Clinically stable covid-19 patients presenting to acute unscheduled episodic care venues have increased risk of hospitalization: secondary analysis of a randomized control trial.

BACKGROUND: Assessment for risks associated with acute stable COVID-19 is important to optimize clinical trial enrollment and target patients for scarce therapeutics. To assess whether healthcare system engagement location is an independent predictor of outcomes we performed a secondary analysis of the ACTIV-4B Outpatient Thrombosis Prevention trial. METHODS: A secondary analysis of the ACTIV-4B trial that was conducted at 52 US sites between September 2020 and August 2021. Participants were enrolled through acute unscheduled episodic care (AUEC) enrollment location (emergency department, or urgent care clinic visit) compared to minimal contact (MC) enrollment (electronic contact from test center lists of positive patients).We report the primary composite outcome of cardiopulmonary hospitalizations, symptomatic venous thromboembolism, myocardial infarction, stroke, transient ischemic attack, systemic arterial thromboembolism, or death among stable outpatients stratified by enrollment setting, AUEC versus MC. A propensity score for AUEC enrollment was created, and Cox proportional hazards regression with inverse probability weighting (IPW) was used to compare the primary outcome by enrollment location. RESULTS: Among the 657 ACTIV-4B patients randomized, 533 (81.1%) with known enrollment setting data were included in this analysis, 227 from AUEC settings and 306 from MC settings. In a multivariate logistic regression model, time from COVID test, age, Black race, Hispanic ethnicity, and body mass index were associated with AUEC enrollment. Irrespective of trial treatment allocation, patients enrolled at an AUEC setting were 10-times more likely to suffer from the adjudicated primary outcome, 7.9% vs. 0.7%; p < 0.001, compared with patients enrolled at a MC setting. Upon Cox regression analysis adjustment patients enrolled at an AUEC setting remained at significant risk of the primary composite outcome, HR 3.40 (95% CI 1.46, 7.94). CONCLUSIONS: Patients with clinically stable COVID-19 presenting to an AUEC enrollment setting represent a population at increased risk of arterial and venous thrombosis complications, hospitalization for cardiopulmonary events, or death, when adjusted for other risk factors, compared with patients enrolled at a MC setting. Future outpatient therapeutic trials and clinical therapeutic delivery programs of clinically stable COVID-19 patients may focus on inclusion of higher-risk patient populations from AUEC engagement locations. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04498273.

D-dimer Thresholds to Exclude Pulmonary Embolism among COVID-19 Patients in the Emergency Department: Derivation with Independent Validation.

OBJECTIVE: To derive and validate a D-dimer cutoff for ruling out pulmonary embolism (PE) in COVID-19 patients presenting to the emergency department (ED). METHODS: A retrospective cohort study was performed in an integrated healthcare system including 22 adult ED's between March 1, 2020, and January 31, 2021. Results were validated among patients enrolled in the RECOVER Registry, representing data from 154 ED's from 26 US states. Consecutive ED patients with laboratory confirmed COVID-19, a D-dimer performed within 48 h of ED arrival, and with objectively confirmed PE were compared to those without PE. After identifying a D-dimer threshold at which the 95% confidence lower bound of the negative predictive value for PE was higher than 98% in the derivation cohort, it was validated using RECOVER registry data. RESULTS: Among 3978 patients with a D-dimer result, 3583 with confirmed COVID-19 infection were included in the derivation cohort. Overall, PE incidence was 4.1% and a D-dimer cutoff of <2 µ/mL (2000 ng/mL) was associated with a NPV of 98.5% (95% CI = 98.0%-98.9%). In the validation cohort of 13,091 patients with a D-dimer, 7748 had confirmed COVID-19 infection, and the PE incidence was 1.14%. A D-dimer cutoff of <2 µ/mL was associated with a NPV of 99.5% (95% CI = 99.3%-99.7%). CONCLUSION: A D-dimer cutoff of <2 µ/ml was associated with a high negative predictive value for PE among patients with COVID-19. However, the resultant sensitivity for PE result at that threshold without pre-test probability assessment would be considered clinically unsafe.

Biomarker derived risk scores predict venous thromboembolism and major bleeding among patients with COVID-19.

Background: Venous thromboembolism (VTE) risk is increased in patients with COVID-19 infection. Understanding which patients are likely to develop VTE may inform pharmacologic VTE prophylaxis decision making. The hospital-associated venous thromboembolism-Intermountain Risk Score (HA-VTE IMRS) and the hospital-associated major bleeding-Intermountain Risk Score (HA-MB IMRS) are risk scores predictive of VTE and bleeding that were derived from only patient age and data found in the complete blood count (CBC) and basic metabolic panel (BMP). Objectives: We assessed the HA-VTE IMRS and HA-MB IMRS for predictiveness of 90-day VTE and major bleeding, respectively, among patients diagnosed with COVID-19, and further investigated if adding D-dimer improved these predictions. We also reported 30-day outcomes. Patients/Methods: We identified 5047 sequential patients with a laboratory confirmed diagnosis of COVID-19 and a CBC and BMP between 2 days before and 7 days following the diagnosis of COVID-19 from March 12, 2020, to February 28, 2021. We calculated the HA-VTE IMRS and the HA-MB IMRS for all patients. We assessed the added predictiveness of D-dimer obtained within 48 hours of the COVID test. Results: The HA-VTE IMRS yielded a c-statistic of 0.70 for predicting 90-day VTE and adding D-dimer improved the c-statistic to 0.764 with the corollary sensitivity/specificity/positive/negative predictive values of 49.4%/75.7%/6.7%/97.7% and 58.8%/76.2%/10.9%/97.4%, respectively. Among hospitalized and ambulatory patients separately, the HA-VTE IMRS performed similarly. The HA-MB IMRS predictiveness for 90-day major bleeding yielded a c-statistic of 0.64. Conclusion: The HA-VTE IMRS and HA-MB IMRS predict 90- and 30-day VTE and major bleeding among COVID-19 patients. Adding D-dimer improved the predictiveness of the HA-VTE IMRS for VTE.

Simple scoring tool to estimate risk of hospitalization and mortality in ambulatory and emergency department patients with COVID-19.

BACKGROUND: Accurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality. METHODS: All consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality. RESULTS: 22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n = 16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n = 6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9). CONCLUSION: A prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

Association of the Intermountain Risk Score with major adverse health events in patients positive for COVID-19: an observational evaluation of a US cohort.

OBJECTIVES: The Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive. DESIGN: Prospective observational cohort study. SETTING: Primary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020. PARTICIPANTS: Patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately. RESULTS: Among 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged <60 years had HR=3.06 (95% CI 2.01 to 4.65) and HR=7.38 (95% CI 3.14 to 17.34) for moderate and high risks versus low risk, respectively; those ≥60 years had HR=1.95 (95% CI 0.99 to 3.86) and HR=3.40 (95% CI 1.63 to 7.07). In multivariable analyses, IMRS was independently predictive and was shown to capture substantial risk variation of comorbidities. CONCLUSIONS: IMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.

Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

Effect of Antithrombotic Therapy on Clinical Outcomes in Outpatients With Clinically Stable Symptomatic COVID-19: The ACTIV-4B Randomized Clinical Trial.

Importance: Acutely ill inpatients with COVID-19 typically receive antithrombotic therapy, although the risks and benefits of this intervention among outpatients with COVID-19 have not been established. Objective: To assess whether anticoagulant or antiplatelet therapy can safely reduce major adverse cardiopulmonary outcomes among symptomatic but clinically stable outpatients with COVID-19. Design, Setting, and Participants: The ACTIV-4B Outpatient Thrombosis Prevention Trial was designed as a minimal-contact, adaptive, randomized, double-blind, placebo-controlled trial to compare anticoagulant and antiplatelet therapy among 7000 symptomatic but clinically stable outpatients with COVID-19. The trial was conducted at 52 US sites between September 2020 and June 2021; final follow-up was August 5, 2021. Prior to initiating treatment, participants were required to have platelet count greater than 100 000/mm3 and estimated glomerular filtration rate greater than 30 mL/min/1.73 m2. Interventions: Random allocation in a 1:1:1:1 ratio to aspirin (81 mg orally once daily; n = 164), prophylactic-dose apixaban (2.5 mg orally twice daily; n = 165), therapeutic-dose apixaban (5 mg orally twice daily; n = 164), or placebo (n = 164) for 45 days. Main Outcomes and Measures: The primary end point was a composite of all-cause mortality, symptomatic venous or arterial thromboembolism, myocardial infarction, stroke, or hospitalization for cardiovascular or pulmonary cause. The primary analyses for efficacy and bleeding events were limited to participants who took at least 1 dose of trial medication. Results: On June 18, 2021, the trial data and safety monitoring board recommended early termination because of lower than anticipated event rates; at that time, 657 symptomatic outpatients with COVID-19 had been randomized (median age, 54 years [IQR, 46-59]; 59% women). The median times from diagnosis to randomization and from randomization to initiation of study treatment were 7 days and 3 days, respectively. Twenty-two randomized participants (3.3%) were hospitalized for COVID-19 prior to initiating treatment. Among the 558 patients who initiated treatment, the adjudicated primary composite end point occurred in 1 patient (0.7%) in the aspirin group, 1 patient (0.7%) in the 2.5-mg apixaban group, 2 patients (1.4%) in the 5-mg apixaban group, and 1 patient (0.7%) in the placebo group. The risk differences compared with placebo for the primary end point were 0.0% (95% CI not calculable) in the aspirin group, 0.7% (95% CI, -2.1% to 4.1%) in the 2.5-mg apixaban group, and 1.4% (95% CI, -1.5% to 5.0%) in the 5-mg apixaban group. Risk differences compared with placebo for bleeding events were 2.0% (95% CI, -2.7% to 6.8%), 4.5% (95% CI, -0.7% to 10.2%), and 6.9% (95% CI, 1.4% to 12.9%) among participants who initiated therapy in the aspirin, prophylactic apixaban, and therapeutic apixaban groups, respectively, although none were major. Findings inclusive of all randomized patients were similar. Conclusions and Relevance: Among symptomatic clinically stable outpatients with COVID-19, treatment with aspirin or apixaban compared with placebo did not reduce the rate of a composite clinical outcome. However, the study was terminated after enrollment of 9% of participants because of an event rate lower than anticipated. Trial Registration: ClinicalTrials.gov Identifier: NCT04498273.

Real-world Effectiveness and Tolerability of Monoclonal Antibody Therapy for Ambulatory Patients With Early COVID-19.

BACKGROUND: Neutralizing monoclonal antibodies (MAbs) are a promising therapy for early coronavirus disease 2019 (COVID-19), but their effectiveness has not been confirmed in a real-world setting. METHODS: In this quasi-experimental pre-/postimplementation study, we estimated the effectiveness of MAb treatment within 7 days of symptom onset in high-risk ambulatory adults with COVID-19. The primary outcome was a composite of emergency department visits or hospitalizations within 14 days of positive test. Secondary outcomes included adverse events and 14-day mortality. The average treatment effect in the treated for MAb therapy was estimated using inverse probability of treatment weighting and the impact of MAb implementation using propensity-weighted interrupted time series analysis. RESULTS: Pre-implementation (July-November 2020), 7404 qualifying patients were identified. Postimplementation (December 2020-January 2021), 594 patients received MAb treatment and 5536 did not. The primary outcome occurred in 75 (12.6%) MAb recipients, 1018 (18.4%) contemporaneous controls, and 1525 (20.6%) historical controls. MAb treatment was associated with decreased likelihood of emergency care or hospitalization (odds ratio, 0.69; 95% CI, 0.60-0.79). After implementation, the weighted probability that a given patient would require an emergency department visit or hospitalization decreased significantly (0.7% per day; 95% CI, 0.03%-0.10%). Mortality was 0.2% (n = 1) in the MAb group compared with 1.0% (n = 71) and 1.0% (n = 57) in pre- and postimplementation controls, respectively. Adverse events occurred in 7 (1.2%); 2 (0.3%) were considered serious. CONCLUSIONS: MAb treatment of high-risk ambulatory patients with early COVID-19 was well tolerated and likely effective at preventing the need for subsequent emergency department or hospital care.

Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study.

BACKGROUND: Unlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD). METHODS: This retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare. RESULTS: CLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases. CONCLUSIONS: The performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.