Intravenous ravulizumab in mechanically ventilated patients hospitalised with severe COVID-19: a phase 3, multicentre, open-label, randomised controlled trial.

BACKGROUND: The complement pathway is a potential target for the treatment of severe COVID-19. We evaluated the safety and efficacy of ravulizumab, a terminal complement C5 inhibitor, in patients hospitalised with severe COVID-19 requiring invasive or non-invasive mechanical ventilation. METHODS: This phase 3, multicentre, open-label, randomised controlled trial (ALXN1210-COV-305) enrolled adult patients (aged ≥18 years) from 31 hospitals in France, Japan, Spain, the UK, and the USA. Eligible patients had a confirmed diagnosis of SARS-CoV-2 that required hospitalisation and either invasive or non-invasive mechanical ventilation, with severe pneumonia, acute lung injury, or acute respiratory distress syndrome confirmed by CT scan or x-ray. We randomly assigned participants (2:1) to receive intravenous ravulizumab plus best supportive care (BSC) or BSC alone using a web-based interactive response system. Randomisation was in permuted blocks of six with stratification by intubation status. Bodyweight-based intravenous doses of ravulizumab were administered on days 1, 5, 10, and 15. The primary efficacy endpoint was survival based on all-cause mortality at day 29 in the intention-to-treat (ITT) population. Safety endpoints were analysed in all randomly assigned patients in the ravulizumab plus BSC group who received at least one dose of ravulizumab, and in all randomly assigned patients in the BSC group. The trial is registered with ClinicalTrials.gov, NCT04369469, and was terminated at interim analysis due to futility. FINDINGS: Between May 10, 2020, and Jan 13, 2021, 202 patients were enrolled in the study and randomly assigned to ravulizumab plus BSC or BSC. 201 patients were included in the ITT population (135 in the ravulizumab plus BSC group and 66 in the BSC group). The ravulizumab plus BSC group comprised 96 (71%) men and 39 (29%) women with a mean age of 63·2 years (SD 13·23); the BSC group comprised 43 (65%) men and 23 (35%) women with a mean age of 63·5 years (12·40). Most patients (113 [84%] of 135 in the ravulizumab plus BSC group and 53 [80%] of 66 in the BSC group) were on invasive mechanical ventilation at baseline. Overall survival estimates based on multiple imputation were 58% for patients receiving ravulizumab plus BSC and 60% for patients receiving BSC (Mantel-Haenszel analysis: risk difference -0·0205; 95% CI -0·1703 to 0·1293; one-sided p=0·61). In the safety population, 113 (89%) of 127 patients in the ravulizumab plus BSC group and 56 (84%) of 67 in the BSC group had a treatment-emergent adverse event. Of these events, infections and infestations (73 [57%] vs 24 [36%] patients) and vascular disorders (39 [31%] vs 12 [18%]) were observed more frequently in the ravulizumab plus BSC group than in the BSC group. Five patients had serious adverse events considered to be related to ravulizumab. These events were bacteraemia, thrombocytopenia, oesophageal haemorrhage, cryptococcal pneumonia, and pyrexia (in one patient each). INTERPRETATION: Addition of ravulizumab to BSC did not improve survival or other secondary outcomes. Safety findings were consistent with the known safety profile of ravulizumab in its approved indications. Despite the lack of efficacy, the study adds value for future research into complement therapeutics in critical illnesses by showing that C5 inhibition can be accomplished in severely ill patients. FUNDING: Alexion, AstraZeneca Rare Disease.

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings. DATA SOURCES: We searched MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021. STUDY SELECTION: We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point. DATA EXTRACTION: Study data were abstracted by two independent reviewers using a standardized data extraction form. DATA SYNTHESIS: Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70-0.87]) and reduced hospital LOS overall. CONCLUSIONS: HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

The Social Data Foundation model: Facilitating health and social care transformation through datatrust services

Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.

Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach.

BACKGROUND: ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team's cognitive capacity. METHODS: The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team's decision making. RESULTS: Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19. CONCLUSIONS: Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team's cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

International transfers of personal data for health research following Schrems II: a problem in need of a solution.

On 16 July 2020, the Court of Justice of the European Union issued their decision in the Schrems II case concerning Facebook's transfers of personal data from the EU to the US. The decision may have significant effects on the legitimate transfer of personal data for health research purposes from the EU. This article aims: (i) to outline the consequences of the Schrems II decision for the sharing of personal data for health research between the EU and third countries, particularly in the context of the COVID-19 pandemic; and, (ii) to consider certain options available to address the consequences of the decision and to facilitate international data exchange for health research moving forward.

Healthcare disparities among anticoagulation therapies for severe COVID-19 patients in the multi-site VIRUS registry.

Here we analyze hospitalized andintensive care unit coronavirus disease 2019 (COVID-19) patient outcomes from the international VIRUS registry (https://clinicaltrials.gov/ct2/show/NCT04323787). We find that COVID-19 patients administered unfractionated heparin but not enoxaparin have a higher mortality-rate (390 of 1012 = 39%) compared to patients administered enoxaparin but not unfractionated heparin (270 of 1939 = 14%), presenting a risk ratio of 2.79 (95% confidence interval [CI]: [2.42, 3.16]; p = 4.45e-52). This difference persists even after balancing on a number of covariates including demographics, comorbidities, admission diagnoses, and method of oxygenation, with an increased mortality rate on discharge from the hospital of 37% (268 of 733) for unfractionated heparin versus 22% (154 of 711) for enoxaparin, presenting a risk ratio of 1.69 (95% CI: [1.42, 2.00]; p = 1.5e-8). In these balanced cohorts, a number of complications occurred at an elevated rate for patients administered unfractionated heparin compared to patients administered enoxaparin, including acute kidney injury, acute cardiac injury, septic shock, and anemia. Furthermore, a higher percentage of Black/African American COVID patients (414 of 1294 [32%]) were noted to receive unfractionated heparin compared to White/Caucasian COVID patients (671 of 2644 [25%]), risk ratio 1.26 (95% CI: [1.14, 1.40]; p = 7.5e-5). After balancing upon available clinical covariates, this difference in anticoagulant use remained statistically significant (311 of 1047 [30%] for Black/African American vs. 263 of 1047 [25%] for White/Caucasian, p = .02, risk ratio 1.18; 95% CI: [1.03, 1.36]). While retrospective studies cannot suggest any causality, these findings motivate the need for follow-up prospective research into the observed racial disparity in anticoagulant use and outcomes for severe COVID-19 patients.

Clinical Characteristics, Treatment, and Outcomes of Critically Ill Patients With COVID-19: A Scoping Review.

A growing number of studies on coronavirus disease 2019 (COVID-19) are becoming available, but a synthesis of available data focusing on the critically ill population has not been conducted. We performed a scoping review to synthesize clinical characteristics, treatment, and clinical outcomes among critically ill patients with COVID-19. Between January 1, 2020, and May 15, 2020, we identified high-quality clinical studies describing critically ill patients with a sample size of greater than 20 patients by performing daily searches of the World Health Organization and LitCovid databases on COVID-19. Two reviewers independently reviewed all abstracts (2785 unique articles), full text (218 articles), and abstracted data (92 studies). The 92 studies included 61 from Asia, 16 from Europe, 10 from North and South America, and 5 multinational studies. Notable similarities among critically ill populations across all regions included a higher proportion of older males infected and with severe illness, high frequency of comorbidities (hypertension, diabetes, and cardiovascular disease), abnormal chest imaging findings, and death secondary to respiratory failure. Differences in regions included newly identified complications (eg, pulmonary embolism) and epidemiological risk factors (eg, obesity), less chest computed tomography performed, and increased use of invasive mechanical ventilation (70% to 100% vs 15% to 47% of intensive care unit patients) in Europe and the United States compared with Asia. Future research directions should include proof-of-mechanism studies to better understand organ injuries and large-scale collaborative clinical studies to evaluate the efficacy and safety of antivirals, antibiotics, interleukin 6 receptor blockers, and interferon. The current established predictive models require further verification in other regions outside China.

GM-CSF Neutralization With Lenzilumab in Severe COVID-19 Pneumonia: A Case-Cohort Study.

OBJECTIVE: To assess the efficacy and safety of lenzilumab in patients with severe coronavirus disease 2019 (COVID-19) pneumonia. METHODS: Hospitalized patients with COVID-19 pneumonia and risk factors for poor outcomes were treated with lenzilumab 600 mg intravenously for three doses through an emergency single-use investigational new drug application. Patient characteristics, clinical and laboratory outcomes, and adverse events were recorded. We also identified a cohort of patients matched to the lenzilumab patients for age, sex, and disease severity. Study dates were March 13, 2020, to June 18, 2020. All patients were followed through hospital discharge or death. RESULTS: Twelve patients were treated with lenzilumab; 27 patients comprised the matched control cohort (untreated). Clinical improvement, defined as improvement of at least 2 points on the 8-point ordinal clinical endpoints scale, was observed in 11 of 12 (91.7%) patients treated with lenzilumab and 22 of 27 (81.5%) untreated patients. The time to clinical improvement was significantly shorter for the lenzilumab-treated group compared with the untreated cohort with a median of 5 days versus 11 days (P=.006). Similarly, the proportion of patients with acute respiratory distress syndrome (oxygen saturation/fraction of inspired oxygen<315 mm Hg) was significantly reduced over time when treated with lenzilumab compared with untreated (P<.001). Significant improvement in inflammatory markers (C-reactive protein and interleukin 6) and markers of disease severity (absolute lymphocyte count) were observed in patients who received lenzilumab, but not in untreated patients. Cytokine analysis showed a reduction in inflammatory myeloid cells 2 days after lenzilumab treatment. There were no treatment-emergent adverse events attributable to lenzilumab. CONCLUSION: In high-risk COVID-19 patients with severe pneumonia, granulocyte-macrophage colony-stimulating factor neutralization with lenzilumab was safe and associated with faster improvement in clinical outcomes, including oxygenation, and greater reductions in inflammatory markers compared with a matched control cohort of patients hospitalized with severe COVID-19 pneumonia. A randomized, placebo-controlled clinical trial to validate these findings is ongoing (NCT04351152).

Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis.

Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVIDpos; n = 2,317) versus COVID-19-negative (COVIDneg; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVIDpos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.