External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study.

BACKGROUND: Tools proposed to triage ED acuity in suspected COVID-19 were derived and validated in higher income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa. METHODS: An observational cohort study using routinely collected data from EDs across the Western Cape, from 27 August 2020 to 11 March 2022, was conducted to assess the performance of the PRIEST (Pandemic Respiratory Infection Emergency System Triage) tool, NEWS2 (National Early Warning Score, version 2), TEWS (Triage Early Warning Score), the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS (Pandemic Medical Early Warning Score) in suspected COVID-19. The primary outcome was intubation or non-invasive ventilation, death or intensive care unit admission at 30 days. RESULTS: Of the 446 084 patients, 15 397 (3.45%, 95% CI 34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity of 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) of 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST scores achieved good estimated discrimination (C-statistic 0.79 to 0.82) and identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.41 to 0.64. Use of the tools at recommended thresholds would have more than doubled admissions, with only a 0.01% reduction in false negative triage. CONCLUSION: No risk score outperformed existing clinical decision-making in determining the need for inpatient admission based on prediction of the primary outcome in this setting. Use of the PRIEST score at a threshold of one point higher than the previously recommended best approximated existing clinical accuracy.

The utility of the Rapid Emergency Medicine Score (REMS) compared with three other early warning scores in predicting in-hospital mortality among COVID-19 patients in the emergency department: a multicenter validation study.

BACKGROUND: Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes of COVID-19 in the Emergency Department (ED), including the quick Sequential Organ Failure Assessment (qSOFA), the Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has not been widely validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of qSOFA, MEWS, and NEWS for predicting mortality in emergency COVID-19 patients. METHODS: We conducted a multi-center retrospective study at five EDs of various levels of care in Thailand. Adult patients visiting the ED who tested positive for COVID-19 prior to ED arrival or within the index hospital visit between January and December 2021 were included. Their EWSs at ED arrival were calculated and analysed. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation. RESULTS: A total of 978 patients were included in the study; 254 (26%) died at hospital discharge, and 155 (15.8%) were intubated. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.771 (95% confidence interval (CI) 0.738, 0.804)), which was significantly higher than qSOFA (AUROC 0.620 (95%CI 0.589, 0.651); p < 0.001), MEWS (AUROC 0.657 (95%CI 0.619, 0.694); p < 0.001), and NEWS (AUROC 0.732 (95%CI 0.697, 0.767); p = 0.037). REMS was also the best EWS in terms of calibration, overall model performance, and balanced diagnostic accuracy indices at its optimal cutoff. REMS also performed better than other EWSs for mechanical ventilation. CONCLUSION: REMS was the early warning score with the highest prognostic utility as it outperformed qSOFA, MEWS, and NEWS in predicting in-hospital mortality in COVID-19 patients in the ED.

Impact of frailty on the performance of the National Early Warning Score 2 to predict poor outcome in patients hospitalised due to COVID-19.

BACKGROUND: The National Early Warning Score 2 (NEWS2) is a scoring tool predictive of poor outcome in hospitalised patients. Older patients with COVID-19 have increased risk of poor outcome, but it is not known if frailty may impact the predictive performance of NEWS2. We aimed to investigate the impact of frailty on the performance of NEWS2 to predict in-hospital mortality in patients hospitalised due to COVID-19. METHODS: We included all patients admitted to a non-university Norwegian hospital due to COVID-19 from 9 March 2020 until 31 December 2021. NEWS2 was scored based on the first vital signs recorded upon hospital admission. Frailty was defined as a Clinical Frailty Scale score ≥ 4. The performance of a NEWS2 score ≥ 5 to predict in-hospital mortality was assessed with sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) according to frailty status. RESULTS: Out of 412 patients, 70 were aged ≥ 65 years and with frailty. They presented less frequently with respiratory symptoms, and more often with acute functional decline or new-onset confusion. In-hospital mortality was 6% in patients without frailty, and 26% in patients with frailty. NEWS2 predicted in-hospital mortality with a sensitivity of 86%, 95% confidence interval (CI) 64%-97% and AUROC 0.73, 95% CI 0.65-0.81 in patients without frailty. In older patients with frailty, sensitivity was 61%, 95% CI 36%-83% and AUROC 0.61, 95% CI 0.48-0.75. CONCLUSION: A single NEWS2 score at hospital admission performed poorly to predict in-hospital mortality in patients with frailty and COVID-19 and should be used with caution in this patient group. Graphical abstract summing up study design, results and conclusion.

Evaluation of Early Warning Scores on In-Hospital Mortality in COVID-19 Patients: A Tertiary Hospital Study from Taiwan.

Coronavirus disease 2019 (COVID-19) remains a global pandemic. Early warning scores (EWS) are used to identify potential clinical deterioration, and this study evaluated the ability of the Rapid Emergency Medicine score (REMS), National Early Warning Score (NEWS), and Modified EWS (MEWS) to predict in-hospital mortality in COVID-19 patients. This study retrospectively analyzed data from COVID-19 patients who presented to the emergency department and were hospitalized between 1 May and 31 July 2021. The area under curve (AUC) was calculated to compare predictive performance of the three EWS. Data from 306 COVID-19 patients (61 ± 15 years, 53% male) were included for analysis. REMS had the highest AUC for in-hospital mortality (AUC: 0.773, 95% CI: 0.69-0.85), followed by NEWS (AUC: 0.730, 95% CI: 0.64-0.82) and MEWS (AUC: 0.695, 95% CI: 0.60-0.79). The optimal cut-off value for REMS was 6.5 (sensitivity: 71.4%; specificity: 76.3%), with positive and negative predictive values of 27.9% and 95.4%, respectively. Computing REMS for COVID-19 patients who present to the emergency department can help identify those at risk of in-hospital mortality and facilitate early intervention, which can lead to better patient outcomes.

Performance of digital early warning score (NEWS2) in a cardiac specialist setting: retrospective cohort study.

INTRODUCTION: Patients with cardiovascular diseases (CVD) are at significant risk of developing critical events. Early warning scores (EWS) are recommended for early recognition of deteriorating patients, yet their performance has been poorly studied in cardiac care settings. Standardisation and integrated National Early Warning Score 2 (NEWS2) in electronic health records (EHRs) are recommended yet have not been evaluated in specialist settings. OBJECTIVE: To investigate the performance of digital NEWS2 in predicting critical events: death, intensive care unit (ICU) admission, cardiac arrest and medical emergencies. METHODS: Retrospective cohort analysis. STUDY COHORT: Individuals admitted with CVD diagnoses in 2020; including patients with COVID-19 due to conducting the study during the COVID-19 pandemic. MEASURES: We tested the ability of NEWS2 in predicting the three critical outcomes from admission and within 24 hours before the event. NEWS2 was supplemented with age and cardiac rhythm and investigated. We used logistic regression analysis with the area under the receiver operating characteristic curve (AUC) to measure discrimination. RESULTS: In 6143 patients admitted under cardiac specialties, NEWS2 showed moderate to low predictive accuracy of traditionally examined outcomes: death, ICU admission, cardiac arrest and medical emergency (AUC: 0.63, 0.56, 0.70 and 0.63, respectively). Supplemented NEWS2 with age showed no improvement while age and cardiac rhythm improved discrimination (AUC: 0.75, 0.84, 0.95 and 0.94, respectively). Improved performance was found of NEWS2 with age for COVID-19 cases (AUC: 0.96, 0.70, 0.87 and 0.88, respectively). CONCLUSION: The performance of NEWS2 in patients with CVD is suboptimal, and fair for patients with CVD with COVID-19 to predict deterioration. Adjustment with variables that strongly correlate with critical cardiovascular outcomes, that is, cardiac rhythm, can improve the model. There is a need to define critical endpoints, engagement with clinical experts in development and further validation and implementation studies of EHR-integrated EWS in cardiac specialist settings.

Implementation of a digital early warning score (NEWS2) in a cardiac specialist and general hospital settings in the COVID-19 pandemic: a qualitative study.

OBJECTIVES: To evaluate implementation of digital National Early Warning Score 2 (NEWS2) in a cardiac care setting and a general hospital setting in the COVID-19 pandemic. DESIGN: Thematic analysis of qualitative semistructured interviews using the non-adoption, abandonment, scale-up, spread, sustainability framework with purposefully sampled nurses and managers, as well as online surveys from March to December 2021. SETTINGS: Specialist cardiac hospital (St Bartholomew's Hospital) and general teaching hospital (University College London Hospital, UCLH). PARTICIPANTS: Eleven nurses and managers from cardiology, cardiac surgery, oncology and intensive care wards (St Bartholomew's) and medical, haematology and intensive care wards (UCLH) were interviewed and 67 were surveyed online. RESULTS: Three main themes emerged: (1) implementing NEWS2 challenges and supports; (2) value of NEWS2 to alarm, escalate and during the pandemic; and (3) digitalisation: electronic health record (EHR) integration and automation. The value of NEWS2 was partly positive in escalation, yet there were concerns by nurses who undervalued NEWS2 particularly in cardiac care. Challenges, like clinicians' behaviours, lack of resources and training and the perception of NEWS2 value, limit the success of this implementation. Changes in guidelines in the pandemic have led to overlooking NEWS2. EHR integration and automated monitoring are improvement solutions that are not fully employed yet. CONCLUSION: Whether in specialist or general medical settings, the health professionals implementing early warning score in healthcare face cultural and system-related challenges to adopting NEWS2 and digital solutions. The validity of NEWS2 in specialised settings and complex conditions is not yet apparent and requires comprehensive validation. EHR integration and automation are powerful tools to facilitate NEWS2 if its principles are reviewed and rectified, and resources and training are accessible. Further examination of implementation from the cultural and automation domains is needed.

Evaluation of Short-Term Mortality Prediction Using Initial Lactate and NEWS+L at Admission in COVID-19 Patients.

OBJECTIVE: To predict the short-term mortality of the serum lactate level and the National Early Warning Score + lactate (NEWS+L) at the time of first admission to the emergency department in COVID-19 patients. MATERIALS AND METHODS: This retrospective analysis was performed by screening the data of COVID-19 patients over a 6-month period (from January 15, 2021, to June 15, 2021). The demographic, comorbidities, vital parameters, and lactate values, as well as C- reactive protein (CRP), blood urea nitrogen (BUN), and 28-day mortality data were recorded. RESULTS: A total of 70 patients were included in our study. The median (25th - 75th percentile) age was 58 (47.3 - 73.5) years, and 33 (47.1%) patients were female. The mean lactate value was 1.6 (1.2 - 1.98) mmol/L, the mean NEWS was 6 (4-7.75), and the mean NEWS+L was 7.24 ± 2.54. Mortality occurred in 13 (18.2%) of the 70 patients at 28 days. Lactate, NEWS, and NEWS+L had no significant relationship with mortality. None of these parameters was able to predict mortality (P = 0.132, 0.670, and 0.994, respectively). CONCLUSION: Our findings showed that the NEWS+L, NEWS, and lactate level could not predict short-term mortality in COVID-19 patients at the time of first admission.

National early warning score on admission as risk factor for invasive mechanical ventilation in COVID-19 patients: A STROBE-compliant study.

ABSTRACT: The coronavirus disease (COVID-19) has become a global pandemic. Invasive mechanical ventilation is recommended for the management of patients with COVID-19 who have severe respiratory symptoms. However, various complications can develop after its use. The efficient and appropriate management of patients requires the identification of factors associated with an aggravation of COVID-19 respiratory symptoms to a degree where invasive mechanical ventilation becomes necessary, thereby enabling clinicians to prevent such ventilation. This retrospective study included 138 inpatients with COVID-19 at a tertiary hospital. We evaluated the differences in the demographic and clinical data between 27 patients who required invasive mechanical ventilation and 111 patients who did not. Multivariate logistic regression analysis indicated that the duration of fever, national early warning score (NEWS), and lactate dehydrogenase (LDH) levels on admission were significantly associated with invasive mechanical ventilation in this cohort. The optimal cut-off values were: fever duration ≥1 day (sensitivity 100.0%, specificity 54.95%), NEWS ≥7 (sensitivity 72.73%, specificity 92.52%), and LDH >810 mg/dL (sensitivity 56.0%, specificity 90.29%). These findings can assist in the early identification of patients who will require invasive mechanical ventilation. Further studies in larger patient populations are recommended to validate our findings.

Retrospective Study to Identify Risk Factors for Severe Disease and Mortality Using the Modified Early Warning Score in 5127 Patients with COVID-19 Admitted to an Emergency Department in Poland Between March 2020 and April 2021.

BACKGROUND COVID-19, a disease caused by SARS-CoV-2, has posed a threat to global public health. This retrospective study of 5127 patients with COVID-19 admitted to an Emergency Department in Poland between March 2020 and April 2021 aimed to identify risk factors for severe disease and mortality using the modified early warning score (MEWS). MATERIAL AND METHODS The study was based on a retrospective analysis of patients with SARS-CoV-2 infection admitted to the Emergency Department between March 2020 and April 2021. A total of 5127 cases were included in the final analysis. Identifying the group of high-risk patients with COVID-19 was determined based on the MEWS score. RESULTS Most of the patients studied were male (53.38%). The in-hospital mortality rate among the patients was 21.53%. The factors associated with the risk of in-hospital mortality from COVID-19 were age (>60 years, hazard ratio [HR]=2.27, P<0.001), comorbidities (cancer, HR=1.39, P=0.005; heart failure, HR=1.31, P=0.009; renal failure, HR=1.37, P=0.004), higher MEWS score (MEWS ≥5, HR=1.43, P<0.001), higher percentage of lung parenchyma affected (>50%, HR=2.10, P=0.001), and higher respiratory rate (>24 breaths per min, HR=2.10, P<0.001). CONCLUSIONS This study produced real-world data of risk factors for mortality from COVID-19 and the use of the MEWS for a faster identification of patients with COVID-19 requiring more intensive medical care.

National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19 - a prospective cohort study.

BACKGROUND: There is a need for validated clinical risk scores to identify patients at risk of severe disease and to guide decision-making during the covid-19 pandemic. The National Early Warning Score 2 (NEWS2) is widely used in emergency medicine, but so far, no studies have evaluated its use in patients with covid-19. We aimed to study the performance of NEWS2 and compare commonly used clinical risk stratification tools at admission to predict risk of severe disease and in-hospital mortality in patients with covid-19. METHODS: This was a prospective cohort study in a public non-university general hospital in the Oslo area, Norway, including a cohort of all 66 patients hospitalised with confirmed SARS-CoV-2 infection from the start of the pandemic; 13 who died during hospital stay and 53 who were discharged alive. Data were collected consecutively from March 9th to April 27th 2020. The main outcome was the ability of the NEWS2 score and other clinical risk scores at emergency department admission to predict severe disease and in-hospital mortality in covid-19 patients. We calculated sensitivity and specificity with 95% confidence intervals (CIs) for NEWS2 scores ≥5 and ≥ 6, quick Sequential Organ Failure Assessment (qSOFA) score ≥ 2, ≥2 Systemic Inflammatory Response Syndrome (SIRS) criteria, and CRB-65 score ≥ 2. Areas under the curve (AUCs) for the clinical risk scores were compared using DeLong's test. RESULTS: In total, 66 patients (mean age 67.9 years) were included. Of these, 23% developed severe disease. In-hospital mortality was 20%. Tachypnoea, hypoxemia and confusion at admission were more common in patients developing severe disease. A NEWS2 score ≥ 6 at admission predicted severe disease with 80.0% sensitivity and 84.3% specificity (Area Under the Curve (AUC) 0.822, 95% CI 0.690-0.953). NEWS2 was superior to qSOFA score ≥ 2 (AUC 0.624, 95% CI 0.446-0.810, p < 0.05) and other clinical risk scores for this purpose. CONCLUSION: NEWS2 score at hospital admission predicted severe disease and in-hospital mortality, and was superior to other widely used clinical risk scores in patients with covid-19.

Development and validation of an early warning score to identify COVID-19 in the emergency department based on routine laboratory tests: a multicentre case-control study.

OBJECTIVES: Identifying patients with a possible SARS-CoV-2 infection in the emergency department (ED) is challenging. Symptoms differ, incidence rates vary and test capacity may be limited. As PCR-testing all ED patients is neither feasible nor effective in most centres, a rapid, objective, low-cost early warning score to triage ED patients for a possible infection is developed. DESIGN: Case-control study. SETTING: Secondary and tertiary hospitals in the Netherlands. PARTICIPANTS: The study included patients presenting to the ED with venous blood sampling from July 2019 to July 2020 (n=10 417, 279 SARS-CoV-2-positive). The temporal validation cohort covered the period from July 2020 to October 2021 (n=14 080, 1093 SARS-CoV-2-positive). The external validation cohort consisted of patients presenting to the ED of three hospitals in the Netherlands (n=12 061, 652 SARS-CoV-2-positive). PRIMARY OUTCOME MEASURES: The primary outcome was one or more positive SARS-CoV-2 PCR test results within 1 day prior to or 1 week after ED presentation. RESULTS: The resulting 'CoLab-score' consists of 10 routine laboratory measurements and age. The score showed good discriminative ability (AUC: 0.930, 95% CI 0.909 to 0.945). The lowest CoLab-score had high sensitivity for COVID-19 (0.984, 95% CI 0.970 to 0.991; specificity: 0.411, 95% CI 0.285 to 0.520). Conversely, the highest score had high specificity (0.978, 95% CI 0.973 to 0.983; sensitivity: 0.608, 95% CI 0.522 to 0.685). The results were confirmed in temporal and external validation. CONCLUSIONS: The CoLab-score is based on routine laboratory measurements and is available within 1 hour after presentation. Depending on the prevalence, COVID-19 may be safely ruled out in over one-third of ED presentations. Highly suspect cases can be identified regardless of presenting symptoms. The CoLab-score is continuous, in contrast to the binary outcome of lateral flow testing, and can guide PCR testing and triage ED patients.

Comparison of Quick Sequential Organ Failure Assessment (Qsofa) and National Early Warning Score (News) in Covid-19 Patients and its Correlation with the Outcome.

The recent outbreak of COVID 19 is a great threat to public health. Because of limitation of resources, the number of patients that can be monitored and treated in Intensive Care Units is restricted. Hence identifying medical patients at risk of deterioration at the initial stage by means of simple protocols based on physiological parameters is crucial. The qSOFA score was introduced as a rapid bedside clinical score to identify patients with a suspected infection that are at greater risk for a poor outcome. The National Early Warning Score (NEWS) was developed to improve the detection of and response to clinical deterioration in patients with acute illness. There is paucity of literature regarding the use of these scores in patients with COVID 19 infection. This study aims at comparing the scoring systems qSOFA and NEWS in the setting of COVID-19 infection and its correlation with the final outcome of the illness. MATERIAL: It is a retrospective study in which patients presenting with COVID 19 infection(diagnosed by RT-PCR testing of nasopharyngeal and oral swab) between April 2021 to June 2021 were included. Scoring was done using both the scores at admission and the patients were followed up till the outcome. Outcome was defined as 5-day, 10-day and 15-day mortality after presentation. Predictive performance was expressed as discrimination (AUC). Subsequently, sensitivity and specificity were calculated. OBSERVATION: A total of 100 patients were included in the study, of whom 17 died within 5 days and 37 died within 10 days and 30 died within 15 days after presentation. q SOFA had the best performance, compared to NEWS (5 day auc : .668, .621, 10-day auc: .580, .569, 15-day auc: .625, .511) with q SOFA having sensitivity of 90.2% while that of news being 95.1% where as specificity of q SOFA is 40.7% and that of NEWS is 47.5%. CONCLUSION: qSOFA score is more accurate in predicting 5, 10 and 15-day mortality than NEWS score in COVID 19 patients. In resource limited settings, it is an inexpensive and simple tool for early identification of high risk COVID 19 patients.

Prognostic value of National Early Warning Scores (NEWS2) and component physiology in hospitalised patients with COVID-19: a multicentre study.

BACKGROUND: National Early Warning Scores (NEWS2) are used to detect all-cause deterioration. While studies have looked at NEWS2, the use of virtual consultation and remote monitoring of patients with COVID-19 mean there is a need to know which physiological observations are important. AIM: To investigate the relationship between outcome and NEWS2, change in NEWS2 and component physiology in COVID-19 inpatients. METHODS: A multi-centre retrospective study of electronically recorded, routinely collected physiological measurements between March and June 2020. First and maximum NEWS2, component scores and outcomes were recorded. Areas under the curve (AUCs) for 2-day, 7-day and 30-day mortality were calculated. RESULTS: Of 1263 patients, 26% died, 7% were admitted to intensive care units (ICUs) before discharge and 67% were discharged without ICU. Of 1071 patients with initial NEWS2, most values were low: 50% NEWS2=0-2, 27% NEWS2=3-4, 14% NEWS2=5-6 and 9% NEWS2=7+. Maximum scores were: 14% NEWS2=0-2, 22% NEWS2=3-4, 17% NEWS2=5-6 and 47% NEWS2=7+. Higher first and maximum scores were predictive of mortality, ICU admission and longer length of stay. AUCs based on 2-day, 7-day, 30-day and any hospital mortality were 0.77 (95% CI 0.70 to 0.84), 0.70 (0.65 to 0.74), 0.65 (0.61 to 0.68) and 0.65 (0.61 to 0.68), respectively. The AUCs for 2-day mortality were 0.71 (0.65 to 0.77) for supplemental oxygen, 0.65 (0.56 to 0.73) oxygen saturation and 0.64 (0.56 to 0.73) respiratory rate. CONCLUSION: While respiratory parameters were most predictive, no individual parameter was as good as a full NEWS2, which is an acceptable predictor of short-term mortality in patients with COVID-19. This supports recommendation to use NEWS2 alongside clinical judgement to assess patients with COVID-19.

A study to introduce National Early Warning Scores (NEWS) in care homes: Influence on decision-making and referral processes.

AIM: Early warning scores are commonly used in hospital settings, but little is known about their use in care homes. This study aimed to evaluate the impacts of National Early Warning Scores alongside other measures in this setting. DESIGN: Convergent parallel design. METHODS: Quantitative data from 276 care home residents from four care homes were used to analyse the relationship between National Early Warning Scores score, resident outcome and functional daily living (Barthel ADL (Barthel Index for Activities of Daily Living)) and Rockwood (frailty). Interviews with care home staff (N = 13) and care practitioners (N = 4) were used to provide qualitative data. RESULTS: A statistically significant link between National Early Warning Scores (p = .000) and Barthel ADL (p = .013) score and hospital admissions was found, while links with Rockwood were insignificant (p = .551). Care home staff reported many benefits of National Early Warning Scores, including improved communication, improved decision-making and role empowerment. Although useful, due to the complexity of the resident population's existing health conditions, National Early Warning Scores alone could not act as a diagnostic tool.

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

COVID-19 disease in hospitalized young adults in India and China: Evaluation of risk factors predicting progression across two major ethnic groups.

Data pertaining to risk factor analysis in coronavirus disease 2019 (COVID-19) is confounded by the lack of data from an ethnically diverse population. In addition, there is a lack of data for young adults. This study was conducted to assess risk factors predicting COVID-19 severity and mortality in hospitalized young adults. A retrospective observational study was conducted at two centers from China and India on COVID-19 patients aged 20-50 years. Regression analysis to predict adverse outcomes was performed using parameters including age, sex, country of origin, hospitalization duration, comorbidities, lymphocyte count, and National Early Warning Score 2 (NEWS2) score at admission. A total of 420 patients (172 East Asians and 248 South Asians) were included. The predictive model for intensive care unit (ICU) admission with variables NEWS2 Category II and higher, diabetes mellitus, liver dysfunction, and low lymphocyte counts had an area under the curve (AUC) value of 0.930 with a sensitivity of 0.931 and a specificity of 0.784. The predictive model for mortality with NEWS2 Category III, cancer, and decreasing lymphocyte count had an AUC value of 0.883 with a sensitivity of 0.903 and a specificity of 0.701. A combined predictive model with bronchial asthma and low lymphocyte count, in contrast, had an AUC value of 0.768 with a sensitivity of 0.828 and a specificity of 0.719 for NEWS2 score (5 or above) at presentation. NEWS2 supplemented with comorbidity profile and lymphocyte count could help identify hospitalized young adults at risk of adverse COVID-19 outcomes.

Early warning scores to assess the probability of critical illness in patients with COVID-19.

OBJECTIVE: Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation. METHODS: Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation. RESULTS: In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively. CONCLUSION: In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.

Low-dose radiation therapy for COVID-19 pneumonia: a pilot study.

OBJECTIVES: The World Health Organization (WHO) has declared coronavirus disease 2019 (COVID-19) as pandemic in March 2020. Currently there is no specific effective treatment for COVID-19. The major cause of death in COVID-19 is severe pneumonia leading to respiratory failure. Radiation in low doses (<100 cGy) has been known for its anti-inflammatory effect and therefore, low dose radiation therapy (LDRT) to lungs can potentially mitigate the severity of pneumonia and reduce mortality. We conducted a pilot trial to study the feasibility and clinical efficacy of LDRT to lungs in the management of patients with COVID-19. METHODS: From June to Aug 2020, we enrolled 10 patients with COVID-19 having moderate to severe risk disease [National Early Warning Score (NEWS) of ≥5]. Patients were treated as per the standard COVID-19 management guidelines along with LDRT to both lungs with a dose of 70cGy in single fraction. Response assessment was done based on the clinical parameters using the NEWS. RESULTS: All patients completed the prescribed treatment. Nine patients had complete clinical recovery mostly within a period ranging from 3 to 7 days. One patient, who was a known hypertensive, showed clinical deterioration and died 24 days after LDRT. No patients showed the signs of acute radiation toxicity. CONCLUSION: The results of our pilot study suggest that LDRT is feasible in COVID-19 patients having moderate to severe disease. Its clinical efficacy may be tested by conducting randomized controlled trials. ADVANCES IN KNOWLEDGE: LDRT has shown promising results in COVID-19 pneumonia and should be researched further through randomized controlled trials.