Micronutrients in HIV: A Bayesian MetaAnalysis

Background: Approximately 28.5 million people living with HIV are eligible for treatment (CD4&500), but currently have no access to antiretroviral therapy. Reduced serum level of micronutrients is common in HIV disease. Micronutrient supplementation (MNS) may mitigate disease progression and mortality. Objectives: We synthesized evidence on the effect of micronutrient supplementation on mortality and rate of disease progression in HIV disease. Methods: We searched MEDLINE, EMBASE, the Cochrane Central, AMED and CINAHL databases through December 2014, without language restriction, for studies of greater than 3 micronutrients versus any or no comparator. We built a hierarchical Bayesian random effects model to synthesize results. Inferences are based on the posterior distribution of the population effects; posterior distributions were approximated by Markov chain Monte Carlo in OpenBugs. Principal Findings: From 2166 initial references, we selected 49 studies for full review and identified eight reporting on disease progression and/or mortality. Bayesian synthesis of data from 2,249 adults in three studies estimated the relative risk of disease progression in subjects on MNS vs. control as 0.62 (95% credible interval, 0.37, 0.96). Median number needed to treat is 8.4 (4.8, 29.9) and the Bayes Factor 53.4. Based on data reporting on 4,095 adults reporting mortality in 7 randomized controlled studies, the RR was 0.84 (0.38, 1.85), NNT is 25 (4.3, ∞). Conclusions: MNS significantly and substantially slows disease progression in HIV+ adults not on ARV, and possibly reduces mortality. Micronutrient supplements are effective in reducing progression with a posterior probability of 97.9%. Considering MNS low cost and lack of adverse effects, MNS should be standard of care for HIV+ adults not yet on ARV

Correction: Micronutrients in HIV: A Bayesian Meta-Analysis.

[This corrects the article DOI: 10.1371/journal.pone.0120113.]

Abstract W P421: Trend in Concurrent Hospitalization for Moyamoya and Sickle Cell Disease: An Analysis of Nationwide Inpatient Sample Data

BACKGROUND: The association between Moyamoya disease and sickle cell disease is well recognized in the literature. However, there is limited data on inpatient admission of concurrent sickle cell disease and Moyamoya disease. We sought to determine the trend in incidence of admissions of concurrent Moyamoya and sickle cell disease as well as the most common presentation of these admissions. METHODS: We reviewed the Healthcare Cost and Utilization Project's Nationwide Inpatient Sample (NIS) database from 2000-2011 for concurrent Moyamoya and sickle cell admissions using the ICD 9-CM codes We obtained data on gender, clinical presentation, procedures, co-morbidities and patient outcomes RESULTS: From 2000 to 2011, an estimated patients 756 (weighted (n)=3692) with co-existing Moyamoya disease and sickle cell disease were admitted. The incidence of admission for concurrent disease increased significantly from 0.04/100,000 admissions in 2000 to 0.21/100,000 admissions in 2011(figure 1). This was very significant using the Cochrane Armitage trend test(p&lt;0.001). The most likely reasons for admissions were ischemic stroke(7.2)% followed by hemorrhagic stroke(2.8 and transient ischemic attack(1.2%) (p = 0.0116). The most commonly performed treatment procedures included packed cell transfusion(33.7%) followed by exchange transfusion (8.8%). However there was no significant change in mortality from 2000-2011. CONCLUSION: The number of hospitalizations due to concurrent Moyamoya and sickle cell disease has increased significantly over the last decade and are likely to present with a cerebrovascular accident. Thus, it is important to maintain a high degree of suspicion for Moyamoya disease in sickle cell disease patients presenting with neurological symptoms since this could potentially impact their management. </jats:p

Summary of Data Extraction.

<p>Summary of Data Extraction.</p

Flow Chart of Selection Process of Reviewed Studies.

<p>Flow Chart of Selection Process of Reviewed Studies.</p

Density Strip Plot of Relative Risk of Progression to Clinical Disease/AIDS.

<p>Bayesian random effects analysis (favors treatment on the left of 1.0); density plots on the figure on the left represent combined data; on the right accounts for individual study arms and the impact of adding selenium or vitamin A to MNS [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120113#pone.0120113.ref055" target="_blank">55</a>].</p

Density Strip Plot of Effect of MNS on Mortality.

<p>Bayesian random effects analysis (favors treatment on the left of 1.0); density plots on the figure on the left represent combined data; on the right accounts for individual study arms and the impact of adding selenium, vitamin A or zinc to MNS.</p

Abstract 19854: A National Perspective of Weekend- Effect on Mortality and Adverse Outcomes in Patients with Intracerebral Hemorrhage

Introduction: Management of intracerebral hemorrhage (ICH) requires urgent diagnostic and therapeutic procedures, which may not be uniformly available throughout the week. We attempt to define a "weekend effect" for ICH, which has not yet been fully established in this patient population. Hypothesis: We aimed to evaluate whether outcomes differ with respect to the day of admission in patients admitted with ICH. Methods: We reviewed the Healthcare Cost and Utilization Project’s Nationwide Inpatient Sample (NIS) database from 2000 to 2011 for ICH using ICD 9-CM codes. NIS represents 20% of all US hospital pts and weighted numbers represent national estimates.We defined primary outcome as mortality and adverse outcome(composite of in-hospital mortality &amp; discharge other than home). We utilized chi-square test for univariable analysis for categorical variables and generated hierarchical multilevel regression models to determine independent predictors of mortality and adverse outcome. Results: We included 161017 patients (weighted n=788641) with ICH, out of which 42996(weighted n= 210592) were admitted on weekend. After adjusting for confounders (demographics, Deyo’s modification of charlson’s co-morbidity index, admission type (elective or emergent), hospital region, hospital teaching status, hospital ICH volume and primary payer), the weekend admissions were still associated with 10 % higher mortality (OR 1.10, 95% CI 1.07-1.16, P=0.001) and 20% higher adverse outcome (OR 1.12, 95% CI 1.09-1.16, p=0.001). Conclusions: Thus, admission for ICH on the weekend was a significant and independent predictor of increased in hospital mortality and adverse outcomes as compared to weekday admission. The reasons for this are likely manifold and warrant further investigation both from a quantitative and qualitative standpoint. </jats:p