DSM-5 specifies bulimia nervosa (BN) severity based on specific thresholds of compensatory behavior frequency. There is limited empirical support for such severity groupings. Limited support could be because the DSM-5’s compensatory behavior frequency cutpoints are inaccurate or because compensatory behavior frequency does not capture true underlying differences in severity. In support of the latter possibility, some work has suggested shape/weight overvaluation or use of single versus multiple purging methods may be better severity indicators. We used structural equation modeling (SEM) Trees to empirically determine the ideal variables and cutpoints for differentiating BN severity, and compared the SEM Tree groupings to alternate severity classifiers: the DSM-5 indicators, single versus multiple purging methods, and a binary indicator of shape/weight overvaluation.

Treatment-seeking adolescents and adults with BN (N = 1017) completed self-report measures assessing BN and comorbid symptoms. SEM Trees specified an outcome model of BN severity and recursively partitioned this model into subgroups based on shape/weight overvaluation and compensatory behaviors. We then compared groups on clinical characteristics (eating disorder symptoms, depression, anxiety, and binge eating frequency).

SEM Tree analyses resulted in five severity subgroups, all based on shape/weight overvaluation: overvaluation <1.25; overvaluation 1.25–3.74; overvaluation 3.75–4.74; overvaluation 4.75–5.74; and overvaluation ≥5.75. SEM Tree groups explained 1.63–6.41 times the variance explained by other severity schemes.

Shape/weight overvaluation outperformed the DSM-5 severity scheme and single versus multiple purging methods, suggesting the DSM-5 severity scheme should be reevaluated. Future research should examine the predictive utility of this severity scheme.