Writing Committee for the REMAP-CAP Investigators IMPORTANCE The evidence for benefit of convalescent plasma for critically ill patients with COVID-19 is inconclusive.OBJECTIVE To determine whether convalescent plasma would improve outcomes for critically ill adults with COVID-19. DESIGN, SETTING, AND PARTICIPANTSThe ongoing Randomized, Embedded, Multifactorial, Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) enrolled and randomized 4763 adults with suspected or confirmed COVID-19 between March 9, 2020, and January 18, 2021, within at least 1 domain; 2011 critically ill adults were randomized to open-label interventions in the immunoglobulin domain at 129 sites in 4 countries. Follow-up ended on April 19, 2021. INTERVENTIONSThe immunoglobulin domain randomized participants to receive 2 units of high-titer, ABO-compatible convalescent plasma (total volume of 550 mL ± 150 mL) within 48 hours of randomization (n = 1084) or no convalescent plasma (n = 916). MAIN OUTCOMES AND MEASURESThe primary ordinal end point was organ support-free days (days alive and free of intensive care unit-based organ support) up to day 21 (range, −1 to 21 days; patients who died were assigned -1 day). The primary analysis was an adjusted bayesian cumulative logistic model. Superiority was defined as the posterior probability of an odds ratio (OR) greater than 1 (threshold for trial conclusion of superiority >99%). Futility was defined as the posterior probability of an OR less than 1.2 (threshold for trial conclusion of futility >95%). An OR greater than 1 represented improved survival, more organ support-free days, or both. The prespecified secondary outcomes included in-hospital survival; 28-day survival; 90-day survival; respiratory support-free days; cardiovascular support-free days; progression to invasive mechanical ventilation, extracorporeal mechanical oxygenation, or death; intensive care unit length of stay; hospital length of stay; World Health Organization ordinal scale score at day 14; venous thromboembolic events at 90 days; and serious adverse events. RESULTS Among the 2011 participants who were randomized (median age, 61 [IQR, 52 to 70] years and 645/1998 [32.3%] women), 1990 (99%) completed the trial. The convalescent plasma intervention was stopped after the prespecified criterion for futility was met. The median number of organ support-free days was 0 (IQR, -1 to 16) in the convalescent plasma group and 3 (IQR, -1 to 16) in the no convalescent plasma group. The in-hospital mortality rate was 37.3% (401/1075) for the convalescent plasma group and 38.4% (347/904) for the no convalescent plasma group and the median number of days alive and free of organ support was 14 (IQR, 3 to 18) and 14 (IQR, 7 to 18), respectively. The median-adjusted OR was 0.97 (95% credible interval, 0.83 to 1.15) and the posterior probability of futility (OR <1.2) was 99.4% for the convalescent plasma group compared with the no convalescent plasma group. The treatment effects were consistent across the primary outcome and the 11...
Background SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)–based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients’ radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
IMPORTANCEThe efficacy of antiplatelet therapy in critically ill patients with COVID-19 is uncertain.OBJECTIVE To determine whether antiplatelet therapy improves outcomes for critically ill adults with COVID-19. DESIGN, SETTING, AND PARTICIPANTSIn an ongoing adaptive platform trial (REMAP-CAP) testing multiple interventions within multiple therapeutic domains, 1557 critically ill adult patients with COVID-19 were enrolled between October 30, 2020, and June 23, 2021, from 105 sites in 8 countries and followed up for 90 days (final follow-up date: July 26, 2021).INTERVENTIONS Patients were randomized to receive either open-label aspirin (n = 565), a P2Y12 inhibitor (n = 455), or no antiplatelet therapy (control; n = 529). Interventions were continued in the hospital for a maximum of 14 days and were in addition to anticoagulation thromboprophylaxis. MAIN OUTCOMES AND MEASURESThe primary end point was organ support-free days (days alive and free of intensive care unit-based respiratory or cardiovascular organ support) within 21 days, ranging from −1 for any death in hospital (censored at 90 days) to 22 for survivors with no organ support. There were 13 secondary outcomes, including survival to discharge and major bleeding to 14 days. The primary analysis was a bayesian cumulative logistic model. An odds ratio (OR) greater than 1 represented improved survival, more organ support-free days, or both. Efficacy was defined as greater than 99% posterior probability of an OR greater than 1. Futility was defined as greater than 95% posterior probability of an OR less than 1.2 vs control. Intervention equivalence was defined as greater than 90% probability that the OR (compared with each other) was between 1/1.2 and 1.2 for 2 noncontrol interventions. RESULTSThe aspirin and P2Y12 inhibitor groups met the predefined criteria for equivalence at an adaptive analysis and were statistically pooled for further analysis. Enrollment was discontinued after the prespecified criterion for futility was met for the pooled antiplatelet group compared with control. Among the 1557 critically ill patients randomized, 8 patients withdrew consent and 1549 completed the trial (median age, 57 years; 521 [33.6%] female). The median for organ support-free days was 7 (IQR, −1 to 16) in both the antiplatelet and control groups (median-adjusted OR, 1.02 [95% credible interval {CrI}, 0.86-1.23]; 95.7% posterior probability of futility). The proportions of patients surviving to hospital discharge were 71.5% (723/1011) and 67.9% (354/521) in the antiplatelet and control groups, respectively (median-adjusted OR, 1.27 [95% CrI, 0.99-1.62]; adjusted absolute difference, 5% [95% CrI, −0.2% to 9.5%]; 97% posterior probability of efficacy). Among survivors, the median for organ support-free days was 14 in both groups. Major bleeding occurred in 2.1% and 0.4% of patients in the antiplatelet and control groups (adjusted OR, 2.97 [95% CrI,; adjusted absolute risk increase, 0.8% [95% CrI, 0.1%-2.7%]; 99.4% probability of harm).CONCLUSIONS AND RELEVANCE Among crit...
Owing to its extraordinary lifespan and wide geographical distribution along the continental margins of the North Atlantic Ocean, the ocean quahog Arctica islandica may become an important indicator species in environmental change research. To test for applicability and ''calibrate'' the Arctica-indicator, metabolic properties of A. islandica specimens were compared across different climatic and oceanographic regions. Fully saline populations from Iceland to the North Sea as well as animals from polyhaline and low salinity, environments, the White Sea and the Baltic were included in the study. This calibration centrally includes recordings of growth-age relationships in different populations. Shells were used as age recorders by counting annual growth bands. As a result of this study, we propose a general respiration model that links individual metabolic rates of A. islandica from five populations: Norwegian coast, Kattegat, Kiel Bay (Baltic Sea), White Sea and German Bight (North Sea), to body mass, water temperature and site. Temperature exerts distinct site specific effects on respiration rate, which is indicated by Q 10 values ranging from 4.48 for German Bight to 1.15 for Kiel Bay animals. Individual age, occurrence of apneal respiratory gaps, parasite infestation and salinity do not affect respiration rate. Respiration of Arctica islandica is significantly below the average of 59 bivalve species when compared at the same temperature and animal mass. This respiration model principally enables the coupling of A. islandica life history and population dynamics to regional oceanographic temperature models.
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