IMPORTANCEIn patients who require mechanical ventilation for acute hypoxemic respiratory failure, further reduction in tidal volumes, compared with conventional low tidal volume ventilation, may improve outcomes. OBJECTIVE To determine whether lower tidal volume mechanical ventilation using extracorporeal carbon dioxide removal improves outcomes in patients with acute hypoxemic respiratory failure. DESIGN, SETTING, AND PARTICIPANTS This multicenter, randomized, allocation-concealed, open-label, pragmatic clinical trial enrolled 412 adult patients receiving mechanical ventilation for acute hypoxemic respiratory failure, of a planned sample size of 1120, between May 2016 and December 2019 from 51 intensive care units in the UK. Follow-up ended on March 11, 2020. INTERVENTIONS Participants were randomized to receive lower tidal volume ventilation facilitated by extracorporeal carbon dioxide removal for at least 48 hours (n = 202) or standard care with conventional low tidal volume ventilation (n = 210). MAIN OUTCOMES AND MEASURESThe primary outcome was all-cause mortality 90 days after randomization. Prespecified secondary outcomes included ventilator-free days at day 28 and adverse event rates. RESULTS Among 412 patients who were randomized (mean age, 59 years; 143 [35%] women), 405 (98%) completed the trial. The trial was stopped early because of futility and feasibility following recommendations from the data monitoring and ethics committee. The 90-day mortality rate was 41.5% in the lower tidal volume ventilation with extracorporeal carbon dioxide removal group vs 39.5% in the standard care group (risk ratio, 1.05 [95% CI, 0.83-1.33]; difference, 2.0% [95% CI, −7.6% to 11.5%]; P = .68). There were significantly fewer mean ventilator-free days in the extracorporeal carbon dioxide removal group compared with the standard care group (7.1 [95% CI, 5.9-8.3] vs 9.2 [95% CI, 7.9-10.4] days; mean difference, −2.1 [95% CI, −3.8 to −0.3]; P = .02). Serious adverse events were reported for 62 patients (31%) in the extracorporeal carbon dioxide removal group and 18 (9%) in the standard care group, including intracranial hemorrhage in 9 patients (4.5%) vs 0 (0%) and bleeding at other sites in 6 (3.0%) vs 1 (0.5%) in the extracorporeal carbon dioxide removal group vs the control group. Overall, 21 patients experienced 22 serious adverse events related to the study device.CONCLUSIONS AND RELEVANCE Among patients with acute hypoxemic respiratory failure, the use of extracorporeal carbon dioxide removal to facilitate lower tidal volume mechanical ventilation, compared with conventional low tidal volume mechanical ventilation, did not significantly reduce 90-day mortality. However, due to early termination, the study may have been underpowered to detect a clinically important difference.

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...

T here has been considerable recent interest in the dynamic vehicle routing problem, but the complexities of this problem class have generally restricted research to myopic models. In this paper, we address the simpler dynamic assignment problem, where a resource (container, vehicle, or driver) can serve only one task at a time. We propose a very general class of dynamic assignment models, and propose an adaptive, nonmyopic algorithm that involves iteratively solving sequences of assignment problems no larger than what would be required of a myopic model. We consider problems where the attribute space of future resources and tasks is small enough to be enumerated, and propose a hierarchical aggregation strategy for problems where the attribute spaces are too large to be enumerated. Finally, we use the formulation to also test the value of advance information, which offers a more realistic estimate over studies that use purely myopic models. The problem of dynamically assigning resources to tasks over time arises in a number of applications in transportation. In the field of freight transportation, truckload motor carriers, railroads, and shipping companies all have to manage fleets of containers (trucks, boxcars, and intermodal containers) that move one load at a time, with orders arriving continuously over time. In the passenger arena, taxi companies and companies that manage fleets of business jets have to assign vehicles (taxicabs or jets) to move customers from one location to the next. It is common to assume that the arrival of customer demands is random (e.g., known only through a probability distribution) over time, but it may also be the case that the vehicles become available in a random way. Finally, each assignment of a resource to a task generates a contribution to profits, which may also be random.We refer to the problem of dynamically assigning resources to tasks as a dynamic assignment problem. In general, it may be possible to assign a resource to a sequence of two or more tasks at the same time, but we focus on problems where we assign a resource to one task at a time. We assume that resources and tasks are each characterized by a set of possibly unique attributes, where the contribution generated by an assignment will depend on the attributes of the resource and task. Resources do not have to be used and tasks do not all have to be covered, although there can be a cost for holding either one.The dynamic assignment problem is a fundamental problem in routing and scheduling. It is a special case of the dynamic vehicle routing problem, without the complexities of in-vehicle consolidation. For this reason, it provides a natural framework for modeling the dynamic information processes and comparing myopic models with those that exploit distributional information about the future. It is common practice, for example, to model dynamic vehicle routing problems using myopic models, which ignore any forecasts of the future based on currently available data. These problems are themselves quite difficult be...

To study the efficacy of lopinavir-ritonavir and hydroxychloroquine in critically ill patients with coronavirus disease 2019 .Methods: Critically ill adults with COVID-19 were randomized to receive lopinavir-ritonavir, hydroxychloroquine, combination therapy of lopinavir-ritonavir and hydroxychloroquine or no antiviral therapy (control). The primary endpoint was an ordinal scale of organ support-free days. Analyses used a Bayesian cumulative logistic model and expressed treatment effects as an adjusted odds ratio (OR) where an OR > 1 is favorable. Results:We randomized 694 patients to receive lopinavir-ritonavir (n = 255), hydroxychloroquine (n = 50), combination therapy (n = 27) or control (n = 362). The median organ support-free days among patients in lopinavir-ritonavir, hydroxychloroquine, and combination therapy groups was 4 (-1 to 15), 0 (-1 to 9) and-1 (-1 to 7), respectively,

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).