Latent trajectories of eating disorder treatment response among female patients in residential care

Abstract

Objective: Eating disorder (ED) treatment outcomes are highly variable from beginning to end of treatment; however, little is known about differential trajectories during the course of treatment. This study sought to characterize heterogeneous patterns of ED treatment response during residential care. Method: Participants were adolescent girls and adult women (N = 360) receiving residential ED treatment for anorexia nervosa, bulimia nervosa, binge-eating disorder, other specified feeding or eating disorder, unspecified feeding or eating disorder, or avoidant/ restrictive food intake disorder. Self-report symptom assessments were completed at admission, discharge, and approximately weekly throughout the residential stay to assess curvilinear patterns of change. Latent growth mixture modeling was applied to identify subgroups of patients with similar treatment response trajectories. Results: Three latent groups emerged, including gradual response (58.3%; steady improvements from admission to discharge), rapid response (23.9%; steep early improvements that were maintained through discharge), and low-symptom static response (17.8%; nearly nonclinical self-reported symptoms at admission that remained static through discharge). Groups differed on important clinical characteristics, such as body mass index, endorsement of compensatory behaviors, severity of global ED psychopathology at admission, and degree of symptom improvement by end of treatment. Discussion: Patients follow heterogeneous response patterns in residential ED treatment, and these patterns are associated with differential treatment outcome. Future work should explore whether these trajectories are associated with differential outcomes at follow-up and whether tailoring clinical intervention to a patient's trajectory type can improve treatment response. K E Y W O R D S feeding and eating disorders, outcome and process assessment, residential treatment 1 | INTRODUCTION Eating disorders (EDs) are among the most medically compromising psychiatric illnesses (Harris & Barraclough, 1998; Smink, van Hoeken, & Hoek, 2013) and are characterized by substantial heterogeneity in symptoms (Dechartres et al., 2011; Forbush & Wildes, 2016). When patients are grouped into empirically derived latent groups according to their symptom profiles, group membership

Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach

“…At these facilities, data are collected from all patients as part of a routine assessment battery for internal quality assurance. The assessment protocol consists of a longer self‐report assessment battery at admission and discharge and a brief progress assessment administered weekly throughout treatment (see Espel‐Huynh, Zhang, et al, 2020, for more detail).…”

“…The data used here were drawn from the dataset used for trajectory analysis in Espel‐Huynh, Zhang, et al (2020). Data were gathered from all consenting patients admitted on or after February 1, 2016, and discharged before June 29, 2017.…”

“…We selected one primary machine learning approach—support vector machine (SVM)—based upon its prior success with trajectory prediction in PTSD (Galatzer‐Levy et al, 2014) and its potential clinical utility for the patient population of interest. SVM performs especially well when separating groups with overlapping distributions (Guyon, Weston, Barnhill, & Vapnik, 2002; James et al, 2013), which were present in the latent trajectory data (Espel‐Huynh, Zhang, et al, 2020). Importantly, the use of complex predictive methods should only be used when simpler approaches are insufficient.…”

“…Our group has examined latent trajectories of symptom change during treatment in a transdiagnostic sample of patients receiving residential care for EDs (Espel‐Huynh, Zhang, et al, 2020). In this case, symptoms were measured via a self‐report progress monitoring assessment (the Progress Monitoring Tool for EDs; PMED).…”

“…The PMED is tailored for the unique aspects of structured, 24‐h treatment provided in residential care for EDs and assesses cognitive and behavioral symptoms aggregated across five domains: Weight and Shape Concern, ED Urges and Behaviors, Emotion Avoidance, Adaptive Coping (reverse‐scored), and Relational Connection (reverse‐scored). In our group's latent trajectory analysis involving N = 360 patients who completed the assessment on a weekly basis throughout treatment, a three‐class trajectory model emerged: “Gradual Response” (58%), characterized by high‐severity symptoms at admission that improved in a linear fashion through discharge; “Rapid early Response” (24%), with steep, rapid improvements early on in treatment that were maintained through discharge; and “Low‐Symptom static response” (18%), characterized by nearly nonclinical symptoms reported at admission that remained stable throughout treatment (Espel‐Huynh, Zhang, et al, 2020). Some have discussed the limitations of categorizing patients into discrete groups, given that treatment response may ultimately occur on a continuum of varying patterns (Bauer, 2007; Sher, Jackson, & Steinley, 2011).…”