Patients' Perspectives on Life and Recovery 1 Year After COVID-19 Hospitalization.

BACKGROUND: Many patients hospitalized for COVID-19 experience prolonged symptoms months after discharge. Little is known abou t patients' personal experiences recovering from COVID-19 in the United States (US), where medically underserved populations are at particular risk of adverse outcomes. OBJECTIVE: To explore patients' perspectives on the impact of COVID-19 hospitalization and barriers to and facilitators of recovery 1 year after hospital discharge in a predominantly Black American study population with high neighborhood-level socioeconomic disadvantage. DESIGN: Qualitative study utilizing individual, semi-structured interviews. PARTICIPANTS: Adult patients hospitalized for COVID-19 approximately 1 year after discharge home who were engaged in a COVID-19 longitudinal cohort study. APPROACH: The interview guide was developed and piloted by a multidisciplinary team. Interviews were audio-recorded and transcribed. Data were coded and organized into discrete themes using qualitative content analysis with constant comparison techniques. KEY RESULTS: Of 24 participants, 17 (71%) self-identified as Black, and 13 (54%) resided in neighborhoods with the most severe neighborhood-level socioeconomic disadvantage. One year after discharge, participants described persistent deficits in physical, cognitive, or psychological health that impacted their current lives. Repercussions included financial suffering and a loss of identity. Participants reported that clinicians often focused on physical health over cognitive and psychological health, an emphasis that posed a barrier to recovering holistically. Facilitators of recovery included robust financial or social support systems and personal agency in health maintenance. Spirituality and gratitude were common coping mechanisms. CONCLUSIONS: Persistent health deficits after COVID-19 resulted in downstream consequences in participants' lives. Though participants received adequate care to address physical needs, many described persistent unmet cognitive and psychological needs. A more comprehensive understanding of barriers and facilitators for COVID-19 recovery, contextualized by specific healthcare and socioeconomic needs related to socioeconomic disadvantage, is needed to better inform intervention delivery to patients that experience long-term sequelae of COVID-19 hospitalization.

Validating a Proteomic Signature of Severe COVID-19.

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia.

The currently recommended dose of dexamethasone for patients with severe or critical COVID-19 is 6 mg per day (mg/d) regardless of patient features and variation. However, patients with severe or critical COVID-19 are heterogenous in many ways (e.g., age, weight, comorbidities, disease severity, and immune features). Thus, it is conceivable that a standardized dosing protocol may not be optimal. We assessed treatment effect heterogeneity in the COVID STEROID 2 trial, which compared 6 mg/d to 12 mg/d, using a causal inference framework with Bayesian Additive Regression Trees, a flexible modeling method that detects interactive effects and nonlinear relationships among multiple patient characteristics simultaneously. We found that 12 mg/d of dexamethasone, relative to 6 mg/d, was probably associated with better long-term outcomes (days alive without life support and mortality after 90 days) among the entire trial population (i.e., no signals of harm), and probably more beneficial among those without diabetes mellitus, that were older, were not using IL-6 inhibitors at baseline, weighed less, or had higher level respiratory support at baseline. This adds more evidence supporting the use of 12 mg/d in practice for most patients not receiving other immunosuppressants and that additional study of dosing could potentially optimize clinical outcomes.

Validating a Proteomic Signature of Severe COVID-19

OBJECTIVES: COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Immune Modulation in Sepsis, ARDS, and Covid-19 - The Road Traveled and the Road Ahead

Leligdowicz et al. consider the history and future of immunomodulating therapies in sepsis and ARDS, including ARDS due to Covid-19, and remark on the larger challenge of clinical research on therapies for syndromes with profound clinical and biologic heterogeneity.

Use of pragmatic and explanatory trial designs in acute care research: lessons from COVID-19.

Unique challenges arise when conducting trials to evaluate therapies already in common clinical use, including difficulty enrolling patients owing to widespread open-label use of trial therapies and the need for large sample sizes to detect small but clinically meaningful treatment effects. Despite numerous successes in trials evaluating novel interventions such as vaccines, traditional explanatory trials have struggled to provide definitive answers to time-sensitive questions for acutely ill patients with COVID-19. Pragmatic trials, which can increase efficiency by allowing some or all trial procedures to be embedded into clinical care, are increasingly proposed as a means to evaluate therapies that are in common clinical use. In this Personal View, we use two concurrently conducted COVID-19 trials of hydroxychloroquine (the US ORCHID trial and the UK RECOVERY trial) to contrast the effects of explanatory and pragmatic trial designs on trial conduct, trial results, and the care of patients managed outside of clinical trials. In view of the potential advantages and disadvantages of explanatory and pragmatic trial designs, we make recommendations for their optimal use in the evaluation of therapies in the acute care setting.

Statewide Interventions and Coronavirus Disease 2019 Mortality in the United States: An Observational Study.

BACKGROUND: Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in coronavirus disease 2019 (COVID-19) has not been demonstrated. In the United States, social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were 2 early statewide interventions. METHODS: To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55 146 COVID-19 nonsurvivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. RESULTS: At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay; 95% confidence interval [CI], 1.00-1.09; P = .040) and later school closure (aMRR 1.05; 95% CI, 1.01-1.09; P = .008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05; 95% CI, 1.01-1.09; P = .020) or closing schools (aMRR 1.06; 95% CI, 1.03-1.09; P < .001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. CONCLUSIONS: Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

Outcomes of Patients With Coronavirus Disease 2019 Receiving Organ Support Therapies: The International Viral Infection and Respiratory Illness Universal Study Registry.

OBJECTIVES: To describe the outcomes of hospitalized patients in a multicenter, international coronavirus disease 2019 registry. DESIGN: Cross-sectional observational study including coronavirus disease 2019 patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus-2 infection between February 15, 2020, and November 30, 2020, according to age and type of organ support therapies. SETTING: About 168 hospitals in 16 countries within the Society of Critical Care Medicine's Discovery Viral Infection and Respiratory Illness University Study coronavirus disease 2019 registry. PATIENTS: Adult hospitalized coronavirus disease 2019 patients who did and did not require various types and combinations of organ support (mechanical ventilation, renal replacement therapy, vasopressors, and extracorporeal membrane oxygenation). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome was hospital mortality. Secondary outcomes were discharge home with or without assistance and hospital length of stay. Risk-adjusted variation in hospital mortality for patients receiving invasive mechanical ventilation was assessed by using multilevel models with hospitals as a random effect, adjusted for age, race/ethnicity, sex, and comorbidities. Among 20,608 patients with coronavirus disease 2019, the mean (± sd) age was 60.5 (±17), 11,1887 (54.3%) were men, 8,745 (42.4%) were admitted to the ICU, and 3,906 (19%) died in the hospital. Hospital mortality was 8.2% for patients receiving no organ support (n = 15,001). The most common organ support therapy was invasive mechanical ventilation (n = 5,005; 24.3%), with a hospital mortality of 49.8%. Mortality ranged from 40.8% among patients receiving only invasive mechanical ventilation (n =1,749) to 71.6% for patients receiving invasive mechanical ventilation, vasoactive drugs, and new renal replacement therapy (n = 655). Mortality was 39% for patients receiving extracorporeal membrane oxygenation (n = 389). Rates of discharge home ranged from 73.5% for patients who did not require organ support therapies to 29.8% for patients who only received invasive mechanical ventilation, and 8.8% for invasive mechanical ventilation, vasoactive drugs, and renal replacement; 10.8% of patients older than 74 years who received invasive mechanical ventilation were discharged home. Median hospital length of stay for patients on mechanical ventilation was 17.1 days (9.7-28 d). Adjusted interhospital variation in mortality among patients receiving invasive mechanical ventilation was large (median odds ratio 1.69). CONCLUSIONS: Coronavirus disease 2019 prognosis varies by age and level of organ support. Interhospital variation in mortality of mechanically ventilated patients was not explained by patient characteristics and requires further evaluation.

Variation in Initial U.S. Hospital Responses to the Coronavirus Disease 2019 Pandemic.

OBJECTIVES: The coronavirus disease 2019 pandemic has strained many healthcare systems. In response, U.S. hospitals altered their care delivery systems, but there are few data regarding specific structural changes. Understanding these changes is important to guide interpretation of outcomes and inform pandemic preparedness. We sought to characterize emergency responses across hospitals in the United States over time and in the context of local case rates early in the coronavirus disease 2019 pandemic. DESIGN: We surveyed hospitals from a national acute care trials group regarding operational and structural changes made in response to the coronavirus disease 2019 pandemic from January to August 2020. We collected prepandemic characteristics and changes to hospital system, space, staffing, and equipment during the pandemic. We compared the timing of these changes with county-level coronavirus disease 2019 case rates. SETTING AND PARTICIPANTS: U.S. hospitals participating in the Prevention and Early Treatment of Acute Lung Injury Network Coronavirus Disease 2019 Observational study. Site investigators at each hospital collected local data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Forty-five sites participated (94% response rate). System-level changes (incident command activation and elective procedure cancellation) occurred at nearly all sites, preceding rises in local case rates. The peak inpatient census during the pandemic was greater than the prior hospital bed capacity in 57% of sites with notable regional variation. Nearly half (49%) expanded ward capacity, and 63% expanded ICU capacity, with nearly all bed expansion achieved through repurposing of clinical spaces. Two-thirds of sites adapted staffing to care for patients with coronavirus disease 2019, with 48% implementing tiered staffing models, 49% adding temporary physicians, nurses, or respiratory therapists, and 30% changing the ratios of physicians or nurses to patients. CONCLUSIONS: The coronavirus disease 2019 pandemic prompted widespread system-level changes, but front-line clinical care varied widely according to specific hospital needs and infrastructure. Linking operational changes to care delivery processes is a necessary step to understand the impact of the coronavirus disease 2019 pandemic on patient outcomes.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Study protocol for a multicentre, prospective cohort study of the association of angiotensin II type 1 receptor blockers on outcomes of coronavirus infection.

INTRODUCTION: The COVID-19 epidemic grows and there are clinical trials of antivirals. There is an opportunity to complement these trials with investigation of angiotensin II type 1 receptor blockers (ARBs) because an ARB (losartan) was effective in murine influenza pneumonia. METHODS AND ANALYSIS: Our innovative design includes: ARBs; alignment with the WHO Ordinal Scale (primary endpoint) to align with other COVID-19 trials; joint longitudinal analysis; and predictive biomarkers (angiotensins I, 1-7, II and ACE1 and ACE2). Our hypothesis is: ARBs decrease the need for hospitalisation, severity (need for ventilation, vasopressors, extracorporeal membrane oxygenation or renal replacement therapy) or mortality of hospitalised COVID-19 infected adults. Our two-pronged multicentre pragmatic observational cohort study examines safety and effectiveness of ARBs in (1) hospitalised adult patients with COVID-19 and (2) out-patients already on or not on ARBs. The primary outcome will be evaluated by ordinal logistic regression and main secondary outcomes by both joint longitudinal modelling analyses. We will compare rates of hospitalisation of ARB-exposed versus not ARB-exposed patients. We will also determine whether continuing ARBs or not decreases the primary outcome. Based on published COVID-19 cohorts, assuming 15% of patients are ARB-exposed, a total sample size of 497 patients can detect a proportional OR of 0.5 (alpha=0.05, 80% power) comparing WHO scale of ARB-exposed versus non-ARB-exposed patients. ETHICS AND DISSEMINATION: This study has core institution approval (UBC Providence Healthcare Research Ethics Board) and site institution approvals (Health Research Ethics Board, University of Alberta; Comite d'etique de la recerche, CHU Sainte Justine (for McGill University and University of Sherbrook); Conjoint Health Research Ethics Board, University of Calgary; Queen's University Health Sciences & Affiliated Hospitals Research Ethics Board; Research Ethics Board, Sunnybrook Health Sciences Centre; Veritas Independent Research Board (for Humber River Hospital); Mount Sinai Hospital Research Ethics Board; Unity Health Toronto Research Ethics Board, St. Michael's Hospital). Results will be disseminated by peer-review publication and social media releases. TRIAL REGISTRATION NUMBER: NCT04510623.

Cytokine elevation in severe and critical COVID-19: a rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes.

The description of a so-called cytokine storm in patients with COVID-19 has prompted consideration of anti-cytokine therapies, particularly interleukin-6 antagonists. However, direct systematic comparisons of COVID-19 with other critical illnesses associated with elevated cytokine concentrations have not been reported. In this Rapid Review, we report the results of a systematic review and meta-analysis of COVID-19 studies published or posted as preprints between Nov 1, 2019, and April 14, 2020, in which interleukin-6 concentrations in patients with severe or critical disease were recorded. 25 COVID-19 studies (n=1245 patients) were ultimately included. Comparator groups included four trials each in sepsis (n=5320), cytokine release syndrome (n=72), and acute respiratory distress syndrome unrelated to COVID-19 (n=2767). In patients with severe or critical COVID-19, the pooled mean serum interleukin-6 concentration was 36·7 pg/mL (95% CI 21·6-62·3 pg/mL; I2=57·7%). Mean interleukin-6 concentrations were nearly 100 times higher in patients with cytokine release syndrome (3110·5 pg/mL, 632·3-15 302·9 pg/mL; p<0·0001), 27 times higher in patients with sepsis (983·6 pg/mL, 550·1-1758·4 pg/mL; p<0·0001), and 12 times higher in patients with acute respiratory distress syndrome unrelated to COVID-19 (460 pg/mL, 216·3-978·7 pg/mL; p<0·0001). Our findings question the role of a cytokine storm in COVID-19-induced organ dysfunction. Many questions remain about the immune features of COVID-19 and the potential role of anti-cytokine and immune-modulating treatments in patients with the disease.

Randomized elimination and prolongation of ACE inhibitors and ARBs in coronavirus 2019 (REPLACE COVID) Trial Protocol.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), is associated with high incidence of multiorgan dysfunction and death. Angiotensin-converting enzyme 2 (ACE2), which facilitates SARS-CoV-2 host cell entry, may be impacted by angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), two commonly used antihypertensive classes. In a multicenter, international randomized controlled trial that began enrollment on March 31, 2020, participants are randomized to continuation vs withdrawal of their long-term outpatient ACEI or ARB upon hospitalization with COVID-19. The primary outcome is a hierarchical global rank score incorporating time to death, duration of mechanical ventilation, duration of renal replacement or vasopressor therapy, and multiorgan dysfunction severity. Approval for the study has been obtained from the Institutional Review Board of each participating institution, and all participants will provide informed consent. A data safety monitoring board has been assembled to provide independent oversight of the project.

Guiding Principles for the Conduct of Observational Critical Care Research for Coronavirus Disease 2019 Pandemics and Beyond: The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study Registry.

OBJECTIVES: Use of observational data to inform the response and care of patients during a pandemic faces unique challenges. DESIGN: The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study COVID 2019 Registry Core data and research methodology team convened over virtual meetings throughout March to June 2020 to determine best practice goals for development of a pandemic disease registry to support rapid data collection and analysis. SETTING: International, multi-center registry of hospitalized patients. PATIENTS: None. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Large-scale observational data collection requires: 1) quality assurance and harmonization across many sites; 2) a transparent process for selecting from among many potential research questions; 3) the use of best practices in design of descriptive, predictive, and inferential studies; (4) innovative approaches to characterize random error in the setting of constantly updated data; (5) rapid peer-review and reporting; and (6) transitions from a focus on discovery to implementation. Herein, we describe the guiding principles to best practices and suggestions for innovations to study design and reporting within the coronavirus disease 2019 Viral Infection and Respiratory Illness Universal Study pandemic registry. CONCLUSIONS: Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study coronavirus disease 2019 registry sought to develop and implement prespecified best practices combined with grassroots efforts from clinical sites worldwide in order to develop clinically useful knowledge in response to a pandemic.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Is There an Association Between COVID-19 Mortality and the Renin-Angiotensin System? A Call for Epidemiologic Investigations.

Mortality from coronavirus disease 2019 (COVID-19) is strongly associated with cardiovascular disease, diabetes, and hypertension. These disorders share underlying pathophysiology related to the renin-angiotensin system (RAS) that may be clinically insightful. In particular, activity of the angiotensin-converting enzyme 2 (ACE2) is dysregulated in cardiovascular disease, and this enzyme is used by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to initiate the infection. Cardiovascular disease and pharmacologic RAS inhibition both increase ACE2 levels, which may increase the virulence of SARS-CoV-2 within the lung and heart. Conversely, mechanistic evidence from related coronaviruses suggests that SARS-CoV-2 infection may downregulate ACE2, leading to toxic overaccumulation of angiotensin II that induces acute respiratory distress syndrome and fulminant myocarditis. RAS inhibition could mitigate this effect. With conflicting mechanistic evidence, we propose key clinical research priorities necessary to clarify the role of RAS inhibition in COVID-19 mortality that could be rapidly addressed by the international research community.