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

Pandemic phase-related racial and ethnic disparities in COVID-19 positivity and outcomes among patients presenting to emergency departments during the first two pandemic waves in the USA

BACKGROUND

In many countries including the USA, the UK and Canada, the impact of COVID-19 on people of colour has been disproportionately high but examination of disparities in patients presenting to ED has been limited. We assessed racial and ethnic differences in COVID-19 positivity and outcomes in patients presenting to EDs in the USA, and the effect of the phase of the pandemic on these outcomes.

METHODS

This is a retrospective cohort study of adult patients tested for COVID-19 during, or 14 days prior to, the index ED visit in 2020. Data were obtained from the National Registry of Suspected COVID-19 in Emergency Care network which has data from 155 EDs across 27 US states. Hierarchical models were used to account for clustering by hospital. The outcomes included COVID-19 diagnosis, hospitalisation at index visit, subsequent hospitalisation within 30 days and 30-day mortality. We further stratified the analysis by time period (early phase: March-June 2020; late phase: July-September 2020).

RESULTS

Of the 26 111 adult patients, 38% were non-Hispanic White (NHW), 29% Black, 20% Hispanic/Latino, 3% Asian and 10% all others; half were female. The median age was 56 years (IQR 40-69), and 53% were diagnosed with COVID-19; of those, 59% were hospitalised at index visit. Of those discharged from ED, 47% had a subsequent hospitalisation in 30 days. Hispanic/Latino patients had twice (adjusted OR (aOR) 2.3; 95% CI 1.8 to 3.0) the odds of COVID-19 diagnosis than NHW patients, after adjusting for age, sex and comorbidities. Black, Asian and other minority groups also had higher odds of being diagnosed (compared with NHW patients). On stratification, this association was observed in both phases for Hispanic/Latino patients. Hispanic/Latino patients had lower odds of hospitalisation at index visit, but when stratified, this effect was only observed in early phase. Subsequent hospitalisation was more likely in Asian patients (aOR 3.1; 95% CI 1.1 to 8.7) in comparison with NHW patients. Subsequent ED visit was more likely in Blacks and Hispanic/Latino patients in late phase.

CONCLUSION

We found significant differences in ED outcomes that are not explained by comorbidity burden. The gap decreased but persisted during the later phase in 2020.

Primer on Logistic Regression for Emergency Care Researchers

BACKGROUND

Logistic regression plays a fundamental role in the production of decision rules, risk assessment, and in establishing cause and effect relationships. This primer is aimed at novice researchers with minimal statistical expertise.

OBJECTIVE

Introduce the logit equation and provide a hands-on example to facilitate understanding of its benefits and limitations.

DISCUSSION

This primer reviews the mathematical basis of a logit equation by comparing and contrasting it with the simple straight-line (linear) equation. After gaining an understanding of the meaning of beta coefficients, readers are encouraged to download a free statistical program and database to produce a logistic regression analysis. Using this example, the narrative then discusses commonly used methods to describe model fitness, including the C-statistic, chi square, Akaike and Bayesian Information Criteria, McFadden's pseudo R², and the Hosmer-Lemeshow test. The authors provide a how-to discussion for variable selection and estimate of sample size. However, logistic regression alone can seldom establish causal inference without further steps to explore the often complex relationship amongst variables and outcomes, such as with the use of a directed acyclic graphs. We present key elements that generally should be considered when appraising an article that uses logistic regression. This primer provides a basic understanding of the theory, hands-on construction, model analysis, and limitations of logistic regression in emergency care research.

CONCLUSIONS

Logistic regression can provide information about the association of independent variables with important clinical outcomes, which can be the first step to show predictiveness or causation of variables on the outcomes of interest. © 2022 Elsevier Inc.

The Tip of the Spear: Emergency Medicine and Missouri's Response to the COVID-19 Pandemic

The COVID-19 pandemic provided the specialty of emergency medicine the opportunity to showcase what many knew all along: emergency physicians (EP) are well suited to deal with the unknown and can quickly adapt even with incomplete or limited information and resources. Emergency physicians in Missouri served in integral positions locally, nationally and internationally. Missouri EPs published numerous manuscripts on topics from basic science to clinical care. Device innovation also occurred with the development of protective devices for health care workers. As we approach the three-year mark of the COVID-19 pandemic, the burden of clinical care still weighs heavily on EPs. Each wave of the pandemic has brought challenges and spurred EPs to innovate in new ways. As Michigan EP Brian Zink, MD once said "Anyone, Anything, Anytime". These words correctly sum up emergency medicine. When others hesitated to care for COVID-19 patients, EPs stepped up despite uncertainty and risks to their own health. Emergency medicine has led the way and continues to innovate and push the envelope of emergency care.

Lessons from the frontline: The value of emergency care processes and data to pandemic responses across the Pacific region

Background

Emergency care (EC) addresses the needs of patients with acute illness and injury, and has fulfilled a critical function during the COVID-19 pandemic. 'Processes' (e.g. triage) and 'data' (e.g. surveillance) have been nominated as essential building blocks for EC systems. This qualitative research sought to explore the impact of the pandemic on EC clinicians across the Pacific region, including the contribution of EC building blocks to effective responses.

Methods

The study was conducted in three phases, with data obtained from online support forums, key informant interviews and focus group discussions. There were 116 participants from more than 14 Pacific Island Countries and Territories. A phenomenological approach was adopted, incorporating inductive and deductive methods. The deductive thematic analysis utilised previously identified building blocks for Pacific EC. This paper summarises findings for the building blocks of 'processes' and 'data'.

Findings

Establishing triage and screening capacity, aimed at assessing urgency and transmission risk respectively, were priorities for EC clinicians. Enablers included support from senior hospital leaders, previous disaster experience and consistent guidelines. The introduction of efficient patient flow processes, such as streaming, proved valuable to emergency departments, and checklists and simulation were useful implementation strategies. Some response measures impacted negatively on non-COVID patients, and proactive approaches were required to maintain 'business as usual'. The pandemic also highlighted the value of surveillance and performance data.

Interpretation

Developing effective processes for triage, screening and streaming, among other areas, was critical to an effective EC response. Beyond the pandemic, strengthening processes and data management capacity will build resilience in EC systems.

Funding

Phases 1 and 2A of this study were part of an Epidemic Ethics/World Health Organization (WHO) initiative, supported by Foreign, Commonwealth and Development Office/Wellcome Grant 214711/Z/18/Z. Co-funding for this research was received from the Australasian College for Emergency Medicine Foundation via an International Development Fund Grant.

Age-related differences in symptoms in older emergency department patients with COVID-19: Prevalence and outcomes in a multicenter cohort

BACKGROUND

Older adults represent a disproportionate share of severe COVID-19 presentations and fatalities, but we have limited understanding of the differences in presentation by age and the association between less typical emergency department (ED) presentations and clinical outcomes.

METHODS

This retrospective cohort study used the RECOVER Network registry, a research collaboration of 86 EDs in 27 U.S. states. We focused on encounters with a positive nasopharyngeal swab for SARS-CoV-2, and described their demographics, clinical presentation, and outcomes. Sequential multivariable logistic regressions examined the strength of association between age cohort and outcomes.

RESULTS

Of 4536 encounters, median patient age was 55 years, 49% were women, and 34% were non-Hispanic Black persons. Cough was the most common presenting complaint across age groups (18-64, 65-74, and 75+): 71%, 67%, and 59%, respectively (p < 0.001). Neurological symptoms, particularly altered mental status, were more common in older adults (2%, 11%, 26%; p < 0.001). Patients 75+ had the greatest odds of ED index visit admission of all age groups (adjusted odds ratio [aOR] 6.66; 95% CI 5.23-8.56), 30-day hospitalization (aOR 7.44; 95% CI 5.63-9.99), and severe COVID-19 (aOR 4.26; 95% CI 3.45-5.27). Compared to individuals with alternate presentations and adjusting for age, patients with typical symptoms (fever, cough and/or shortness of breath) had similar odds of ED index visit admission (aOR 1.01; 95% CI 0.81-1.24), potentially higher odds of 30-day hospitalization (aOR 1.23; 95% CI 1.00-1.53), and greater odds of severe COVID-19 (aOR 1.46; 95% CI 1.12-1.90).

CONCLUSIONS

Older patients with COVID-19 are more likely to have presentations without the most common symptoms. However, alternate presentations of COVID-19 in older ED patients are not associated with greater odds of mechanical ventilation and/or death. Our data highlights the importance of a liberal COVID-19 testing strategy among older ED patients to facilitate accurate diagnoses and timely treatment and prophylaxis.

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

BACKGROUND

COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020.

OBJECTIVES

To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19.

SEARCH METHODS

We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions.

SELECTION CRITERIA

Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable.

MAIN RESULTS

We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%.

AUTHORS' CONCLUSIONS

Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network

OBJECTIVES

Estimating mortality risk in hospitalised SARS-CoV-2+ patients may help with choosing level of care and discussions with patients. The Coronavirus Clinical Characterisation Consortium Mortality Score (4C Score) is a promising COVID-19 mortality risk model. We examined the association of risk factors with 30-day mortality in hospitalised, full-code SARS-CoV-2+ patients and investigated the discrimination and calibration of the 4C Score. This was a retrospective cohort study of SARS-CoV-2+ hospitalised patients within the RECOVER (REgistry of suspected COVID-19 in EmeRgency care) network.

SETTING

99 emergency departments (EDs) across the USA.

PARTICIPANTS

Patients ≥18 years old, positive for SARS-CoV-2 in the ED, and hospitalised.

PRIMARY OUTCOME

Death within 30 days of the index visit. We performed logistic regression analysis, reporting multivariable risk ratios (MVRRs) and calculated the area under the ROC curve (AUROC) and mean prediction error for the original 4C Score and after dropping the C reactive protein (CRP) component.

RESULTS

Of 6802 hospitalised patients with COVID-19, 1149 (16.9%) died within 30 days. The 30-day mortality was increased with age 80+ years (MVRR=5.79, 95% CI 4.23 to 7.34); male sex (MVRR=1.17, 1.05 to 1.28); and nursing home/assisted living facility residence (MVRR=1.29, 1.1 to 1.48). The 4C Score had comparable discrimination in the RECOVER dataset compared with the original 4C validation dataset (AUROC: RECOVER 0.786 (95% CI 0.773 to 0.799), 4C validation 0.763 (95% CI 0.757 to 0.769). Score-specific mortalities in our sample were lower than in the 4C validation sample (mean prediction error 6.0%). Dropping the CRP component from the 4C Score did not substantially affect discrimination and 4C risk estimates were now close (mean prediction error 0.7%).

CONCLUSIONS

We independently validated 4C Score as predicting risk of 30-day mortality in hospitalised SARS-CoV-2+ patients. We recommend dropping the CRP component of the score and using our recalibrated mortality risk estimates.

Should COVID-19 symptoms be used to cohort patients in the emergency department? A retrospective analysis

Objective

Methods

Results

Conclusion

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

OBJECTIVE

To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED).

METHODS

We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance.

RESULTS

The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05).

CONCLUSION

A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Implementation of a COVID-19 screening tool in a southern Nigerian tertiary health facility

Screening for coronavirus disease 2019 (COVID-19) in emergency rooms of health facilities during outbreaks prevents nosocomial transmission. However, effective tools adapted for use in African countries are lacking. This study appraised an indigenous screening and triage tool for COVID-19 deployed at the medical emergency room of a Nigerian tertiary facility and determined the predictors of a positive molecular diagnostic test for COVID-19. A cross-sectional study of all patients seen between May and July 2020 at the Accident and Emergency of the University of Benin Teaching Hospital was conducted. Patients with any one of the inputs- presence of COVID-19 symptoms, history of international travel, age 60 years and above, presence of comorbidities and oxygen saturation < 94%- were stratified as high-risk and subjected to molecular testing for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Data was obtained from the screening record book patterned after a modified screening tool for COVID-19, deidentified and entered into IBM-SPSS version 25.0. Binary logistic regression was conducted to determine significant predictors of a positive SARS-CoV-2 test. The level of significance was set at p < 0.05. In total, 1,624 patients were screened. Mean age (standard deviation) was 53.9±18.0 years and 651 (40.1%) were 60 years and above. One or more symptoms of COVID-19 were present in 586 (36.1%) patients. Overall, 1,116 (68.7%) patients were designated high risk and tested for SARS-CoV-2, of which 359 (32.2%) were positive. Additional inputs, besides symptoms, increased COVID-19 detection by 108%. Predictors of a positive test were elderly age [AOR = 1.545 (1.127-2.116)], co-morbidity [AOR = 1.811 (1.296-2.530)] and oxygen saturation [AOR = 3.427 (2.595-4.528)]. This protocol using additional inputs such as oxygen saturation improved upon symptoms-based screening for COVID-19. Models incorporating identified predictors will be invaluable in resource limited settings.

CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19

OBJECTIVES

To develop and validate a clinical risk score that can accurately quantify the probability of SARS-CoV-2 infection in patients presenting to an emergency department without the need for laboratory testing.

DESIGN

Cohort study of participants in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. Regression models were fitted to predict a positive SARS-CoV-2 test result using clinical and demographic predictors, as well as an indicator of local SARS-CoV-2 incidence.

SETTING

32 emergency departments in eight Canadian provinces.

PARTICIPANTS

27 665 consecutively enrolled patients who were tested for SARS-CoV-2 in participating emergency departments between 1 March and 30 October 2020.

MAIN OUTCOME MEASURES

Positive SARS-CoV-2 nucleic acid test result within 14 days of an index emergency department encounter for suspected COVID-19 disease.

RESULTS

We derived a 10-item CCEDRRN COVID-19 Infection Score using data from 21 743 patients. This score included variables from history and physical examination and an indicator of local disease incidence. The score had a c-statistic of 0.838 with excellent calibration. We externally validated the rule in 5295 patients. The score maintained excellent discrimination and calibration and had superior performance compared with another previously published risk score. Score cut-offs were identified that can rule-in or rule-out SARS-CoV-2 infection without the need for nucleic acid testing with 97.4% sensitivity (95% CI 96.4 to 98.3) and 95.9% specificity (95% CI 95.5 to 96.0).

CONCLUSIONS

The CCEDRRN COVID-19 Infection Score uses clinical characteristics and publicly available indicators of disease incidence to quantify a patient's probability of SARS-CoV-2 infection. The score can identify patients at sufficiently high risk of SARS-CoV-2 infection to warrant isolation and empirical therapy prior to test confirmation while also identifying patients at sufficiently low risk of infection that they may not need testing.

TRIAL REGISTRATION NUMBER

NCT04702945.

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study

OBJECTIVES

Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge.

METHODS

We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches.

RESULTS

Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models.

CONCLUSIONS

A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.

Multicenter Study of Outcomes Among Persons With HIV Who Presented to US Emergency Departments With Suspected SARS-CoV-2

BACKGROUND

There is a need to characterize patients with HIV with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

SETTING

Multicenter registry of patients from 116 emergency departments in 27 US states.

METHODS

Planned secondary analysis of patients with suspected SARS-CoV-2, with (n = 415) and without (n = 25,306) HIV. Descriptive statistics were used to compare patient information and clinical characteristics by SARS-CoV-2 and HIV status. Unadjusted and multivariable models were used to explore factors associated with death, intubation, and hospital length of stay. Kaplan-Meier curves were used to estimate survival by SARS-CoV-2 and HIV infection status.

RESULTS

Patients with both SARS-CoV-2 and HIV and patients with SARS-CoV-2 but without HIV had similar admission rates (62.7% versus 58.6%, P = 0.24), hospitalization characteristics [eg, rates of admission to the intensive care unit from the emergency department (5.0% versus 6.3%, P = 0.45) and intubation (10% versus 13.3%, P = 0.17)], and rates of death (13.9% versus 15.1%, P = 0.65). They also had a similar cumulative risk of death (log-rank P = 0.72). However, patients with both HIV and SARS-CoV-2 infections compared with patients with HIV but without SAR-CoV-2 had worsened outcomes, including increased mortality (13.9% versus 5.1%, P < 0.01, log-rank P < 0.0001) and their deaths occurred sooner (median 11.5 versus 34 days, P < 0.01).

CONCLUSIONS

Among emergency department patients with HIV, clinical outcomes associated with SARS-CoV-2 infection are not worse when compared with patients without HIV, but SARS-CoV-2 infection increased the risk of death in patients with HIV.

Viral Coinfection is Associated with Improved Outcomes in Emergency Department Patients with SARS-CoV-2

Introduction

Coinfection with severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) and another virus may influence the clinical trajectory of emergency department (ED) patients. However, little empirical data exists on the clinical outcomes of coinfection with SARS-CoV-2

Methods

In this retrospective cohort analysis, we included adults presenting to the ED with confirmed, symptomatic coronavirus 2019 who also underwent testing for additional viral pathogens within 24 hours. To investigate the association between coinfection status with each of the outcomes, we performed logistic regression.

Results

Of 6,913 ED patients, 5.7% had coinfection. Coinfected individuals were less likely to experience index visit or 30-day hospitalization (odds ratio [OR] 0.57; 95% confidence interval [CI], 0.36–0.90 and OR 0.39; 95% CI, 0.25–0.62, respectively).

Conclusion

Coinfection is relatively uncommon in symptomatic ED patients with SARS-CoV-2 and the clinical short- and long-term outcomes are more favorable in coinfected individuals.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Inter-rater reliability and prospective validation of a clinical prediction rule for SARS-CoV-2 infection

Objectives

Accurate estimation of the risk of SARS‐CoV‐2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13‐item CORC (COVID [or coronavirus] Rule‐out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter‐rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt.

Methods

This noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS‐CoV‐2 test result and confidence (0%–100%). The criterion standard for SARS‐CoV‐2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs).

Results

For 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579–0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591–0.697). The agreement for clinician gestalt binary determination of SARs‐CoV‐2 status was κ = 0.534 (95% CI = 0.437–0.632) with median confidence of 76% (first–third quartile = 66–88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR−) of 0.24 (95% CI = 0.10–0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS‐CoV‐2 test was 58% (95% CI = 28%–85%) and positive LR was 13.1 (95% CI = 4.5–37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR− of 0.57 (95% CI = 0.39–0.74).

Conclusion

In this prospective study, the CORC score and rule demonstrated good inter‐rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs‐CoV‐2 infection.