Current medical research and opinion
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Meta Analysis
Relative performance of brivaracetam as adjunctive treatment of focal seizures in adults: a network meta-analysis.
Objective: To estimate the relative efficacy, safety and tolerability of adjunctive brivaracetam and other antiepileptic drugs (AEDs) using a Bayesian network meta-analysis (NMA) approach. Methods: A systematic literature review (SLR) identified randomized controlled trials of AEDs treating focal (partial-onset) seizures for ≥8 weeks and assessed them for inclusion in the NMA. Bayesian random-effects NMA was performed for several outcomes. ⋯ Conclusions: This NMA would appear to show relative equivalence in efficacy, safety and tolerability outcomes of the included AEDs. However, patient heterogeneity within trials and in clinical practice should be considered when interpreting these results. While NMAs are based on the best available evidence the authors suggest that, due to the inability of NMAs to capture unmeasured confounding factors and population heterogeneity, NMAs must not be the sole basis for comparative treatment recommendations.
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Objective: To investigate the coexistence effect of hypertension and angiotensin II on the risk of coronary heart disease based on a prospective study in an Inner Mongolian population of China. Methods: The participants were categorized into four subgroups according to hypertension status and median of angiotensin II level. Incident coronary heart disease (CHD) was defined as study outcome. ⋯ Furthermore, compared to normotensives with angiotensin II ≤49 pg/mL, the multivariate-adjusted HRs (95% CIs) of CHD for normotensives with angiotensin II >49 pg/mL, hypertensives with angiotensin II ≤49 pg/mL and hypertensives with angiotensin II >49 pg/mL were 1.33 (0.60-2.91), 2.35 (1.16-4.76) and 3.00 (1.52-5.92), respectively (p for trend <.05). The hypertensives with angiotensin II >49 pg/mL were at the highest risk of CHD. Conclusions: Hypertension not angiotensin II was an independent risk factor for incident CHD, but the coexistence of both hypertension and high angiotensin II level further increased risk of incident CHD among the Inner Mongolians.
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Objective: Describe the development of a claims-based classifier utilizing machine learning to identify patients with probable Lennox-Gastaut syndrome (LGS) from six state Medicaid programs. Methods: Patients were included if they had ≥2 medical claims ≥30 days apart for specified or unspecified epilepsy, excluding those with ≥1 claim for petit mal status. The LGS classifier utilized a random forest algorithm, a compilation of thousands of binary decision trees in which machine-generated predictor variables split the data set into branches that predict the presence or absence of LGS. ⋯ The random forest methodology outperformed logistic regression and single tree methodology. Most of the important LGS predictor characteristics identified by the classifier were statistically significantly associated with LGS status (p < .05). Conclusions: The claims-based LGS classifier showed high sensitivity and specificity, outperformed single tree and logistic regression methodologies and identified a prevalence of probable LGS that was similar to previously published estimates.