IDENTIFYING PANIC DISORDER SUBTYPES USING FACTOR MIXTURE MODELING

The clinical presentation of panic disorder (PD) is known to be highly heterogeneous, complicating research on its etiology, neurobiological pathways, and treatment. None of the attempts to identify PD subtypes have been independently reproduced, rendering the current literature inconclusive.

Using a data-driven, case-centered approach (factor mixture modeling) on a broad range of anxiety symptoms assessed with the Beck anxiety inventory, the present study identifies PD disorder subtypes in a large (n = 658), well-documented mixed-population sample from the Netherlands Study of Depression and Anxiety (NESDA), with subtypes being validated and detailed using a variety of clinical characteristics.

A three-class, one-factor model proved superior to all other possible models (Bayesian information criterion = 13,200; Lo-Mendel-Rubin = 0.0295; bootstrapped likelihood ratio test ≤ 0.0001), with the first class, a cognitive-autonomic subtype, accounting for 29.8%, the second class, the autonomic subtype, for 29.9%, and a third class, the aspecific subtype, for 40.3% of the population. The cognitive-autonomic and autonomic subtypes showed significant differences compared to the aspecific subtype (e.g., comorbidity and suicide attempts) but on severity differed between themselves only.

Three qualitatively different PD subtypes were identified: a severe cognitive-autonomic subtype, a moderate autonomic subtype, and a mild aspecific subtype. Qualitative and quantitative differences were related to severity and clinical properties such as comorbidity, suicide attempts, sleep, and sense of mastery.