Measurement and Prediction of Treatment Outcome among Residential Eating Disorder Patients Using a Novel Progress Monitoring Measure
Authors:   , Espel-Huynh Hallie Marie , Lowe Michael R. 
Published: July 2021

Eating disorders (EDs) are severe psychiatric illnesses which can lead to substantial psychological impairment and significant medical comorbidity. Relative to those with other psychiatric illnesses, individuals with EDs are particularly prone to treatment nonresponse (e.g., reliable deterioration or inadequate improvement during treatment). Further, very little is known about treatment response patterns in intensive treatment settings such as residential care. Evidence from general outpatient psychotherapy research suggests that risk for treatment nonresponse can be significantly reduced with use of Progress Monitoring and clinician Feedback (PM-FB) systems. PM-FB systems use empirically-derived benchmarking and prediction algorithms to determine a patient's expected course of treatment. When integrated into clinical practice, PM-FB systems significantly improve treatment outcomes and reduce risk of treatment nonresponse; effects are particularly pronounced for patients at risk for deterioration. Given the high rates of treatment nonresponse among patients with EDs, PM-FB may be a particularly effective tool for improving ED treatment outcomes. To date, PM-FB systems have been developed and tested primarily for use in general outpatient therapy settings. Their utility in ED treatment settings is largely unknown, particularly in intensive residential treatment. Development of an ED- and treatment-specific PM-FB system could facilitate increased treatment effectiveness. The present study sought to: (1) develop and validate an ED-specific PM measure for residential treatment (the Progress Monitoring tool for EDs, PMED); (2) identify an expected treatment response curve characterizing typical patterns of weekly symptom change; and (3) develop an algorithm to predict patient treatment outcome using only early treatment data. Thirty-seven PMED items were generated by a team of content experts, clinicians, and administrative staff from the target treatment setting. Items were administered to 531 residential patients with EDs as part of routine intake assessments and subsequently submitted to factor analysis. Results yielded a 5-factor, 26-item structure with adequate psychometric properties. To generate expected treatment response curves, the refined PMED was then administered to 371 patients at admission, discharge, and weekly throughout treatment. Latent growth mixture modeling was applied to characterize the number and nature of possible response patterns in residential care. A three-class trajectory model was the best fit for the data. Patients were classified into one of the three groups based on posterior predicted probabilities associated with their individual treatment response trajectories from admission to discharge. Using only data from the first two assessments, support vector machine learning was applied to predict patients' ultimate response class from early treatment data. Analyses yielded 84% overall prediction accuracy, which was significantly better than chance prediction. Findings suggest that residential ED treatment response is best characterized by three distinct patterns of symptom change. Treatment response patterns in residential ED treatment are heterogeneous in nature and are best represented by three distinct patterns of response. Results from machine learning analyses support the feasibility and accuracy of machine learning methods in predicting ED these response patterns early on in treatment. In the future, data from a patient's active treatment could be entered into this algorithm; information on the likely response pattern would inform therapeutic treatment planning and alert providers of risk for nonresponse.