Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score

Abstract

Objective Develop and validate a risk score using variables available during an Emergency Department (ED) encounter to predict adverse events among patients with suspected COVID-19. Methods A retrospective cohort study of adult visits for suspected COVID-19 between March 1 – April 30, 2020 at 15 EDs in Southern California. The primary outcomes were death or respiratory decompensation within 7-days. We used least absolute shrinkage and selection operator (LASSO) models and logistic regression to derive a risk score. We report metrics for derivation and validation cohorts, and subgroups with pneumonia or COVID-19 diagnoses. Results 26,600 ED encounters were included and 1079 experienced an adverse event. Five categories (comorbidities, obesity/BMI ≥ 40, vital signs, age and sex) were included in the final score. The area under the curve (AUC) in the derivation cohort was 0.891 (95% CI, 0.880–0.901); similar performance was observed in the validation cohort (AUC = 0.895, 95% CI, 0.874–0.916). Sensitivity ranging from 100% (Score 0) to 41.7% (Score of ≥15) and specificity from 13.9% (score 0) to 96.8% (score ≥ 15). In the subgroups with pneumonia ( n = 3252) the AUCs were 0.780 (derivation, 95% CI 0.759–0.801) and 0.832 (validation, 95% CI 0.794–0.870), while for COVID-19 diagnoses ( n = 2059) the AUCs were 0.867 (95% CI 0.843–0.892) and 0.837 (95% CI 0.774–0.899) respectively. Conclusion Physicians evaluating ED patients with pneumonia, COVID-19, or symptoms suspicious for COVID-19 can apply the COVAS score to assist with decisions to hospitalize or discharge patients during the SARS CoV-2 pandemic.

Clinical characteristics of COVID‐19 patients evaluated in the emergency department: A retrospective cohort study of 801 cases

“…Innovative tools were developed early on in the pandemic to identify ambulatory patients who may be at risk of critical illness or hospitalization after presenting with symptoms concerning for COVID‐19. The CoVa and COVAS scores studied by Sun et al 13 and Sharp et al, 15 respectively, are 2 such tools. These tools included either ED visits and outpatient respiratory tent visits (CoVa) or ED visits (COVAS) in patients with symptoms concerning for COVID‐19.…”

“… 6 , 7 , 8 , 9 , 10 , 11 , 12 When ED patients have been directly studied, the study population also included non‐ED patients or included in the analysis a majority of patients not confirmed to have COVID‐19. 13 , 14 , 15 Many models have examined population‐level associations, or included variables driven primarily by data‐collection convenience, emphasizing comorbidities or numerous laboratory values over the patient‐level clinical features, examination findings, and diagnostic data typically available in the ED. 16 , 17 These approaches are understandable given the urgent need to address COVID‐19, but it is anticipated that use of more granular ED data will offer stronger associations between patient characteristics and clinically important outcomes.…”

Applications of Big Data Analytics to Control COVID-19 Pandemic

“…The authors in Reference [ 52 ] are interested in developing and validating the risk score to predict adverse events among patients suspected of having COVID-19. They conducted a retrospective cohort study of adult visits to the emergency department.…”

“…Analyzing this data will assist in predicting future events, understanding the current situation, and making several decisions. The medical data can be obtained from many sources, as it can be collected using sensors of wearable/mobile devices or medical devices [39,42,46,53], online questionnaires [55,59], websites or mobile apps [40,41,43,45,60,61], hospital records [50][51][52]62,64], local and international health systems [44,47,57,63,67], interviews and case study samples [54], and data on open databases or social media websites [58]. Numerous data can be utilized in the medical health sector.…”

“…Table 6 shows examples of each type of data discussed in this paragraph. Comorbidities [42,44,47,[50][51][52]60,61,64] Medical history [45,60] Routinely taken medications [42,59] Laboratory findings [47,50,51] CT scans [39,48,50,54] Required ICU [41] ICU length of stay [41] Readmission status [41] COVID-19 data…”