Risk Factors for Dysphagia in Patients Hospitalized with COVID-19.

Patients hospitalized with COVID-19 may be at risk for dysphagia and vulnerable to associated consequences. We investigated predictors for dysphagia and its severity in a cohort of patients hospitalized with COVID-19 at a single hospital center. A large level I trauma center database was queried for all patients hospitalized with COVID-19. Demographics, medical information associated with COVID-19, specific to dysphagia, and interventions were collected. 947 patients with confirmed COVID-19 met the criteria. 118 (12%) were seen for a swallow evaluation. Individuals referred for evaluation were significantly older, had a lower BMI, more severe COVID-19, and higher rates of intubation, pneumonia, mechanical ventilation, tracheostomy placements, prone positioning, and ARDS. Pneumonia (OR 3.57, p = 0.004), ARDS (OR 3.57, p = 0.029), prone positioning (OR 3.99, p = 0.036), ventilation (OR 4.01, p = 0.006), and intubation (OR 4.75, p = 0.007) were significant risk factors for dysphagia. Older patients were more likely to have more severe dysphagia such that for every 1-year increase in age, the odds of severe dysphagia were 1.04 times greater (OR 1.04, p = 0.028). Patients hospitalized with COVID-19 are at risk for dysphagia. We show predictive variables that should be considered when referring COVID-19 patients for dysphagia services to reduce time to intervention/evaluation.

Lopinavir/ritonavir for treatment of non-hospitalized patients with COVID-19: a randomized clinical trial.

OBJECTIVES: Effective and widely available therapies are still needed for outpatients with COVID-19. We aimed to evaluate the efficacy and safety of lopinavir/ritonavir (LPV/r) for early treatment of non-hospitalized individuals diagnosed with COVID-19. METHODS: This randomized, placebo (Plb)-controlled, double-blind, multi-site decentralized clinical trial enrolled non-hospitalized adults with confirmed SARS-CoV-2 infection and six or fewer days of acute respiratory infection symptoms who were randomized to either twice-daily oral LPV/r (400 mg/100 mg) or Plb for 14 days. Daily surveys on study days 1 through 16 and again on study day 28 evaluated symptoms, daily activities, and hospitalization status. The primary outcome was longitudinal change in an ordinal scale based on a combination of symptoms, activity, and hospitalization status through day 15 and was analyzed by use of a Bayesian longitudinal proportional odds logistic regression model for estimating the probability of a superior recovery for LPV/r over Plb (odds ratio >1). RESULTS: Between June 2020 and December 2021, 448 participants were randomized to receive either LPV/r (n = 216) or Plb (n = 221). The mean symptom duration before randomization was 4.3 days (SD 1.3). There were no differences between treatment groups through the first 15 days for the ordinal primary outcome (odds ratio 0.96; 95% credible interval: 0.66 to 1.41). There were 3.2% (n = 7) of LPV/r and 2.7% (n = 6) of Plb participants hospitalized by day 28. Serious adverse events did not differ between groups. CONCLUSION: LPV/r did not significantly improve symptom resolution or reduce hospitalization in non-hospitalized participants with COVID-19. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04372628.

Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic.

OBJECTIVE: To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift. MATERIALS AND METHODS: We obtained four datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-COVID (Jan 1, 2019-Feb 1, 2020) and COVID-era (May 15, 2020-Feb 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than four hours was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for two experiments: 1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, 2) we evaluated the impact of spatial drift by testing models trained at Location 1 on data from Location 2, and vice versa. RESULTS: The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at Location 2) to 0.81 (COVID-era at Location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs. 0.78 at Location 1). Models that were transferred from Location 2 to Location 1 performed worse than models trained at Location 1 (0.51 vs. 0.78). DISCUSSION AND CONCLUSION: Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift.

Trial of Early Antiviral Therapies during Non-hospitalized Outpatient Window (TREAT NOW) for COVID-19: a summary of the protocol and analysis plan for a decentralized randomized controlled trial.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has a heterogeneous outcome in individuals from remaining asymptomatic to death. In a majority of cases, mild symptoms are present that do not require hospitalization and can be successfully treated in the outpatient setting, though symptoms may persist for a long duration. We hypothesize that drugs suitable for decentralized study in outpatients will have efficacy among infected outpatients METHODS: The TREAT NOW platform is designed to accommodate testing multiple agents with the ability to incorporate new agents in the future. TREAT NOW is an adaptive, blinded, multi-center, placebo-controlled superiority randomized clinical trial which started with two active therapies (hydroxychloroquine and lopinavir/ritonavir) and placebo, with the hydroxychloroquine arm dropped shortly after enrollment began due to external evidence. Each arm has a target enrollment of 300 participants who will be randomly assigned in an equal allocation to receive either an active therapy or placebo twice daily for 14 days with daily electronic surveys collected over days 1 through 16 and on day 29 to evaluate symptoms and a modified COVID-19 ordinal outcome scale. Participants are enrolled remotely by telephone and consented with a digital interface, study drug is overnight mailed to study participants, and data collection occurs electronically without in-person interactions. DISCUSSION: If effective treatments for COVID-19 can be identified for individuals in the outpatient setting before they advance to severe disease, it will prevent progression to more severe disease, reduce the need for hospitalization, and shorten the duration of symptoms. The novel decentralized, "no touch" approach used by the TREAT NOW platform has distinction advantages over traditional in-person trials to reach broader populations and perform study procedures in a pragmatic yet rigorous manner. TRIAL REGISTRATION: ClinicalTrials.gov NCT04372628. Registered on April 30, 2020. First posted on May 4, 2020.

The impact of coronavirus disease 2019 (COVID-19) on the antimicrobial stewardship pharmacist workforce: A multicenter survey.

Objective: The coronavirus disease 2019 (COVID-19) pandemic has required healthcare systems and hospitals to rapidly modify standard practice, including antimicrobial stewardship services. Our study examines the impact of COVID-19 on the antimicrobial stewardship pharmacist. Design: A survey was distributed nationally to all healthcare improvement company members. Participants: Pharmacist participants were mostly leaders of antimicrobial stewardship programs distributed evenly across the United States and representing urban, suburban, and rural health-system practice sites. Results: Participants reported relative increases in time spent completing tasks related to medication access and preauthorization (300%; P = .018) and administrative meeting time (34%; P = .067) during the COVID-19 pandemic compared to before the pandemic. Time spent rounding, making interventions, performing pharmacokinetic services, and medication reconciliation decreased. Conclusion: A shift away from clinical activities may negatively affect the utilization of antimicrobials.

Association Between Baseline Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers and Death Among Patients Tested for COVID-19.

Angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) drugs may modify risk associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, we assessed whether baseline therapy with ACEIs or ARBs was associated with lower mortality, respiratory failure (noninvasive ventilation or intubation), and renal failure (new renal replacement therapy) in SARS-CoV-2-positive patients. This retrospective registry-based observational cohort study used data from a national database of emergency department patients tested for SARS-CoV-2. Symptomatic emergency department patients were accrued from January to October 2020, across 197 hospitals in the United States. Multivariable analysis using logistic regression evaluated end points among SARS-CoV-2-positive cases, focusing on ACEIs/ARBs and adjusting for covariates. Model performance was evaluated using the c statistic for discrimination and Cox plotting for calibration. A total of 13 859 (99.9%) patients had known mortality status, of whom 2045 (14.8%) died. Respiratory failure occurred in 2485/13 880 (17.9%) and renal failure in 548/13 813 (4.0%) patients with available data. ACEI/ARB status was associated with a 25% decrease in mortality odds (odds ratio [OR], 0.75; 95%CI, 0.59-0.94; P = .011; c = .82). ACEIs/ARBs were not significantly associated with respiratory failure (OR, 0.89; 95%CI, 0.78-1.06; P = .206) or renal failure (OR, 0.75; 95%CI, 0.55-1.04; P = .083). Adjusting for covariates, baseline ACEI/ARB was associated with 25% lower mortality in SARS-CoV-2-positive patients. The potential mechanism for ACEI/ARB mortality modification requires further exploration.

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.

COVID-19: An Emerging Threat to Antibiotic Stewardship in the Emergency Department.

While current research efforts focus primarily on identifying patient level interventions that mitigate the direct impact of COVID-19, it is important to consider the collateral effects of COVID-19 on antimicrobial resistance. Early reports suggest high rates of antibiotic utilization in COVID-19 patients despite their lack of direct activity against viral pathogens. The ongoing pandemic is exacerbating known barriers to optimal antibiotic stewardship in the ED, representing an additional direct threat to patient safety and public health. There is an urgent need for research analyzing overall and COVID-19 specific antibiotic prescribing trends in the ED. Optimizing ED stewardship during COVID-19 will likely require a combination of traditional stewardship approaches (e.g. academic detailing, provider education, care pathways) and effective implementation of host response biomarkers and rapid COVID-19 diagnostics. Antibiotic stewardship interventions with demonstrated efficacy in mitigating the impact of COVID-19 on ED prescribing should be widely disseminated and inform the ongoing pandemic response.

Electronic Health Record-Based Surveillance for Community Transmitted COVID-19 in the Emergency Department.

INTRODUCTION: SARS-CoV-2, a novel coronavirus, manifests as a respiratory syndrome (COVID-19) and is the cause of an ongoing pandemic. The response to COVID-19 in the United States has been hampered by an overall lack of diagnostic testing capacity. To address uncertainty about ongoing levels of SARS-CoV-2 community transmission early in the pandemic, we aimed to develop a surveillance tool using readily available emergency department (ED) operations data extracted from the electronic health record (EHR). This involved optimizing the identification of acute respiratory infection (ARI)-related encounters and then comparing metrics for these encounters before and after the confirmation of SARS-CoV-2 community transmission. METHODS: We performed an observational study using operational EHR data from two Midwest EDs with a combined annual census of over 80,000. Data were collected three weeks before and after the first confirmed case of local SARS-CoV-2 community transmission. To optimize capture of ARI cases, we compared various metrics including chief complaint, discharge diagnoses, and ARI-related orders. Operational metrics for ARI cases, including volume, pathogen identification, and illness severity, were compared between the preand post-community transmission timeframes using chi-square tests of independence. RESULTS: Compared to our combined definition of ARI, chief complaint, discharge diagnoses, and isolation orders individually identified less than half of the cases. Respiratory pathogen testing was the top performing individual ARI definition but still only identified 72.2% of cases. From the pre to post periods, we observed significant increases in ED volumes due to ARI and ARI cases without identified pathogen. CONCLUSION: Certain methods for identifying ARI cases in the ED may be inadequate and multiple criteria should be used to optimize capture. In the absence of widely available SARS-CoV-2 testing, operational metrics for ARI-related encounters, especially the proportion of cases involving negative pathogen testing, are useful indicators for active surveillance of potential COVID-19 related ED visits.

Multi-tiered screening and diagnosis strategy for COVID-19: a model for sustainable testing capacity in response to pandemic.

Coronavirus disease 2019 (COVID-19), caused by novel enveloped single stranded RNA coronavirus (SARS-CoV-2), is responsible for an ongoing global pandemic. While other countries deployed widespread testing as an early mitigation strategy, the U.S. experienced delays in development and deployment of organism identification assays. As such, there is uncertainty surrounding disease burden and community spread, severely hampering containment efforts. COVID-19 illuminates the need for a tiered diagnostic approach to rapidly identify clinically significant infections and reduce disease spread. Without the ability to efficiently screen patients, hospitals are overwhelmed, potentially delaying treatment for other emergencies. A multi-tiered, diagnostic strategy incorporating a rapid host immune response assay as a screening test, molecular confirmatory testing and rapid IgM/IgG testing to assess benefit from quarantine/further testing and provide information on population exposure/herd immunity would efficiently evaluate potential COVID-19 patients. Triaging patients within minutes with a fingerstick rather than hours/days after an invasive swab is critical to pandemic response as reliance on the existing strategy is limited by assay accuracy, time to results, and testing capacity. Early screening and triage is achievable from the outset of a pandemic with point-of-care host immune response testing which will improve response time to clinical and public health actions.Key messagesDelayed testing deployment has led to uncertainty surrounding overall disease burden and community spread, severely hampering public health containment and healthcare system preparation efforts.A multi-tiered testing strategy incorporating rapid, host immune point-of-care tests can be used now and for future pandemic planning by effectively identifying patients at risk of disease thereby facilitating quarantine earlier in the progression of the outbreak during the weeks and months it can take for pathogen specific confirmatory tests to be developed, validated and manufactured in sufficient quantities.The ability to triage patients at the point of care and support the guidance of medical and therapeutic decisions, for viral isolation or confirmatory testing or for appropriate treatment of COVID-19 and/or bacterial infections, is a critical component to our national pandemic response and there is an urgent need to implement the proposed strategy to combat the current outbreak.