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ObjectiveTo assess the clinical utility of two staging models for bipolar disorder by examining distribution, correlation, and the relationship to external criteria. These are primarily defined by the recurrence of mood episodes (model A), or by intra-episodic functioning (model B).MethodsIn the Dutch Bipolar Cohort, stages according to models A and B were assigned to all patients with bipolar-I-disorder (BD-I; N = 1396). The dispersion of subjects over the stages was assessed and the association between the two models calculated. For both models, change in several clinical markers were concordant with the stage was investigated.ResultsStaging was possible in 87% of subjects for model A and 75% for model B. For model A, 1079 participants (93%) were assigned to stage 3c (recurrent episodes). Subdividing stage 3c with cut-offs at 5 and 10 episodes resulted in subgroups containing 242, 510, and 327 subjects. For model B, most participants were assigned to stage II (intra-episodic symptoms, N = 431 (41%)) or stage III (inability to work, N = 451 (43%)). A low association between models was found. For both models, the clinical markers "age at onset," "treatment resistance," and "episode acceleration" changed concordant with the stages.ConclusionThe majority of patients with BD-I clustered in recurrent stage 3 of Model A. Model B showed a larger dispersion. The stepwise change in several clinical markers supports the construct validity of both models. Combining the two staging models and sub-differentiating the recurrent stage into categories with cut-offs at 5 and 10 lifetime episodes improves the clinical utility of staging for individual patients.

ObjectiveTo assess the clinical utility of two staging models for bipolar disorder by examining distribution, correlation, and the relationship to external criteria. These are primarily defined by the recurrence of mood episodes (model A), or by intra-episodic functioning (model B).MethodsIn the Dutch Bipolar Cohort, stages according to models A and B were assigned to all patients with bipolar-I-disorder (BD-I; N = 1396). The dispersion of subjects over the stages was assessed and the association between the two models calculated. For both models, change in several clinical markers were concordant with the stage was investigated.ResultsStaging was possible in 87% of subjects for model A and 75% for model B. For model A, 1079 participants (93%) were assigned to stage 3c (recurrent episodes). Subdividing stage 3c with cut-offs at 5 and 10 episodes resulted in subgroups containing 242, 510, and 327 subjects. For model B, most participants were assigned to stage II (intra-episodic symptoms, N = 431 (41%)) or stage III (inability to work, N = 451 (43%)). A low association between models was found. For both models, the clinical markers "age at onset," "treatment resistance," and "episode acceleration" changed concordant with the stages.ConclusionThe majority of patients with BD-I clustered in recurrent stage 3 of Model A. Model B showed a larger dispersion. The stepwise change in several clinical markers supports the construct validity of both models. Combining the two staging models and sub-differentiating the recurrent stage into categories with cut-offs at 5 and 10 lifetime episodes improves the clinical utility of staging for individual patients.

Introduction: The manifestation of bipolar disorder (BD) is hypothesized to be determined by clinical characteristics such as familial loading, childhood abuse, age at onset, illness duration, comorbid psychiatric disorders, addiction, treatment resistance, and premorbid cognitive functioning. Which of these are associated with a more severe course and worse outcome is currently unknown. Our objective is to find a combination of clinical characteristics associated with advancement to subsequent stages in two clinical staging models for BD. Methods: Using cross‐sectional data from the Dutch Bipolar Cohort, staging was applied to determine the progression of bipolar‐I‐disorder (BD‐I; N = 1396). Model A is primarily defined by recurrence of mood episodes, ranging from prodromal to chronicity. Model B is defined by level of inter‐episodic functioning, ranging from prodromal to inability to function autonomously. For both models, ordinal logistic regression was conducted to test which clinical characteristics are associated with subsequent stages. Results: For model A, familial loading, childhood abuse, earlier onset, longer illness duration, psychiatric comorbidity, and treatment resistance were all predictors for a higher stage in contrast to addiction and cognitive functioning. For model B, childhood abuse, psychiatric comorbidity, cognitive functioning, and treatment resistance were predictors for a more severe stage, whereas age at onset, illness duration, and addiction were not. Discussion/conclusions: Differences in clinical characteristics across stages support the construct validity of both staging models. Characteristics associated with a higher stage largely overlapped across both models. This study is a first step toward determining different clinical profiles, with a corresponding course and outcome. [ABSTRACT FROM AUTHOR]

Additionally, most cognitive remediation programs for bipolar disorder enroll patients with subjective cognitive complaints. An intervention aimed at improving objective cognitive and social functioning in OABD that includes exercise is difficult as many patients suffer from an additional poor physical condition. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

Objectives: Little is known about the course of late‐life bipolar disorder (LLBD). First, we studied patients with LLBD retrospectively with regard to age at first mood episode, onset polarity, predominant polarity and episode density and its associations with other clinical variables. Next, we examined prospectively the clinical course and its associated factors. Methods: Data were used from a dynamic cohort (Dutch Older Bipolars [DOBi]) including 101 patients with LLBD (mean age of 68.9 years) at baseline in 2012, with 3‐year follow‐up measurements available for 64 of these patients. Retrospective course was assessed by diagnostic interviews, and at follow‐up polarity and duration for each consecutive episode were noted. Linear and logistic analyses were performed to assess associations between relevant factors and outcome. Results: The mean age at the first episode was 33.0 years. Onset polarity was depression in 44.6% of patients, with a predominant polarity of depression in 47.5%. At 3‐year follow‐up, 37.5% of patients reported at least one mood episode, mainly depression. Life events, somatic illness, use of lithium and other factors were not associated with recurrence during the 3‐year follow‐up. Discussion: A relapse rate of 37.5% in 3 years is high, considering that LLBD patients generally have a longer history of disease and were receiving care and medication. The course of LLBD can provide important information on which clinical factors are associated with recurrence. Further phenotyping may reveal unique predictors for outcome, and both course specifiers and clinical variables should be included. [ABSTRACT FROM AUTHOR]

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., (© 2018 The Authors. Bipolar Disorders Published by John Wiley & Sons Ltd.)