Development of transdiagnostic clinical risk prediction models for 12-month onset and course of eating disorders among adolescents in the community

Abstract

Objective: To develop and internally validate risk prediction models for adolescent onset and persistence of eating disorders.

Methods: N = 963 Australian adolescents (11-19 years) in the EveryBODY Study cohort completed online surveys in 2018 and 2019. Models were built to predict 12-month risk of (1) onset, and (2) persistence of a DSM-5 eating disorder.

Results: Onset Model. Of the n = 687 adolescents without an eating disorder at baseline, 16.9% were identified with an eating disorder after 12 months. The prediction model was based on evidence-based risk factors for eating disorder onset available within the dataset (sex, body mass index percentile, strict weight loss dieting, history of bullying, psychological distress, weight/shape concerns). This model showed fair discriminative performance (mean AUC = .75). The most important factors were psychological distress, weight and shape concerns, and female sex. Diagnostic Persistence Model. Of the n = 276 adolescents with an eating disorder at baseline, 74.6% were identified as continuing to meet criteria for an eating disorder after 12 months. The prediction model for diagnostic persistence was based on available evidence-based risk factors for eating disorder persistence (purging, distress, social impairment). This model showed poor discriminative performance (mean AUC = .65). The most important factors were psychological distress and self-induced vomiting for weight control.

Discussion: We found preliminary evidence for the utility of a parsimonious model for 12-month onset of an eating disorder among adolescents in the community. Future research should include additional evidence-based risk factors and validate models beyond the original sample.

Public significance: This study demonstrated the feasibility of developing parsimonious and accurate models for the prediction of future onset of an eating disorder among adolescents. The most important predictors in this model included psychological distress and weight and shape concerns. This study has laid the ground work for future research to build and test more accurate prediction models in diverse samples, prior to translation into a clinical tool for use in real world settings to aid decisions about referral to early intervention.

Keywords: eating disorder; illness course; onset; prediction model.

FIGURE 1

Descriptive visualizations of the distribution of cross-validated model performance metrics of elastic net and logistic regression models predicting eating disorders onset at Wave 2 based on Wave 1 data ($N\hspace{0.17em}\text{=}\hspace{0.17em}687$). AUC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve.

FIGURE 2

Descriptive visualizations of the distribution of cross-validated model performance metrics of elastic net and logistic regression models predicting eating disorders persistence at Wave 2 based on Wave 1 data ($N\hspace{0.17em}\text{=}\hspace{0.17em}\text{276}$). AUC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve.