Bipolar Disorders最新文献

Introduction: Long-acting injectable antipsychotics (LAIs) are not routinely offered to patients living with bipolar disorder type I (BP-I), despite widespread evidence that supports their benefits over oral antipsychotics, particularly in early disease.

Methods: A round-table meeting of psychiatrists convened to discuss barriers and opportunities and provide consensus recommendations around the early use of LAIs for BP-I.

Results: LAIs are rarely prescribed to treat BP-I unless a patient has severe symptoms, sub-optimal adherence to oral antipsychotics, or has experienced multiple relapses. Beyond country-specific accessibility issues (e.g., healthcare infrastructure and availability/approval status), primary barriers to the effective use of LAIs were identified as attitudinal and knowledge/experience-based. Direct discussions between healthcare providers and patients about treatment preferences may not occur due to a preconceived notion that patients prefer oral antipsychotics. Moreover, as LAIs have historically been limited to the treatment of schizophrenia and the most severe cases of BP-I, healthcare providers might be unaware of the benefits LAIs provide in the overall management of BP-I. Improved treatment adherence associated with LAIs compared to oral antipsychotics may support improved outcomes for patients (e.g., reduced relapse and hospitalization). Involvement of all stakeholders (healthcare providers, patients, and their supporters) participating in the patient journey is critical in early and shared decision-making processes. Clinical and database studies could potentially bridge knowledge gaps to facilitate acceptance of LAIs.

Conclusion: This review discusses the benefits of LAIs in the management of BP-I and identifies barriers to use, while providing expert consensus recommendations for potential solutions to support informed treatment decision-making.

Objectives: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]).

Methods: Supervised machine learning models were trained on data from BP-D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission.

Results: An AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine-treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week-4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week-4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45-5.76; p < 9.3E-11).

Conclusion: Machine learning strategies achieved cross-trial and cross-drug replication in predicting remission after 8 weeks of pharmacotherapy for BP-D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.

Introduction: The use of antidepressants in bipolar disorder (BD) remains contentious, in part due to the risk of antidepressant-induced mania (AIM). However, there is no information on the architecture of mood regulation in patients who have experienced AIM. We compared the architecture of mood regulation in euthymic patients with and without a history of AIM.

Methods: Eighty-four euthymic participants were included. Participants rated their mood, anxiety and energy levels daily using an electronic (e-) visual analog scale, for a mean (SD) of 280.8(151.4) days. We analyzed their multivariate time series by computing each variable's auto-correlation, inter-variable cross-correlation, and composite multiscale entropy of mood, anxiety, and energy. Then, we compared the data features of participants with a history of AIM and those without AIM, using analysis of covariance, controlling for age, sex, and current treatment.

Results: Based on 18,103 daily observations, participants with AIM showed significantly stronger day-to-day auto-correlation and cross-correlation for mood, anxiety, and energy than those without AIM. The highest cross-correlation in participants with AIM was between mood and energy within the same day (median (IQR), 0.58 (0.27)). The strongest negative cross-correlation in participants with AIM was between mood and anxiety series within the same day (median (IQR), -0.52 (0.34)).

Conclusion: Patients with a history of AIM have a different underlying mood architecture compared to those without AIM. Their mood, anxiety and energy stay the same from day-to-day; and their anxiety is negatively correlated with their mood.