Effects of remdesivir in patients hospitalised with COVID-19 a systematic review and individual patient data meta- analysis of randomised controlled trials

Background Interpretation of the evidence from randomised controlled trials (RCTs) of remdesivir in patients treated in hospital for COVID-19 is conflicting. We aimed to assess the benefits and harms of remdesivir compared with placebo or usual care in these patients, and whether treatment effects differed between prespecified patient subgroups. Methods For this systematic review and meta-analysis, we searched PubMed, Embase, the Cochrane COVID-19 trial registry, ClinicalTrials.gov, the International Clinical Trials Registry Platform, and preprint servers from Jan 1, 2020, until April 11, 2022, for RCTs of remdesivir in adult patients hospitalised with COVID-19, and contacted the authors of eligible trials to request individual patient data. The primary outcome was all-cause mortality at day 28 after randomisation. We used multivariable hierarchical regression-adjusting for respiratory support, age, and enrollment period-to investigate effect modifiers. This study was registered with PROSPERO, CRD42021257134. Findings Our search identified 857 records, yielding nine RCTs eligible for inclusion. Of these nine eligible RCTs, individual data were provided for eight, covering 10 480 patients hospitalised with COVID-19 (99% of such patients included in such RCTs worldwide) recruited between Feb 6, 2020, and April 1, 2021. Within 28 days of randomisation, 662 (12 center dot 5%) of 5317 patients assigned to remdesivir and 706 (14 center dot 1%) of 5005 patients assigned to no remdesivir died (adjusted odds ratio [aOR] 0 center dot 88, 95% CI 0 center dot 78-1 center dot 00, p=0 center dot 045). We found evidence for a credible subgroup effect according to respiratory support at baseline (pinteraction=0 center dot 019). Of patients who were ventilated-including those who received high-flow oxygen-253 (30 center dot 0%) of 844 patients assigned to remdesivir died compared with 241 (28 center dot 5%) of 846 patients assigned to no remdesivir (aOR 1 center dot 10 [0 center dot 88-1 center dot 38]; low-certainty evidence). Of patients who received no oxygen or low-flow oxygen, 409 (9 center dot 1%) of 4473 patients assigned to remdesivir died compared with 465 (11 center dot 2%) of 4159 patients assigned to no remdesivir (0 center dot 80 [0 center dot 70-0 center dot 93]; high-certainty evidence). No credible subgroup effect was found for time to start of remdesivir after symptom onset, age, presence of comorbidities, enrolment period, or corticosteroid use. Remdesivir did not increase the frequency of severe or serious adverse events. Interpretation This individual patient data meta-analysis showed that remdesivir reduced mortality in patients hospitalised with COVID-19 who required no or conventional oxygen support, but was underpowered to evaluate patients who were ventilated when receiving remdesivir. The effect size of remdesivir in patients with more respiratory support or acquired immunity and the cost-effectiveness of remdesivir remain to be further elucidated.Peer reviewe

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare