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

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Establishment of a simplified score for predicting risk during intrahospital transport of critical patients: A prospective cohort study

AIMS AND OBJECTIVES

To establish a simple score that enables nurses to quickly, conveniently and accurately identify patients whose condition may change during intrahospital transport.

BACKGROUND

Critically ill patients may experience various complications during intrahospital transport; therefore, it is important to predict their risk before they leave the emergency department. The existing scoring systems were not developed for this population.

DESIGN

A prospective cohort study.

METHODS

This study used convenience sampling and continuous enrolment from 1 January, 2019, to 30 June, 2021, and 584 critically ill patients were included. The collected data included vital signs and any condition change during transfer. The STROBE checklist was used.

RESULTS

The median age of the modelling group was 74 (62, 83) years; 93 (19.7%) patients were included in the changed group, and 379 (80.3%) were included in the stable group. The five independent model variables (respiration, pulse, oxygen saturation, systolic pressure and consciousness) were statistically significant (p < .05). The above model was simplified based on beta coefficient values, and each variable was assigned 1 point, for a total score of 0-5 points. The AUC of the simplified score in the modelling group was 0.724 (95% CI: 0.682-0.764); the AUC of the simplified score in the validation group (112 patients) was 0.657 (95% CI: 0.566-0.741).

CONCLUSIONS

This study preliminarily established a simplified scoring system for the prediction of risk during intrahospital transport from the emergency department to the intensive care unit. It provides emergency nursing staff with a simple assessment tool to quickly, conveniently and accurately identify a patient's transport risk.

RELEVANCE TO CLINICAL PRACTICE

This study suggested the importance of strengthening the evaluation of the status of critical patients before intrahospital transport, and a simple score was formed to guide emergency department nurses in evaluating patients.

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.

Assessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis

BACKGROUND

The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. The National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases.

OBJECTIVE

To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital.

METHODS

We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models' performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score.

RESULTS

Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient's age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score - 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage.

CONCLUSION

Our model demonstrated incremental value for COVID-19 patient's status evaluation.

COVID-19 triajında CLUE protokolünün etkinliği

Purpose: The purpose of this study was to evaluate the effectiveness of the CLUE protocol in COVID-19 triage. Materials and Methods: Patients who presented to the emergency department due to dyspnea with oxygen saturation below 95 % and were diagnosed with COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) tests were included in this prospective, observational study. Patients included in the study underwent lung ultrasound (LUS) in the light of the CLUE protocol, and were accordingly given LUS scores of between 0 and 36, also within the scope of the protocol. Patients were placed under observation, and clinical outcomes of discharge from the emergency department, admission to the ward, and admission to intensive care or discharge were recorded. ROC analysis was applied in the calculation of threshold values for LUS scores predicting discharge, admission to intensive care, and mortality. Results: Forty-five patients with a mean age of 63 ± 18 years were included in the study. Fifteen patients (33 %) were treated on an outpatient basis and discharged, while 12 (27 %) were admitted to the ward and 18 (40 %) to the intensive care unit. Mortality occurred in 15 (33 %) patients. An LUS score lower than 3 was 97 % sensitive and 80 % specific for discharge, a score greater than 10 was 94 % sensitive and 78 % specific for admission to the intensive care unit, and a score higher than 11 was 93 % sensitive and 87 % specific for mortality. Based on regression analysis, an LUS score higher than 10 emerged as an independent risk factor for intensive care requirement, a score lower than 3 for discharge, and a score over 11 for mortality. Conclusion: The CLUE protocol may be a useful bedside test in COVID-19 triage, and one that does not involve radiation or require laboratory tests.

Glasgow Coma Scale Versus Physiologic Scoring Systems in Predicting the Outcome of ICU admitted Trauma Patients; a Diagnostic Accuracy Study

Introduction

There is no consensus on the performance of decision rules in predicting the prognosis of trauma patients. Therefore, the present study aimed to compare the value of Glasgow coma scale (GCS) and physiologic scoring systems in predicting mortality and poor outcome of trauma patients.

Methods

This diagnostic accuracy study was conducted on multiple trauma patients admitted to the intensive care units of two hospitals in Tehran, Iran, from 21 November 2020 to 22 May 2021. The patients' demographic characteristics, length of stay in the intensive care unit (ICU), the vital signs, and the GCS on admission were recorded. Finally, the mortality, disability, and complete recovery of patients at the time of discharge were evaluated and receiver operating characteristics (ROC) curve analysis was used to compare the performance of physiologic scoring systems with GCS.

Results

200 trauma patients with the mean age of 43.53±19.84 years were evaluated (74% male). The area under the ROC curve for New Trauma Score (NTS), Revised Trauma Score (RTS), Worthing Physiological Scoring System (WPSS), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), Glasgow Coma Scale, Age, and Systolic Blood Pressure score (GAPS) ,Glasgow coma scale (GCS) in prediction of mortality were 0.95, 0.95, 0.83, 0.89, 0.91, 0.84, 0.77, 0.97, and 0.98 respectively. The performance of GCS was statistically superior to RTS (P=0.005), WPSS (P=0.0001), RAPS (P=0.0002), REMS (P=0.002), MEWS (P<0.0001), and NEWS (P<0.0001). However, the performance of GCS, NTS (P=0.146), and GAPS (P=0.513) were not significantly different. Also, in prediction of poor outcomes, the AUC of GCS (0.98) was significantly higher than RTS (0.95), RAPS (0.85), REMS (0.85), MEWS (0.84), NEWS (0.77), and WPSS (0.75).

Conclusion

The GCS score seems to be a better instrument to predict mortality and poor outcome in trauma patients compared to other tools due to its high accuracy, wide application, and easy calculation.

Evaluation and Comparison of the Predictive Value of 4C Mortality Score, NEWS, and CURB-65 in Poor Outcomes in COVID-19 Patients: A Retrospective Study from a Single Center in Romania

To date, the COVID-19 pandemic has caused millions of deaths across the world. Prognostic scores can improve the clinical management of COVID-19 diagnosis and treatment. The objective of this study was to assess the predictive role of 4C Mortality, CURB-65, and NEWS in COVID-19 mortality among the Romanian population. A single-center, retrospective, observational study was conducted on patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-proven COVID-19 admitted to the Municipal Emergency Clinical Hospital of Timisoara, Romania, between 1 October 2020 and 15 March 2021. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were performed to determine the discrimination accuracy of the three scores. The mean values of the risk scores were higher in the non-survivors group (survivors group vs. non-survivors group: 8 vs. 15 (4C Mortality Score); 3 vs. 8.5 (NEWS); 1 vs. 3 (CURB-65)). In terms of mortality risk prediction, the NEWS performed best, with an AUC of 0.86, and the CURB-65 score performed poorly, with an AUC of 0.80. CURB-65, NEWS, and 4C Mortality scores were significant mortality predictors in the analysis, with acceptable calibration. Among the scores assessed in our study, NEWS had the highest performance in predicting in-hospital mortality in COVID-19 patients. Thus, the findings from this study suggest that the use of NEWS may be beneficial to the early identification of high-risk COVID-19 patients and the provision of more aggressive care to reduce mortality associated with COVID-19.

The AIFELL Score as a Predictor of Coronavirus Disease 2019 (COVID-19) Severity and Progression in Hospitalized Patients

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has caused a global burden for health care systems due to high morbidity and mortality rates, leading to caseloads that episodically surpass hospital resources. Due to different disease manifestations, the triage of patients at high risk for a poor outcome continues to be a major challenge for clinicians. The AIFELL score was developed as a simple decision instrument for emergency rooms to distinguish COVID-19 patients in severe disease stages from less severe COVID-19 and non-COVID-19 cases. In the present study, we aimed to evaluate the AIFELL score as a prediction tool for clinical deterioration and disease severity in hospitalized COVID-19 patients. During the second wave of the COVID-19 pandemic in Switzerland, we analyzed consecutively hospitalized patients at the Triemli Hospital Zurich from the end of November 2020 until mid-February 2021. Statistical analyses were performed for group comparisons and to evaluate significance. AIFELL scores of patients developing severe COVID-19 stages IIb and III during hospitalization were significantly higher upon admission compared to those patients not surpassing stages I and IIa. Group comparisons indicated significantly different AIFELL scores between each stage. In conclusion, the AIFELL score at admission was useful to predict the disease severity and progression in hospitalized COVID-19 patients.

Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Background

The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization.

Methods

We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan–Meier survival curves were used in survival rate.

Results

In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001).

Conclusion

We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.

The COVID-19 Assessment for Survival at Admission (CASA) Index: A 12 Months Observational Study

Objective: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the stratification of patients at risk of mortality is not well defined. In this study, we aimed to define a mortality risk index to allocate patients to the appropriate intensity of care. Methods: This is a 12 months observational longitudinal study designed to develop and validate a pragmatic mortality risk score to stratify COVID-19 patients aged ≥18 years and admitted to hospital between March 2020 and March 2021. Main outcome was in-hospital mortality. Results: 244 patients were included in the study (mortality rate 29.9%). The Covid-19 Assessment for Survival at Admission (CASA) index included seven variables readily available at admission: respiratory rate, troponin, albumin, CKD-EPI, white blood cell count, D-dimer, Pa02/Fi02. The CASA index showed high discrimination for mortality with an AUC of 0.91 (sensitivity 98.6%; specificity 69%) and a better performance compared to SOFA (AUC = 0.76), age (AUC = 0.76) and 4C mortality (AUC = 0.82). The cut-off identified (11.994) for CASA index showed a negative predictive value of 99.16% and a positive predictive value of 57.58%. Conclusions: A quick and readily available index has been identified to help clinicians stratify COVID-19 patients according to the appropriate intensity of care and minimize hospital admission to patients at high risk of mortality.

The Prognostic Accuracy of National Early Warning Score 2 on Predicting Clinical Deterioration for Patients With COVID-19: A Systematic Review and Meta-Analysis

Background: During the coronavirus disease 2019 (COVID-19) pandemic, the National Early Warning Score 2 (NEWS2) is recommended for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. Therefore, our purpose is to assess the prognostic accuracy of NEWS2 on predicting clinical deterioration for patients with COVID-19. Methods: We searched PubMed, Embase, Scopus, and the Cochrane Library from December 2019 to March 2021. Clinical deterioration was defined as the need for intensive respiratory support, admission to the intensive care unit, or in-hospital death. Sensitivity, specificity, and likelihood ratios were pooled by using the bivariate random-effects model. Overall prognostic performance was summarized by using the area under the curve (AUC). We performed subgroup analyses to assess the prognostic accuracy of NEWS2 in different conditions. Results: Eighteen studies with 6,922 participants were included. The NEWS2 of five or more was commonly used for predicting clinical deterioration. The pooled sensitivity, specificity, and AUC were 0.82, 0.67, and 0.82, respectively. Benefitting from adding a new SpO₂ scoring scale for patients with hypercapnic respiratory failure, the NEWS2 showed better sensitivity (0.82 vs. 0.75) and discrimination (0.82 vs. 0.76) than the original NEWS. In addition, the NEWS2 was a sensitive method (sensitivity: 0.88) for predicting short-term deterioration within 72 h. Conclusions: The NEWS2 had moderate sensitivity and specificity in predicting the deterioration of patients with COVID-19. Our results support the use of NEWS2 monitoring as a sensitive method to initially assess COVID-19 patients at hospital admission, although it has a relatively high false-trigger rate. Our findings indicated that the development of enhanced or modified NEWS may be necessary.