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Introduction: Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient’s disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission. Methods: A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients’ need for hospital admission.Results: In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p < 0.001). Specificity ranged from 56.4 to 67.0%. All healthcare providers outperformed MEWS ≥ 3 score on predicting hospital admission (sensitivity 80.0-91.9% versus 44.0%; all p < 0.001). Predictions for ward admissions specifically were significantly more accurate than MEWS (specificity 94.7–95.9% versus 60.6%, all p < 0.001). Conclusions: Healthcare providers can accurately predict the need for hospital admission, and all providers outperformed the MEWS score.

Background: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients.Methods: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 `wave' in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values.Results: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80-0.84]) compared to the National early warning score (0.72 [0.69-0.74]) and the Modified early warning score (0.67 [0.65-0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [- 0.09 to 0.14], slope = 0.79 [0.73-0.86]).Conclusions: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.

Background: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients. Methods: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 ‘wave’ in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values. Results: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80–0.84]) compared to the National early warning score (0.72 [0.69–0.74]) and the Modified early warning score (0.67 [0.65–0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [− 0.09 to 0.14], slope = 0.79 [0.73–0.86]). Conclusions: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed., Pattern Recognition and Bioinformatics

BACKGROUND: The severity of COVID-19 after SARS-CoV-2 infection is unpredictable. Angiotensin-converting enzyme-2 (ACE2) is the receptor responsible for coronavirus binding, while subsequent cell entry relies on priming by the serine protease TMPRSS2 (transmembrane protease, serine 2). Although renin-angiotensin-aldosterone-system (RAAS) blockers have been suggested to upregulate ACE2, their use in COVID-19 patients is now considered well tolerated. The aim of our study was to investigate parameters that determine COVID-19 severity, focusing on RAAS-components and variation in the genes encoding for ACE2 and TMPRSS2. METHODS: Adult patients hospitalized due to SARS-CoV-2 infection between May 2020 and October 2020 in the Haga Teaching Hospital were included, and soluble ACE2 (sACE2), renin, aldosterone (in heparin plasma) and polymorphisms in the ACE2 and TMPRSS2 genes (in DNA obtained from EDTA blood) were determined. MEASUREMENTS AND MAIN RESULTS: Out of the 188 patients who were included, 60 were defined as severe COVID-19 (ICU and/or death). These patients more often used antidiabetic drugs, were older, had higher renin and sACE2 levels, lower aldosterone levels and a lower aldosterone/renin ratio. In addition, they displayed the TMPRSS2-rs2070788 AA genotype less frequently. No ACE2 polymorphism-related differences were observed. Multivariate regression analysis revealed independent significance for age, sACE2, the aldosterone/renin ratio, and the TMPRSS2 rs2070788 non-AA genotype as predictors of COVID-19 severity, together yielding a C-index of 0.79. Findings were independent of the use of RAAS blockers. CONCLUSION: High sACE2, a low aldosterone/renin ratio and having the TMPRSS2 rs2070788 non-AA genotype are novel independent determinants that may help to predict COVID-19 disease severity. TRIAL REGISTRATION: retrospectively registered.