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

Abstract

IntroductionCoronavirus disease 2019 (COVID-19) has a high burden on the healthcare system and demands information on the outcome early after admission to the emergency department (ED). Previously developed prediction models may assist in triaging patients when allocating healthcare resources. We aimed to assess the value of several prediction models when applied to COVID-19 patients in the ED.MethodsAll consecutive COVID-19 patients who visited the ED of a combined secondary/tertiary care center were included. Prediction models were selected based on their feasibility. The primary outcome was 30-day mortality, secondary outcomes were 14-day mortality, and a composite outcome of 30-day mortality and admission to the medium care unit (MCU) or the intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC).ResultsA total of 403 ED patients were diagnosed with COVID-19. Within 30 days, 95 patients died (23.6%), 14-day mortality was 19.1%. Forty-eight patients (11.9%) were admitted to the MCU, 66 patients (16.4%) to the ICU and 152 patients (37.7%) met the composite endpoint. Eleven models were included: RISE UP score, 4C mortality score, CURB-65, MEWS, REMS, abbMEDS, SOFA, APACHE II, CALL score, ACP index and Host risk factor score. The RISE UP score and 4C mortality score showed a very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84 respectively, 95% CI 0.79-0.88 for both), for 14-day mortality (AUC 0.83, 95% CI: 0.79-0.88, for both) and for the composite outcome (AUC 0.79 and 0.77 respectively, 95% CI 0.75-0.84). The discriminatory performance of the RISE UP score and 4C mortality score was significantly higher compared to that of the other models.ConclusionThe RISE UP score and 4C mortality score have good discriminatory performance in predicting adverse outcome in ED patients with COVID-19. These prediction models can be used to recognize patients at high risk for short-term poor outcome and may assist in guiding clinical decision-making and allocating healthcare resources.

External Validation of the 4C Mortality Score among COVID-19 Patients Visiting the Emergency Department or admitted to Hospital in Ontario, Canada

“…The AUC we observed was higher than in the original research, which is unusual for external validation, but this was also true in other validation studies done in the United Kingdom and the Netherlands 8,10 . The higher discriminability in our study may in part because we examined a more heterogeneous cohort that included patients who visited the emergency department but were not admitted to hospital.…”

Serial EXTEM, FIBTEM, and tPA Rotational Thromboelastometry Observations in the Maastricht Intensive Care COVID Cohort—Persistence of Hypercoagulability and Hypofibrinolysis Despite Anticoagulation

“…Therefore, only relatively healthy patients were transferred to the ICU for mechanical ventilation. A recent study in our center showed that, of all admitted COVID-19 patients, 35.2% had some form of treatment restrictions ( 46 ). This shows that COVID-19 related coagulopathy is not limited to patients with multiple comorbidities since our study sample with relatively healthy individuals presents with significant hemostatic abnormalities.…”

External validation of 4C ISARIC mortality score in the setting of a Saudi Arabian ICU. Retrospective study

“…In view of the variability of presenting pictures of COVID-19 and their progression, and the increasing burden the pandemic imposes on healthcare systems (9), the necessity arose for a tool of risk stratification, to allow early identification of COVID-19 patients at higher risk of mortality, using readily available objective criteria (8)(9)(10). A recent large size prospective cohort study (8) that seemed to outperform other risk stratification models (11).…”

“…In view of the variability of presenting pictures of COVID-19 and their progression, and the increasing burden the pandemic imposes on healthcare systems (9), the necessity arose for a tool of risk stratification, to allow early identification of COVID-19 patients at higher risk of mortality, using readily available objective criteria (8)(9)(10). A recent large size prospective cohort study (8) utilized The International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol to develop and validate such a tool (ISARIC 4C Mortality Score, hereafter referred to as ISARIC) that seemed to outperform other risk stratification models (11).…”