Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study

Abstract

Introduction Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). Methods In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Results We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. Conclusion The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.

External validation of the 4C Mortality Score for patients with COVID-19 and pre-existing cardiovascular diseases/risk factors

“…On the other hand, a recent study adopted a composite endpoint of mortality and admission to the medium/intensive care unit instead of IMV. 14 In this study, the 4C Mortality Score was not superior to the RISE UP score in predicting the composite endpoint. Although admission to the medium/intensive care unit generally reflects deterioration of general condition, including respiratory status, the difference in outcomes may explain the inconsistency of the results with ours.…”

“…More recently, the performance of the 4C Mortality Score was tested in a single medical centre in the Netherlands; however, there was lack of generalisability and missing information on ethnicity. 14 At this point, it is noteworthy that the 4C Mortality Score performed well in the cohort of patients from the Japanese nationwide registry which has different features, including ethnicity, comorbidities and healthcare system. Despite these differences between the two cohorts, the 4C Mortality Score showed similar discriminatory ability to that in the previous study (AUC: 0.786).…”

“…We reviewed the studies comparing multiple prediction models in patients with COVID-19 and selected three scores that have previously shown good discriminatory ability in comparison with the 4C score: the Risk Stratification in the EmergencyDepartment in Acutely Ill Older Patients (RISE UP) score, 11 the Japan Respiratory Society community associated pneumonia severity index (A-DROP scoring system) 12 and the Rapid Emergency Medicine Score (REMS). 13 These three scores were not COVID-19-specific risk scores but were reported to have the highest discriminatory ability in predicting short-term mortality in patients with COVID-19, with an area under the receiver operating characteristic curve (AUC) of 0.83 (RISE UP score), 14 0.87 (A-DROP) 15 and 0.76 (REMS). 16 We calculated the RISE UP score based on age, vital signs, serum albumin, blood urea nitrogen, lactate dehydrogenase and bilirubin, as proposed in the original score.…”

Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection

“…A retrospective study of 403 adult patients seen in the Emergency Department in a combined secondary/tertiary care center in the Netherlands for the first wave of the pandemic (March through May, 2020) tested 11 prediction models of 30-day mortality as the primary outcome. [18]. The investigators identified two prediction models that performed best: 1) RISE-UP (acronym for Risk Stratification in the Emergency Department in Acutely Ill Older Patients) score, which included age, heart rate, mean arterial pressure, respiratory rate, oxygen saturation, Glasgow Coma Scale (GCS), BUN, bilirubin, albumin, and lactate dehydrogenase; and 2) 4-C (Coronavirus Clinical Characterization Consortium) score, which had been tested previously in the United Kingdom [19], and included age, sex, co-morbidity, RR, GCS, O 2 saturation, BUN, and CRP.…”

“…Sixty-one percent were older than 65 years; 48% were treated with oxygen � 4 Liters/minute within the first 24 hours of their hospital stay; radiographic pneumonia was present in 61%; ischemia in 37%; CRP > 10 mg/L was present in 47%; hospital length of stay was 12 ± 16 days; and in-hospital mortality was 36%. The mean number of co-morbid conditions was 2.5 ± 1.7; percentages (in parentheses) were as follows: hypertension (72), insulin-dependent diabetes (23), obesity (52), COPD (12), asthma (14), chronic liver disease (1), history of deep venous thrombosis (7), atrial fibrillation (13), coronary artery disease (23), chronic kidney disease (18), history of cerebrovascular disease (15), history of heart failure (23), history of malignancy (4). For some variables fewer than 100 data values were available; among these were (available numbers for analysis in parentheses): race/ ethnicity (81), SES (98), ischemia (99), pneumonia (98), CRP (95), BNP (42).…”