Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

OBJECTIVE: Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. METHODS: Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. RESULTS: Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). DISCUSSION: Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. PUBLIC SIGNIFICANCE: There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

Are central eating disorder network symptoms sensitive to item selection and sample? Implications for conceptualization of eating disorder psychopathology from a network perspective.

Item selection is a critical decision in modeling psychological networks. The current preregistered two-study research used random selections of 1,000 symptom networks to examine which eating disorder (ED) and co-occurring symptoms are most central in longitudinal networks among individuals with EDs (N = 71, total observations = 6,060) and tested whether centrality changed based on which items were included in the network. Participants completed 2 weeks of ecological momentary assessment (five surveys/day). In Study 1, we obtained initial strength centrality values by estimating an a priori network using eight items with the highest means. We then estimated 1,000 networks and their centrality from a random selection of unique eight-item symptom combinations. We compared the strength centrality from the a priori network to the distribution of strength centrality estimates from the random-item networks. In Study 2, we repeated this procedure in an independent longitudinal dataset (N = 41, total observations = 4,575) to determine if our results generalized across samples. Shame, guilt, worry, and fear of losing control were consistently central across networks, regardless of items included in the network or sample. Results suggest that these symptoms may be important to the structure of ED psychopathology and have implications for how we understand the structure of ED psychopathology. Existing methods for item inclusion in psychological networks may distort the structure of ED symptom networks by either under- or overestimating strength centrality, or by omitting consistently central symptoms that are nontraditional ED symptoms. Future research should consider including these symptoms in models of ED psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personalizing eating disorder treatment using idiographic models: An open series trial.

OBJECTIVE: Treatments for adults with eating disorders (EDs) only work in about 50% of individuals, and for some diagnoses (e.g., anorexia nervosa; atypical anorexia nervosa), there are no existing evidence-based treatments. Part of the reason that treatments may only work in a subset of individuals is because of the high heterogeneity present in the EDs, even within diagnoses. Manualized treatments delivered in a standard format may not always address the most relevant symptoms for a specific individual. METHOD: The current open series trial recruited participants with transdiagnostic ED diagnoses (N = 79) to investigate the feasibility, acceptability, and initial clinical efficacy of a 10-session network-informed personalized treatment for eating disorders. This treatment uses idiographic (i.e., one-person) network models of ecological momentary assessment symptom data to match participants to evidence-based modules of treatment. RESULTS: We found that network-informed personalized treatment was highly feasible with low dropout rates, was rated as highly acceptable, and had strong initial clinical efficacy. ED severity decreased from pre- to posttreatment and at 1-year follow-up with a large effect size. ED cognitions, behaviors, clinical impairment, worry, and depression also decreased from pre- to posttreatment. CONCLUSIONS: These data suggest that network-informed personalized treatment has high acceptability and feasibility and can decrease ED and related pathology, possibly serving as a feasible alternative to existing treatments. Future randomized controlled trials comparing network-informed personalized treatment for ED to existing gold standard treatments are needed. Additionally, more research is needed on this type of personalized treatment both in the EDs, as well as in additional forms of psychopathology, such as depression. (PsycInfo Database Record (c) 2023 APA, all rights reserved).