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This comparative-effectiveness trial assessed clinical outcomes in patients with advanced heart failure who were not candidates for cardiac transplantation and who had a continuous-flow left ventricular assist device as compared with a pulsatile-flow device. The continuous-flow device resulted in better clinical outcomes, but it has not yet been approved by the Food and Drug Administration. This comparative-effectiveness trial assessed clinical outcomes in patients with advanced heart failure who had a continuous-flow left ventricular assist device as compared with a pulsatile-flow device. The continuous-flow device resulted in better clinical outcomes. Medical and electrical therapies for systolic heart failure have improved outcomes and altered the natural history of the disease. 1 – 9 However, heart failure commonly progresses and becomes refractory to current treatments. Continuous intravenous inotropic support may improve clinical status in the short term but results in a survival rate at 1 year of only 10 to 30%. 10 , 11 Cardiac transplantation is available for only a minority of patients, because of a lack of suitable donor hearts. The paucity of effective therapies for advanced heart failure led to the evaluation of mechanical circulatory-support devices as permanent therapy. To date, only two . . .

A series of 133 patients with severe heart failure underwent implantation of a continuous-flow left ventricular assist device. At 6 months, 75% of the patients had undergone heart transplantation, had cardiac recovery with device explantation, or continued to receive device support without any contraindication to subsequent transplantation. Important adverse events included postoperative bleeding, stroke, heart failure, and drive-line infection. A series of 133 patients with severe heart failure underwent implantation of a continuous-flow left ventricular assist device. At 6 months, 75% of the patients had undergone heart transplantation, had cardiac recovery, or continued to receive device support without any contraindication to subsequent transplantation. Therapy with a left ventricular assist device is an established form of treatment for patients with refractory heart failure. 1 In the United States, most patients undergoing implantation of such a device as a bridge to heart transplantation have received support from pulsatile volume-displacement devices that fill with and eject blood in a cyclic fashion that is analogous to the systole and diastole of the native heart. 2 – 7 These devices provide excellent hemodynamic support and improve survival but have substantial constraints, including the need for extensive surgical dissection, the requirement that the recipient have a large body habitus, the presence of . . .

Objective Warfarin is standard anticoagulation therapy for patients with a continuous-flow left ventricular assist device (CF-LVAD). However, warfarin requires regular monitoring and dosage adjustments and fails for many patients, causing thromboembolic and bleeding events. Factor Xa inhibitors have been shown to be noninferior to warfarin in preventing strokes and are associated with less intracranial hemorrhage in patients with atrial fibrillation. We evaluated treatment safety and effectiveness in CF-LVAD patients who switched from warfarin to a factor Xa inhibitor (apixaban or rivaroxaban) after warfarin failure. Methods This was a retrospective, single-center study of patients treated between 2008 and 2018. We assessed the occurrence of stroke, non-central nervous system (CNS) embolism, pump thrombosis, and major gastrointestinal bleeding and intracranial hemorrhage during therapy. Results We identified seven patients: five were male, the average body mass index was 30 kg/m 2 , and average age was 56 years. Preimplantation comorbidities included hypertension (all patients) and diabetes mellitus, ischemic cardiomyopathy, atrial fibrillation, and previous myocardial infarction (four patients each). Overall, patients received warfarin for 3968 days and apixaban/rivaroxaban for 1459 days. The warfarin group was within the therapeutic INR range (2.0–3.0) 30% of the time. Complication rates did not differ between warfarin and apixaban/rivaroxaban: strokes, 0.20 vs none, non-CNS embolism, 0.54 vs none; pump thrombosis, 0.27 vs none; major gastrointestinal bleeding, 0.20 vs 0.50; intracranial hemorrhage, 0.13 vs none. Conclusions Factor Xa inhibitors may be viable treatment options for CF-LVAD patients for whom warfarin therapy has failed. Large prospective studies are necessary to confirm these results.

Continuous-flow left ventricular assist devices (CF-LVADs) are increasingly being used to treat advanced, refractory chronic heart failure. Herein, we sought to determine the incidence of postoperative acute kidney injury (AKI) in axial-flow (HeartMate II; HM-II) and centrifugal-flow (HVAD) CF-LVAD recipients, as well as the effect of AKI on mortality. The study cohort comprised 520 patients who received a HM-II (n = 398) or HVAD (n = 122) at our center between November 2003 and March 2016. Their records were reviewed to determine the incidence of RIFLE-defined AKI after LVAD implantation. We compared the perioperative characteristics, postoperative complications, and survival rates of the patients with and without AKI and differentiated the outcomes based on device type (HM-II or HVAD). Seventy-five patients (14.4%) developed AKI postoperatively. Patients with AKI after LVAD implantation had significantly reduced survival compared to patients without AKI (p = 0.01). Cox proportional hazards models showed that AKI was a significant independent predictor of mortality (HR = 1.54, p = 0.03). Preoperative mechanical circulatory support and prolonged cardiopulmonary bypass time were independent predictors of AKI. The incidence of AKI was similar for HM-II and HVAD recipients (p = 0.25). There was no significant difference in AKI rates for the HM-II and HVAD recipients. Developing AKI adversely affected survival.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown 1 to be highly efficient for discovery of genetic associations 2 . 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 group 3 . 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 ). An analysis of 24,202 critical cases of COVID-19 identifies potentially druggable targets in 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 ).

ObjectiveWe examined the relationship between trimester of SARS-CoV-2 infection, illness severity, and risk for preterm birth.Study designWe analyzed data for 6336 pregnant persons with SARS-CoV-2 infection in 2020 in the United States. Risk ratios for preterm birth were calculated for illness severity, trimester of infection, and illness severity stratified by trimester of infection adjusted for age, selected underlying medical conditions, and pregnancy complications.ResultPregnant persons with critical COVID-19 or asymptomatic infection, compared to mild COVID-19, in the second or third trimester were at increased risk of preterm birth. Pregnant persons with moderate-to-severe COVID-19 did not show increased risk of preterm birth in any trimester.ConclusionCritical COVID-19 in the second or third trimester was associated with increased risk of preterm birth. This finding can be used to guide prevention strategies, including vaccination, and inform clinical practices for pregnant persons.

IntroductionPublic health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Control and Prevention (CDC), in close collaboration with state, local, and territorial health departments, began a 5-year initiative to establish population-based mother–baby linked longitudinal surveillance, the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET).ObjectivesThe objective of this report is to describe an expanded surveillance approach that leverages and modernizes existing surveillance systems to address the impact of emerging health threats during pregnancy on pregnant women and their infants.MethodsMother–baby pairs are identified through prospective identification during pregnancy and/or identification of an infant with retrospective linking to maternal information. All data are obtained from existing data sources (e.g., electronic medical records, vital statistics, laboratory reports, and health department investigations and case reporting).ResultsVariables were selected for inclusion to address key surveillance questions proposed by CDC and health department subject matter experts. General variables include maternal demographics and health history, pregnancy and infant outcomes, maternal and infant laboratory results, and child health outcomes up to the second birthday. Exposure-specific modular variables are included for hepatitis C, syphilis, and Coronavirus Disease 2019 (COVID-19). The system is structured into four relational datasets (maternal, pregnancy outcomes and birth, infant/child follow-up, and laboratory testing).DiscussionSET-NET provides a population-based mother–baby linked longitudinal surveillance approach and has already demonstrated rapid adaptation to COVID-19. This innovative approach leverages existing data sources and rapidly collects data and informs clinical guidance and practice. These data can help to reduce exposure risk and adverse outcomes among pregnant women and their infants, direct public health action, and strengthen public health systems.

Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses. Deep-space exploration missions require new technologies that can support astronaut health systems as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this first of two Review articles based on the findings from the workshop, a vision for autonomous biomonitoring and precision space health is discussed.

Space biology research aims to understand fundamental spaceflight effects on organisms, develop foundational knowledge to support deep space exploration and, ultimately, bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data and model organisms from both spaceborne and ground-analogue studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally automated, light, agile and intelligent to accelerate knowledge discovery. Here we present a summary of decadal recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning and modelling applications that offer solutions to these space biology challenges. The integration of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modelling and analytics, support maximally automated and reproducible experiments, and efficiently manage spaceborne data and metadata, ultimately to enable life to thrive in deep space. Deep space exploration missions will require new technologies that can support astronaut health systems, as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this second of two Review articles based on the findings from the workshop, the intersection between artificial intelligence and space biology is discussed.