a high level of care. This suggests that patients require sufficient time in a higher level of care treatment to meet their specific weight goals and be able to maintain normal weight. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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