RESUMO
ObjectivesAlthough a set of computer-aided risk scoring systems (CARSS), that use the National Early Warning Score and routine blood tests results, have been validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital, little is known about their performance for COVID-19 patients. We compare the performance of CARSS in unplanned admissions with COVID-19 during the first phase of the pandemic. Designa retrospective cross-sectional study SettingTwo acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively. ParticipantsAdult (>=18 years) non-elective admissions discharged between 11-March-2020 to 13-June-2020 with an index NEWS electronically recorded within {+/-}24 hours. We assessed the performance of all four risk score (for sepsis: CARS_N, CARS_NB; for mortality: CARM_N, CARM_NB) according to discrimination (c-statistic) and calibration (graphically) in predicting the risk of COVID-19 and in-hospital mortality. ResultsThe risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89). For predicting in-hospital mortality, the CARM_NB model had the highest discrimination 0.84 (0.82 to 0.75) and calibration slope 0.89 (0.81 to 0.98). ConclusionsTwo of the computer-aided risk scores (CARS_N and CARM_NB) are reasonably accurate for predicting the risk of COVID-19 and in-hospital mortality, respectively. They may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions because they are automated and require no additional data collection. Article SummaryO_LIIn this study, we found that two of the automated computer-aided risk scores are reasonably accurate for predicting the risk of COVID-19 and in-hospital mortality, respectively. C_LIO_LIThey may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions because they are automated and require no additional data collection. C_LIO_LIAlthough we focused on in-hospital mortality (because we aimed to aid clinical decision making in the hospital), the impact of this selection bias needs to be assessed by capturing out-of-hospital mortality by linking death certification data and hospital data. C_LIO_LIWe identified COVID-19 based on ICD-10 code U071 which was determined by COVID-19 swab test results (hospital or community) and clinical judgment and so our findings are constrained by the accuracy of these methods C_LI
RESUMO
ObjectivesTo consider the potential of the National Early Warning Score (NEWS2) for COVID-19 risk prediction on unplanned admission to hospital. DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admission to hospital. SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset. ParticipantsUnplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within {+/-}24 hours of admission. We used logistic regression modelling to predict the risk of COVID-19 using NEWS2 (Model M0) versus enhanced cNEWS models which included age + sex (model M1) + subcomponents (including diastolic blood pressure + oxygen flow rate + oxygen scale) of NEWS2 (model M2). The ICD-10 code U071 was used to identify COVID-19 admissions. Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 [≥]5. ResultsThe prevalence of COVID-19 was higher in SH (11.0%=277/2520) than YH (8.7%=343/3924) with higher index NEWS2 (3.2 vs 2.8) but similar in-hospital mortality (8.4% vs 8.2%). The c-statistics for predicting COVID-19 for cNEWS models (M1,M2) was substantially better than NEWS2 alone (M0) in development (M2: 0.78 (95%CI 0.75-0.80) vs M0 0.71 (95%CI 0.68-0.74)) and validation datasets (M2: 0.72 (95%CI 0.69-0.75) vs M0 0.65 (95%CI 0.61-0.68)). Model M2 had better calibration than Model M0 with improved sensitivity (M2: 57% (95%CI 51%-63%) vs M0 44% (95%CI 38%-50%)) and similar specificity (M2: 76% (95%CI 74%-78%) vs M0 75% (95%CI 73%-77%)) for validation dataset at NEWS2[≥]5. ConclusionsModel M2 is reasonably accurate for predicting the on-admission risk of COVID-19. It may be clinically useful for an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.
RESUMO
ObjectivesThere are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with the novel coronavirus SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and latest version of the National Early Warning Score (NEWS2). DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admissions to hospital. SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset. ParticipantsUnplanned adult medical admissions discharged over three months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within {+/-}24 hours and/or blood test results within {+/-}96 hours of admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: 1) CARMc19_N: age + sex + NEWS2 including subcomponents + COVID19; 2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically), and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+. ResultsThe risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for Model CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB = 0.88 (95%CI 0.86 to 0.90) vs CARMc19_N = 0.86 (95%CI 0.83 to 0.88)). Both models had good internal and external calibration (CARMc19_NB: 1.01 (95%CI 0.88 vs 1.14) & CARMc19_N: 0.95 (95%CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model CARMc19_NB had better sensitivity and similar specificity. ConclusionsWe have developed a validated CARMc19 score with good performance characteristics for predicting the risk of in-hospital mortality following an emergency medical admission using the patients first, electronically recorded vital signs and blood tests results. Since the CARMc19 scores place no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.
RESUMO
BackgroundAlthough the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring for deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We analysed the performance of National Early Warning Score (NEWS2) during the first phase of the COVID-19 pandemic. MethodsAdult non-elective admissions discharged between 11-March-2020 to 13-June-2020 with an index NEWS2 electronically recorded within {+/-}24 hours of admission are used to predict mortality at four time points (in-hospital, 24hours, 48hours, and 72hours) in COVID-19 versus non-COVID-19 admissions. ResultsOut of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7yrs), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (e.g.NEWS2=5: 36% vs 9%). InterpretationNEWS2 is a valid predictor of the mortality risk but substantially underestimates the absolute mortality risk in COVID-19 patients. Clinical staff and escalation protocols based on NEWS2 need to make note of this finding.
