Contribution of Low CD4 Cell Counts and High Human Immunodeficiency Virus (HIV) Viral Load to the Efficacy of Preferred First-Line Antiretroviral Regimens for Treating HIV Infection: A Systematic Review and Meta-Analysis.

We assessed whether low CD4 count and high viral load (VL) affect the response to currently preferred ART. We performed a systematic review of randomized, controlled clinical trials that analyzed preferred first-line ART and a subgroup analysis by CD4 count (≤ or >200 CD4/µL) or VL (≤ or >100 000 copies/mL). We computed the odds ratio (OR) of treatment failure (TF) for each subgroup and individual treatment arm. Patients with ≤200 CD4 cells or VL ≥100 000 copies/mL showed an increased likelihood of TF at 48 weeks: OR, 1.94; 95% confidence interval (CI): 1.45-2.61 and OR, 1.75; 95% CI: 1.30-2.35, respectively. A similar increase in the risk of TF was observed at 96 weeks. There was no significant heterogeneity regarding integrase strand transfer inhibitor or nucleoside reverse transcriptase inhibitor backbone. Our results show that CD4 <200 cells/µL and VL ≥100,000 copies/mL impair ART efficacy in all preferred regimens.

Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models.

BACKGROUND: There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. OBJECTIVE: We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. DATA SOURCES: We searched Medline and EMBASE databases from inception to June 2020. STUDY ELIGIBILITY CRITERIA: We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. METHODS: We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. RESULTS: We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76-0.82), calibration slope 0.98 (95% CI 0.86-1.13) and calibration-in-the-large -0.05 (95% CI -0.20 to 0.11). CONCLUSIONS: The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE. PROTOCOL REGISTRATION: PROSPERO (registration number CRD42020192602).

Impact of Biological Agents on Imaging and Biomarkers of Cardiovascular Disease in Patients with Psoriasis: A Systematic Review and Meta-Analysis of Randomized Placebo-Controlled Trials.

BACKGROUND: The effect of biologics on the risk for cardiovascular disease in patients with psoriasis is still unclear despite their widespread use. OBJECTIVE: The objective of this study was to examine the impact of licensed biological therapies on imaging and biomarkers of cardiovascular disease risk in patients with psoriasis by a systematic review and meta-analysis of placebo-controlled trials. METHODS: A comprehensive search of studies published before 1 June 2020 was performed in Medline-Ovid, EMBASE, and CENTRAL using a predefined strategy to identify relevant articles. RESULTS: Five studies were included for the final examination, and two studies were included in the meta-analysis. We did not find a significant reduction in aortic vascular inflammation in patients treated with adalimumab compared with those who received placebo at weeks 12-16. There was no beneficial effect on imaging biomarkers (aortic vascular inflammation or flow-mediated dilatation) of cardiovascular disease risk in patients exposed to biological therapies (adalimumab and secukinumab) compared with those exposed to placebo, except for ustekinumab showing a reduction in aortic vascular inflammation at week 12 but not at week 52 after the open-label extension period. The strongest reduction in blood-based cardiometabolic risk biomarkers was observed with adalimumab (CRP, TNF-α, IL-6, and GlycA) and phototherapy (CRP and IL-6) compared with that observed with placebo. CONCLUSIONS: Randomized controlled trials show that ustekinumab reduces aortic vascular inflammation and that TNF-α inhibitors and phototherapy reduce CRP and IL-6. These surrogate marker findings require randomized controlled trials evaluating cardiovascular events to inform clinical practice.