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1.
Eur Neuropsychopharmacol ; 78: 81-92, 2024 Jan.
Article in English | MEDLINE | ID: mdl-37775363

ABSTRACT

This meta-analysis investigated the efficacy, safety, and tolerability of lamotrigine versus placebo in preventing relapse and recurrence of mood episodes in women of childbearing age with bipolar I disorder. Following up to 16 weeks' open-label lamotrigine treatment, responders were randomized to double-blind treatment, including lamotrigine 100-400 mg/day or placebo, in four trials of up to 76 weeks. Women aged 18-45 years who received ≥ 1 dose of study treatment and had ≥ 1 efficacy assessment in the double-blind phase were pooled for efficacy analysis. The primary outcome was median time to intervention for any mood episode (TIME). Of 717 eligible women in the open-label phase, 287 responded and were randomized to lamotrigine (n = 153) or placebo (n = 134). The randomized group had a mean (SD) of 2.0(2.02) manic and 2.5(2.02) depressive episodes in the 3 years before screening. Median TIME was 323 days with lamotrigine and 127 days with placebo (HR 0.69; 95% CI 0.49, 0.96; p = 0.030). Lamotrigine delayed time to intervention for any depressive episode (HR 0.59; 95% CI 0.39, 0.90; p = 0.014) with no treatment difference for manic episodes (HR 0.91; 95% CI 0.52, 1.58; p = 0.732). 2/717 (< 1%) participants experienced serious rash-related adverse events (AEs) during the open-label phase, and 52/717 (7%) had non-serious rash-related events leading to study withdrawal. Incidence of AEs and AEs leading to withdrawal were similar between lamotrigine and placebo groups. Lamotrigine delayed relapse and recurrence of mood episodes, largely by preventing depressive episodes, and was well tolerated in women of childbearing age.


Subject(s)
Bipolar Disorder , Exanthema , Humans , Female , Lamotrigine/adverse effects , Bipolar Disorder/diagnosis , Triazines/adverse effects , Anticonvulsants/therapeutic use , Mania/chemically induced , Mania/drug therapy , Double-Blind Method , Recurrence , Exanthema/chemically induced , Exanthema/drug therapy , Treatment Outcome , Randomized Controlled Trials as Topic
2.
Article in English | MEDLINE | ID: mdl-37794168

ABSTRACT

BACKGROUND: Modeling studies using large datasets from men with lower urinary tract symptoms/benign prostate enlargement (LUTS/BPE) can predict changes in International Prostate Symptom Score (IPSS) and risk of acute urinary retention/surgery under different treatment regimens and according to predictors (baseline characteristics) that commonly define risk of progression. We assessed the impact of treatments on different symptom types (storage, voiding, and nocturia), quality of life (QoL; IPSS Q8), and BPH Impact Index [BII]). METHODS: Generalized least squares models were used to predict each outcome. Data from the CombAT study were used to predict outcomes for active treatments (dutasteride, tamsulosin, combination therapy). Predictors included: age; IPSS total, storage, voiding, nocturia and QoL (IPSS Q8) scores; BII; prostate volume; maximum urine flow rate (Qmax), prostate-specific antigen, postvoid residual urine (PVR); alpha-blocker usage within 12 months. Data from phase III dutasteride monotherapy studies were used to predict placebo outcomes. Results were visualized using an interactive web-based tool ( www.bphtool.com ). RESULTS: Combination therapy provided greater predicted benefit than either monotherapy for all five outcomes for most patient profiles within the CombAT inclusion criteria. PVR and corresponding subscores were significant predictors of change in both storage and voiding subscores. Alpha-blocker use within 12 months, age (storage subscore), and Qmax (voiding subscore) were also significant predictors. PVR, age, Qmax, and nocturia score were significant predictors of change in nocturia. PVR, Qmax, previous alpha-blocker use, total IPSS, and QoL (IPSS Q8) score were significant predictors of change in QoL (IPSS Q8) score. For BII, significant predictors were PVR, age, total IPSS, and BII score. The multivariable effect of covariates and treatments is best visualized through the interactive web-based tool. CONCLUSIONS: This predictive modeling study informs our understanding of how risk factors for disease progression interact and affect treatment impact on different symptom types and QoL scores.

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