Showing papers on "Early warning score published in 2021"

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

Abstract: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

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

Abstract: 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.

Predicting In-Hospital Mortality in COVID-19 Older Patients with Specifically Developed Scores.

Abstract: BACKGROUND/OBJECTIVES: Several scoring systems have been specifically developed for risk stratification in COVID-19 patients. DESIGN: We compared, in a cohort of confirmed COVID-19 older patients, three specifically developed scores with a previously established early warning score. Main endpoint was all causes in-hospital death. SETTING: This is a single-center, retrospective observational study, conducted in the Emergency Department (ED) of an urban teaching hospital, referral center for COVID-19. PARTICIPANTS: We reviewed the clinical records of the confirmed COVID-19 patients aged 60 years or more consecutively admitted to our ED over a 6-week period (March 1st to April 15th, 2020). A total of 210 patients, aged between 60 and 98 years were included in the study cohort. MEASUREMENTS: International Severe Acute Respiratory Infection Consortium Clinical Characterization Protocol-Coronavirus Clinical Characterization Consortium (ISARIC-4C) score, COVID-GRAM Critical Illness Risk Score (COVID-GRAM), quick COVID-19 Severity Index (qCSI), National Early Warning Score (NEWS). RESULTS: Median age was 74 (67-82) and 133 (63.3%) were males. Globally, 42 patients (20.0%) deceased. All the score evaluated showed a fairly good predictive value with respect to in-hospital death. The ISARIC-4C score had the highest area under ROC curve (AUROC) 0.799 (0.738-0.851), followed by the COVID-GRAM 0.785 (0.723-0.838), NEWS 0.764 (0.700-0.819), and qCSI 0.749 (0.685-0.806). However, these differences were not statistical significant. CONCLUSION: Among the evaluated scores, the ISARIC-4C and the COVID-GRAM, calculated at ED admission, had the best performance, although the qCSI had similar efficacy by evaluating only three items. However, the NEWS, already widely validated in clinical practice, had a similar performance and could be appropriate for older patients with COVID-19.

National Early Warning Score 2 (NEWS2) to identify inpatient COVID-19 deterioration: a retrospective analysis.

Abstract: Introduction We sought to provide the first report of the use of NEWS2 monitoring to pre-emptively identify clinical deterioration within hospitalised COVID-19 patients. Methods Consecutive adult admissions with PCR-confirmed COVID-19 were included in this single-centre retrospective UK cohort study. We analysed all electronic clinical observations recorded within 28 days of admission until discharge or occurrence of a serious event, defined as any of the following: initiation of respiratory support, admission to intensive care, initiation of end of life care, or in-hospital death. Results 133/296 (44.9%) patients experienced at least one serious event. NEWS2 ≥ 5 heralded the first occurrence of a serious event with sensitivity 0.98 (95% CI 0.96–1.00), specificity 0.28 (0.21–0.35), positive predictive value (PPV) 0.53 (0.47–0.59), and negative predictive value (NPV) 0.96 (0.90–1.00). The NPV (but not PPV) of NEWS2 monitoring exceeded that of other early warning scores including the Modified Early Warning Score (MEWS) (0.59 [0.52–0.66], p Conclusion Our results support the use of NEWS2 monitoring as a sensitive method to identify deterioration of hospitalised COVID-19 patients, albeit at the expense of a relatively high false-trigger rate.

Real-time prediction of COVID-19 related mortality using electronic health records.

Abstract: Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality. Identifying COVID-19 patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, the authors present a clinical risk scoring system trained on a large data set of patients from 69 healthcare institutions in multiple countries.

Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19.

Abstract: Background Radiographic severity may predict patient deterioration and outcomes from COVID-19 pneumonia. Purpose To assess the reliability and reproducibility of three chest radiograph reporting systems (RALE, Brixia, and percentage opacification) in proven SARS-CoV-2 and examine the ability of these scores to predict adverse outcomes both alone and in conjunction with two clinical scoring systems: NEWS2 and ISARIC-4C mortality. Materials and Methods This retrospective cohort study used routinely collected clinical data of PCR-positive SARS-CoV-2 patients admitted to a single UK center from February 2020 until July 2020. Initial chest radiographs were scored for RALE, Brixia, and percentage opacification by one of three radiologists. Intra- and inter-rater agreement was assessed with Intraclass correlation coefficients. The rate of ICU admission or death until 60 days after scored chest radiograph was estimated. NEWS2 and ISARIC-4C mortality, on hospital admission were calculated. Daily risk of admission to ICU or death was modelled with Cox proportional hazards models, incorporating the chest radiograph scores adjusted for NEWS2 or ISARIC-4C mortality. Results Admission chest radiographs of 50 patients (mean age, 74 years +/-16 [sd], 28 men) were scored by all 3 radiologists, with good inter-rater reliability for all scores (ICCs (95% CIs) of for RALE 0.87 (0.80, 0.92), BRIXIA 0.86 (0.76, 0.92), and percentage opacification 0.72 (0.48, 0.85)). Of 751 patients with chest radiograph, those with >75% opacification had a median time to ICU admission or death of just 1-2 days. Among 628 patients with data (median age 76 years (IQR 61 - 84), and 344 were men), 50-75% opacification increased risk of ICU admission or death by twofold (1.6 - 2.8), and over 75% opacification by 4 fold (3.4 - 4.7), compared to a 0-25% opacification when adjusted for NEWS2 score. Conclusion BRIXIA, RALE, and percent opacification scores all reliably predicted adverse outcomes in SARS-CoV-2. See also the editorial by Little.

A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults.

Abstract: BACKGROUND Although coronavirus disease 2019 (COVID-19) disproportionally affects older adults, the use of conventional triage tools in acute care settings ignores the key aspects of vulnerability. OBJECTIVE This study aimed to determine the usefulness of adding a rapid vulnerability screening to an illness acuity tool to predict mortality in hospitalised COVID-19 patients. DESIGN Cohort study. SETTING Large university hospital dedicated to providing COVID-19 care. PARTICIPANTS Participants included are 1,428 consecutive inpatients aged ≥50 years. METHODS Vulnerability was assessed using the modified version of PRO-AGE score (0-7; higher = worse), a validated and easy-to-administer tool that rates physical impairment, recent hospitalisation, acute mental change, weight loss and fatigue. The baseline covariates included age, sex, Charlson comorbidity score and the National Early Warning Score (NEWS), a well-known illness acuity tool. Our outcome was time-to-death within 60 days of admission. RESULTS The patients had a median age of 66 years, and 58% were male. The incidence of 60-day mortality ranged from 22% to 69% across the quartiles of modified PRO-AGE. In adjusted analysis, compared with modified PRO-AGE scores 0-1 ('lowest quartile'), the hazard ratios (95% confidence interval) for 60-day mortality for modified PRO-AGE scores 2-3, 4 and 5-7 were 1.4 (1.1-1.9), 2.0 (1.5-2.7) and 2.8 (2.1-3.8), respectively. The modified PRO-AGE predicted different mortality risk levels within each stratum of NEWS and improved the discrimination of mortality prediction models. CONCLUSIONS Adding vulnerability to illness acuity improved accuracy of predicting mortality in hospitalised COVID-19 patients. Combining tools such as PRO-AGE and NEWS may help stratify the risk of mortality from COVID-19.

False-negative RT-PCR for COVID-19 and a diagnostic risk score: a retrospective cohort study among patients admitted to hospital.

Abstract: Objective To describe the characteristics and outcomes of patients with a clinical diagnosis of COVID-19 and false-negative SARS-CoV-2 reverse transcription-PCR (RT-PCR), and develop and internally validate a diagnostic risk score to predict risk of COVID-19 (including RT-PCR-negative COVID-19) among medical admissions Design Retrospective cohort study Setting Two hospitals within an acute NHS Trust in London, UK Participants All patients admitted to medical wards between 2 March and 3 May 2020 Outcomes Main outcomes were diagnosis of COVID-19, SARS-CoV-2 RT-PCR results, sensitivity of SARS-CoV-2 RT-PCR and mortality during hospital admission For the diagnostic risk score, we report discrimination, calibration and diagnostic accuracy of the model and simplified risk score and internal validation Results 4008 patients were admitted between 2 March and 3 May 2020 1792 patients (448%) were diagnosed with COVID-19, of whom 1391 were SARS-CoV-2 RT-PCR positive and 283 had only negative RT-PCRs Compared with a clinical reference standard, sensitivity of RT-PCR in hospital patients was 831% (95% CI 812%-848%) Broadly, patients with false-negative RT-PCR COVID-19 and those confirmed by positive PCR had similar demographic and clinical characteristics but lower risk of intensive care unit admission and lower in-hospital mortality (adjusted OR 041, 95% CI 027-061) A simple diagnostic risk score comprising of age, sex, ethnicity, cough, fever or shortness of breath, National Early Warning Score 2, C reactive protein and chest radiograph appearance had moderate discrimination (area under the receiver-operator curve 083, 95% CI 082 to 085), good calibration and was internally validated Conclusion RT-PCR-negative COVID-19 is common and is associated with lower mortality despite similar presentation Diagnostic risk scores could potentially help triage patients requiring admission but need external validation

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.

Abstract: Importance Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient’s likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations. Objectives To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients’ risk of death; and evaluate the tool’s predictive accuracy compared with several established clinical scores. Design, Setting, and Participants This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021. Main Outcomes and Measures Three SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively. Secondary outcomes included 3-day mortality and inpatient mortality. The SERP’s predictive power was measured using the area under the curve in the receiver operating characteristic analysis. Results The study included 224 666 ED episodes in the model training cohort (mean [SD] patient age, 63.60 [16.90] years; 113 426 [50.5%] female), 56 167 episodes in the validation cohort (mean [SD] patient age, 63.58 [16.87] years; 28 427 [50.6%] female), and 42 676 episodes in the testing cohort (mean [SD] patient age, 64.85 [16.80] years; 21 556 [50.5%] female). The mortality rates in the training cohort were 0.8% at 2 days, 2.2% at 7 days, and 5.9% at 30 days. In the testing cohort, the areas under the curve of SERP-30d were 0.821 (95% CI, 0.796-0.847) for 2-day mortality, 0.826 (95% CI, 0.811-0.841) for 7-day mortality, and 0.823 (95% CI, 0.814-0.832) for 30-day mortality and outperformed several benchmark scores. Conclusions and Relevance In this retrospective cohort study, SERP had better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment in the ED. It has the potential to be widely applied and validated in different circumstances and health care settings.

Comparison the accuracy of early warning scores with qSOFA and SIRS for predicting sepsis in the emergency department

Abstract: Introduction The aims of this study were to evaluate the accuracy of early warnings scores including National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Mortality in Emergency Department Sepsis score (MEDS), Search Out Severity score (SOS) and compare them with quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS) for detecting sepsis among infected patients at the emergency department (ED). Methods A retrospective study was conducted at ED of a university hospital. Primary outcome was sepsis defined by sepsis-2 definition. Secondary outcomes were sepsis defined by sepsis-3 definition, hospital admission and in-hospital mortality. Results A total of 652 (83.9%) from 777 infected patients were classified as sepsis by sepsis-2. MEWS and SOS outperformed other scores in predicting sepsis with the area under receiver operating characteristic curve (AUC) (95%CI) 0.845 (0.805–0.885) and 0.839 (0.799–0.879), followed by NEWS 0.800 (0.753–0.846), MEDS 0.608 (0.551–0.665) and qSOFA 0.657 (0.609–0.706) (p Conclusion The early warning scores, qSOFA and SIRS had limited decision making for predicting sepsis and adverse outcomes among infected patients.

Prognostic Accuracy of Early Warning Scores for Clinical Deterioration in Patients With COVID-19

Abstract: Background: Early Warning Scores (EWS), including the National Early Warning Score 2 (NEWS2) and Modified NEWS (NEWS-C), have been recommended for triage decision in patients with COVID-19. However, the effectiveness of these EWS in COVID-19 has not been fully validated. The study aimed to investigate the predictive value of EWS to detect clinical deterioration in patients with COVID-19. Methods: Between February 7, 2020 and February 17, 2020, patients confirmed with COVID-19 were screened for this study. The outcomes were early deterioration of respiratory function (EDRF) and need for intensive respiratory support (IRS) during the treatment process. The EDRF was defined as changes in the respiratory component of the sequential organ failure assessment (SOFA) score at day 3 (ΔSOFAresp = SOFA resp at day 3-SOFAresp on admission), in which the positive value reflects clinical deterioration. The IRS was defined as the use of high flow nasal cannula oxygen therapy, noninvasive or invasive mechanical ventilation. The performances of EWS including NEWS, NEWS 2, NEWS-C, Modified Early Warning Scores (MEWS), Hamilton Early Warning Scores (HEWS), and quick sepsis-related organ failure assessment (qSOFA) for predicting EDRF and IRS were compared using the area under the receiver operating characteristic curve (AUROC). Results: A total of 116 patients were included in this study. Of them, 27 patients (23.3%) developed EDRF and 24 patients (20.7%) required IRS. Among these EWS, NEWS-C was the most accurate scoring system for predicting EDRF [AUROC 0.79 (95% CI, 0.69-0.89)] and IRS [AUROC 0.89 (95% CI, 0.82-0.96)], while NEWS 2 had the lowest accuracy in predicting EDRF [AUROC 0.59 (95% CI, 0.46-0.720)] and IRS [AUROC 0.69 (95% CI, 0.57-0.81)]. A NEWS-C ≥ 9 had a sensitivity of 59.3% and a specificity of 85.4% for predicting EDRF. For predicting IRS, a NEWS-C ≥ 9 had a sensitivity of 75% and a specificity of 88%. Conclusions: The NEWS-C was the most accurate scoring system among common EWS to identify patients with COVID-19 at risk for EDRF and need for IRS. The NEWS-C could be recommended as an early triage tool for patients with COVID-19.

The utility of the rapid emergency medicine score (REMS) compared with SIRS, qSOFA and NEWS for Predicting in-hospital Mortality among Patients with suspicion of Sepsis in an emergency department.

Abstract: Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes secondary to sepsis in the Emergency Department (ED). These EWSs include the Systemic Inflammatory Response Syndrome criteria (SIRS), the quick Sequential Organ Failure Assessment (qSOFA) and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has never been validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of SIRS, qSOFA and NEWS for predicting mortality in patients with suspicion of sepsis in the ED. We conducted a retrospective study at the ED of Siriraj Hospital Mahidol University, Thailand. Adult patients suspected of having sepsis in the ED between August 2018 and July 2019 were included. Their EWSs were calculated. The primary outcome was all-cause in-hospital mortality. The secondary outcome was 7-day mortality. A total of 1622 patients were included in the study; 457 (28.2%) died at hospital discharge. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.62 (95% confidence interval (CI) 0.59, 0.65)), which was significantly higher than qSOFA (AUROC 0.58 (95%CI 0.55, 0.60); p = 0.005) and SIRS (AUROC 0.52 (95%CI 0.49, 0.55); p < 0.001) but not significantly superior to NEWS (AUROC 0.61 (95%CI 0.58, 0.64); p = 0.27). REMS was the best EWS in terms of calibration and association with the outcome. It could also provide the highest net benefit from the decision curve analysis. Comparison of EWSs plus baseline risk model showed similar results. REMS also performed better than other EWSs for 7-day mortality. REMS was an early warning score with higher accuracy than sepsis-related scores (qSOFA and SIRS), similar to NEWS, and had the highest utility in terms of net benefit compared to SIRS, qSOFA and NEWS in predicting in-hospital mortality in patients presenting to the ED with suspected sepsis.

Prognostic accuracy of emergency department triage tools for adults with suspected COVID-19: the PRIEST observational cohort study.

Abstract: Background The WHO and National Institute for Health and Care Excellence recommend various triage tools to assist decision-making for patients with suspected COVID-19. We aimed to compare the accuracy of triage tools for predicting severe illness in adults presenting to the ED with suspected COVID-19. Methods We undertook a mixed prospective and retrospective observational cohort study in 70 EDs across the UK. We collected data from people attending with suspected COVID-19 and used presenting data to determine the results of assessment with the WHO algorithm, National Early Warning Score version 2 (NEWS2), CURB-65, CRB-65, Pandemic Modified Early Warning Score (PMEWS) and the swine flu adult hospital pathway (SFAHP). We used 30-day outcome data (death or receipt of respiratory, cardiovascular or renal support) to determine prognostic accuracy for adverse outcome. Results We analysed data from 20 891 adults, of whom 4611 (22.1%) died or received organ support (primary outcome), with 2058 (9.9%) receiving organ support and 2553 (12.2%) dying without organ support (secondary outcomes). C-statistics for the primary outcome were: CURB-65 0.75; CRB-65 0.70; PMEWS 0.77; NEWS2 (score) 0.77; NEWS2 (rule) 0.69; SFAHP (6-point rule) 0.70; SFAHP (7-point rule) 0.68; WHO algorithm 0.61. All triage tools showed worse prediction for receipt of organ support and better prediction for death without organ support. At the recommended threshold, PMEWS and the WHO criteria showed good sensitivity (0.97 and 0.95, respectively) at the expense of specificity (0.30 and 0.27, respectively). The NEWS2 score showed similar sensitivity (0.96) and specificity (0.28) when a lower threshold than recommended was used. Conclusion CURB-65, PMEWS and the NEWS2 score provide good but not excellent prediction for adverse outcome in suspected COVID-19, and predicted death without organ support better than receipt of organ support. PMEWS, the WHO criteria and NEWS2 (using a lower threshold than usually recommended) provide good sensitivity at the expense of specificity. Trial registration number ISRCTN56149622.

The Predictive Role of Combined Cardiac and Lung Ultrasound in Coronavirus Disease 2019.

Abstract: Background and Objectives We aimed to evaluate sonographic features that may aid in risk stratification and propose a focused cardiac and lung ultrasound (LUS) algorithm in patients with COVID-19 Methods Two hundred consecutive hospitalized patients with COVID-19 underwent comprehensive clinical and echocardiographic examination, as well as LUS, irrespective of clinical indication, within 24 hours of admission as part of a prospective predefined protocol. Assessment included calculation of the Modified Early Warning Score (MEWS), left ventricular (LV) systolic and diastolic function, hemodynamic and right ventricular (RV) assessment and a calculated LUS score. We performed outcome analysis to identify echocardiographic and LUS predictors of mortality or the composite event of mortality or need for invasive mechanical ventilation, and to assess their adjunctive value on top of clinical parameters and MEWS. Results A simplified echocardiographic risk score comprised of LV ejection fraction Conclusions In hospitalized patients with COVID-19, a very limited echocardiographic exam is sufficient for outcome prediction. The addition of echocardiography in patients with high risk MEWS score decreases the rate of falsely identifying patients as high risk to die, and may improve resource allocation in case of high patient load.background

Remote monitoring of COVID-19 positive high-risk patients in domestic isolation: A feasibility study.

Abstract: Background If a COVID-19 patient develops a so-called severe course, he or she must be taken to hospital as soon as possible. This proves difficult in domestic isolation, as patients are not continuously monitored. The aim of our study was to establish a telemonitoring system in this setting. Methods Oxygen saturation, respiratory rate, heart rate and temperature were measured every 15 minutes using an in-ear device. The data was transmitted to the Telecovid Centre via mobile network or internet and monitored 24/7 by a trained team. The data were supplemented by daily telephone calls. The patients´ individual risk was assessed using a modified National Early Warning Score. In case of a deterioration, a physician initiated the appropriate measures. Covid-19 Patients were included if they were older than 60 years or fulfilled at least one of the following conditions: pre-existing disease (cardiovascular, pulmonary, immunologic), obesity (BMI >35), diabetes mellitus, hypertension, active malignancy, or pregnancy. Findings 153 patients (median age 59 years, 77 female) were included. Patients were monitored for 9 days (median, IQR 6-13 days) with a daily monitoring time of 13.3 hours (median, IQR 9.4-17.0 hours). 20 patients were referred to the clinic by the Telecovid team. 3 of these required intensive care without invasive ventilation, 4 with invasive ventilation, 1 of the latter died. All patients agreed that the device was easy to use. About 90% of hospitalised patients indicated that they would have delayed hospitalisation further if they had not been part of the study. Interpretation Our study demonstrates the successful implementation of a remote monitoring system in a pandemic situation. All clinically necessary information was obtained and adequate measures were derived from it without delay.

Prospective validation and comparison of COVID-GRAM, NEWS2, 4C mortality score, CURB-65 for the prediction of critical illness in COVID-19 patients.

Abstract: To the editor,When a patient is hospitalizated with COVID-19, prediction of the risk of development of severe or critical illness is of great importance [1]. Several risk scores have been described...

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning.

Abstract: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Performance of the Pandemic Medical Early Warning Score (PMEWS), Simple Triage Scoring System (STSS) and Confusion, Uremia, Respiratory rate, Blood pressure and age ≥ 65 (CURB-65) score among patients with COVID-19 pneumonia in an emergency department triage setting: a retrospective study

Abstract: BACKGROUND: Healthcare institutions are confronted with large numbers of patient admissions during large-scale or long-term public health emergencies like pandemics. Appropriate and effective triage is needed for effective resource use. OBJECTIVES: To evaluate the effectiveness of the Pandemic Medical Early Warning Score (PMEWS), Simple Triage Scoring System (STSS) and Confusion, Uremia, Respiratory rate, Blood pressure and age ≥ 65 years (CURB-65) score in an emergency department (ED) triage setting. DESIGN AND SETTING: Retrospective study in the ED of a tertiary-care university hospital in Duzce, Turkey. METHODS: PMEWS, STSS and CURB-65 scores of patients diagnosed with COVID-19 pneumonia were calculated. Thirty-day mortality, intensive care unit (ICU) admission, mechanical ventilation (MV) need and outcomes were recorded. The predictive accuracy of the scores was assessed using receiver operating characteristic curve analysis. RESULTS: One hundred patients with COVID-19 pneumonia were included. The 30-day mortality was 6%. PMEWS, STSS and CURB-65 showed high performance for predicting 30-day mortality (area under the curve: 0.968, 0.962 and 0.942, respectively). Age > 65 years, respiratory rate > 20/minute, oxygen saturation (SpO2) 4 hours showed associations with 30-day mortality (P < 0.05). CONCLUSIONS: CURB-65, STSS and PMEWS scores are useful for predicting mortality, ICU admission and MV need among patients diagnosed with COVID-19 pneumonia. Advanced age, increased respiratory rate, low SpO2 and prolonged ED length of stay may increase mortality. Further studies are needed for developing the triage scoring systems, to ensure effective long-term use of healthcare service capacity during pandemics.

National Early Warning Score 2 (NEWS2) better predicts critical Coronavirus Disease 2019 (COVID-19) illness than COVID-GRAM, a multi-centre study.

Abstract: Clinical scores to rapidly assess the severity illness of Coronavirus Disease 2019 (COVID-19) could be considered of help for clinicians. Recently, a specific score (named COVID-GRAM) for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, based on a nationwide Chinese cohort, has been proposed. We routinely applied the National Early Warning Score 2 (NEWS2) to predict critical COVID-19. Aim of this study is to compare NEWS2 and COVID-GRAM score. We retrospectively analysed data of 121 COVID-19 patients admitted in two Clinics of Infectious Diseases in the Umbria region, Italy. The primary outcome was critical COVID-19 illness defined as admission to the intensive care unit, invasive ventilation, or death. Accuracy of the scores was evaluated with the area under the receiver-operating characteristic curve (AUROC). Differences between scores were confirmed used Hanley–McNeil test. The NEWS2 AUROC curve measured 0.87 (standard error, SE 0.03; 95% CI 0.80–0.93; p < 0.0001). The COVID-GRAM score AUROC curve measured 0.77 (SE 0.04; 95% CI 0.68–0.85; p < 0.0001). Hanley–McNeil test showed that NEWS2 better predicted severe COVID-19 (Z = 2.03). The NEWS2 showed superior accuracy to COVID-GRAM score for prediction of critical COVID-19 illness.

Do early warning track and trigger tools improve patient outcomes? A systematic synthesis without meta-analysis.

Abstract: Aim To determine the effect of Early Warning Track and Trigger Tools on patient outcomes. Design A systematic review: synthesis without meta‐analysis. Data sources Electronic databases were searched from 1 January 2013–1 August 2018 and 221 papers identified. Review methods A systematic review and narrative synthesis supported the identification of synthesized findings named and reported according to outcome measure. RESULTS Five international papers representing over 74,000 patients were included in the analysis. Seven key findings were identified, the impact of NEWS on: (a) cardiopulmonary arrest; (b) mortality; (c) serious adverse events; (d) length of hospital stay; (e) hospital admissions; (f) observation frequency; and (g) Intensive/High dependency Unit admission. Papers identified statistically significant improvements in mortality, serious adverse events, hospital admissions, observation frequency, and intensive care unit/high dependency unit admission when an Early Warning Track and Trigger protocol is in use. There were conflicting results regarding length of stay and cardiopulmonary arrest data. Conclusion Early Warning Track and Trigger Tools can aid recognition of deteriorating patients. Further research is required in relation to hospital length of stay and cardiopulmonary arrests. Impact Early warning track and trigger tools have been implemented nationally and to a lesser degree internationally. There is evidence to suggest improved clinical outcomes following their use. Further research needs to combine the use of the National Early Warning Score with an agreed set of measured outcomes, and then subsequent study data could be combined to provide much stronger levels of evidence.

Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis

Abstract: Background: Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. Objective: The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. Methods: We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. Results: During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. Conclusions: This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.

National early warning score to predict intensive care unit transfer and mortality in COVID-19 in a French cohort.

Abstract: BACKGROUND: No risk stratification tool has been validated in hospitalised patients with coronavirus disease 2019 (COVID-19), despite a high rate of intensive care requirement and in-hospital mortality. We aimed to determine whether the National Early Warning Score (NEWS) at admission can accurately predict in-hospital mortality and ICU transfer. METHODS: This was a retrospective cohort study from January 24 to April 16, 2020, at Lille University Hospital. All consecutive adult patients with laboratory-confirmed COVID-19 who were initially admitted to non-ICU wards were included. The primary outcome was a composite criterion consisting of ICU transfer or in-hospital mortality. We evaluated the prognostic performance of NEWS by calculating the area under (AUC) the receiver operating characteristic curve, the optimal threshold value of NEWS, and its association with the primary outcome. RESULTS: Of the 202 COVID-19 patients, the median age was 65 (interquartile range 52-78), 38.6% were women and 136 had at least one comorbidity. The median NEWS was 4 (2-6). A total of 65 patients were transferred to the ICU or died in the hospital. Compared with patients with favourable outcome, these patients were significantly older, had more comorbidities and higher NEWS. The AUC for NEWS was 0.68 (0.60-0.77) and the best cutoff value was 6. Adjusted odds ratio for NEWS ≥ 6 as an independent predictor was 3.78 (1.94-7.09). CONCLUSIONS: In hospitalised COVID-19 patients, NEWS was an independent predictor of ICU transfer and in-hospital death. In daily practice, NEWS ≥ 6 at admission may help to identify patients who are at risk to deteriorate.