Angiotensin converting enzyme inhibitors and angiotensin II receptor blockers and outcomes in hospitalized patients with COVID-19: an updated systematic review and meta-analysis of randomized clinical trials.

BACKGROUND: Our prior analysis demonstrated no significant difference in risk of mortality or disease progression among patients with COVID-19. With the availability of findings from randomized controlled trials (RCTs), we provide an updated review of RCTs which explored the outcomes among hospitalized patients with COVID-19 treated with Angiotensin Converting Enzyme inhibitor (ACEis)/Angiotensin Receptor Blockers (ARBs) versus control. RESEARCH DESIGN AND METHODS: This systematic review and meta-analysis covers RCTs exploring mortality, intensive care unit admission, and mechanical ventilation outcomes among hospitalized COVID-19 patients treated with ACEi/ARBs. RESULTS: Ten studies were included in this meta-analysis. For mortality with ACEi/ARB utilization among hospitalized COVID-19 patients, the pooled risk ratio (RR) was 0.97 (95% CI 0.64-1.47, p = 0.89) with heterogeneity of 26%. Further, the pooled RR for ACEi/ARB use on ICU admission and mechanical ventilation were 0.55 (0.55-1.08, p = 0.13) with a heterogeneity of 0% and 1.02 (0.78-1.32, p = 0.91) with a heterogeneity of 0%, respectively. CONCLUSION: Among hospitalized patients with COVID-19, the use of ACEi/ARB was not associated with increased risk of mortality, ICU admission, or mechanical ventilation compared to control. These findings support continuation of ACEi/ARB for whom baseline clinical indications for these agents exist.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Silver lining of COVID-19: Heightened global interest in pneumococcal and influenza vaccines, an infodemiology study.

BACKGROUND: Health-seeking behaviors change during pandemics and may increase with regard to illnesses with symptoms similar to the pandemic. The global reaction to COVID-19 may drive interest in vaccines for other diseases. OBJECTIVES: Our study investigated the correlation between global online interest in COVID-19 and interest in CDC-recommended routine vaccines. DESIGN, SETTINGS, MEASUREMENTS: This infodemiology study used Google Trends data to quantify worldwide interest in COVID-19 and CDC-recommended vaccines using the unit search volume index (SVI), which estimates volume of online search activity relative to highest volume of searches within a specified period. SVIs from December 30, 2019 to March 30, 2020 were collected for "coronavirus (Virus)" and compared with SVIs of search terms related to CDC-recommended adult vaccines. To account for seasonal variation, we compared SVIs from December 30, 2019 to March 30, 2020 with SVIs from the same months in 2015 to 2019. We performed country-level analyses in ten COVID-19 hotspots and ten countries with low disease burden. RESULTS: There were significant positive correlations between SVIs for "coronavirus (Virus)" and search terms for pneumococcal (R = 0.89, p < 0.0001) and influenza vaccines (R = 0.93, p < 0.0001) in 2020, which were greater than SVIs for the same terms in 2015-2019 (p = 0.005, p < 0.0001, respectively). Eight in ten COVID-19 hotspots demonstrated significant positive correlations between SVIs for coronavirus and search terms for pneumococcal and influenza vaccines. LIMITATIONS: SVIs estimate relative changes in online interest and do not represent the interest of people with no Internet access. CONCLUSION: A peak in worldwide interest in pneumococcal and influenza vaccines coincided with the COVID-19 pandemic in February and March 2020. Trends are likely not seasonal in origin and may be driven by COVID-19 hotspots. Global events may change public perception about the importance of vaccines. Our findings may herald higher demand for pneumonia and influenza vaccines in the upcoming season.