Testing a clinical staging model for bipolar disorder using longitudinal life chart data. (12th December 2018)

• Record Type: Journal Article
• Authors: van der Markt, Afra; Klumpers, Ursula MH; Draisma, Stasja; Dols, Annemiek; Nolen, Willem A; Post, Robert M; Altshuler, Lori L; Frye, Mark A; Grunze, Heinz; Keck, Paul E; McElroy, Susan L; Suppes, Trisha; Beekman, Aartjan TF; Kupka, Ralph W
• Is Part Of: Bipolar disorders. Volume 21:Number 3(2019)
• Page Start: 228
• Page End: 234
• Publication Date: 2018-12-12
• Subjects: biphasic onset -- bipolar disorder -- male sex -- mood disorders -- multi‐state model -- staging -- staging models; Manic-depressive illness -- Periodicals; Depression, Mental -- Periodicals; 616.895
• DOI: 10.1111/bdi.12727
• Languages: English
• ISSNs: 1398-5647

Abstract

Abstract : Objective: Bipolar disorder has a wide range of clinical manifestations which may progress over time. The aim of this study was to test the applicability of a clinical staging model for bipolar disorder and to gain insight into the nature of the variables influencing progression through consecutive stages. Methods: Using retrospectively reported longitudinal life chart data of 99 subjects from the Stanley Foundation Bipolar Network Naturalistic Follow‐up Study, the occurrence, duration and timely sequence of stages 2‐4 were determined per month. A multi‐state model was used to calculate progression rates and identify determinants of illness progression. Stages 0, 1 and several other variables were added to the multi‐state model to determine their influence on the progression rates. Results: Five years after onset of BD (stage 2), 72% reached stage 3 (recurrent episodes) and 21% had reached stage 4 (continuous episodes), of whom 8% recovered back to stage 3. The progression from stage 2 to 3 was increased by a biphasic onset for both the depression‐mania and the mania‐depression course and by male sex. Conclusions: Staging is a useful model to determine illness progression in longitudinal life chart data. Variables influencing transition rates were successfully identified.