Performance Analysis of the National Early Warning Score and Modified Early Warning Score in the Adaptive COVID-19 Treatment Trial Cohort

Health-related quality of life after 12 months post discharge in patients hospitalised with COVID-19-related severe acute respiratory infection (SARI): a prospective analysis of SF-36 data and correlation with retrospective admission data on age, disease severity, and frailty

Long-term outcome and 'health-related quality of life' (HRQoL) following hospitalisation for COVID-19-related severe acute respiratory infection (SARI) is limited.

OBJECTIVE

To assess the impact of HRQoL in patients hospitalised with COVID-19-related SARI at 1 year post discharge, focusing on the potential impact of age, frailty, and disease severity.

METHOD

Routinely collected outcome data on 1207 patients admitted with confirmed COVID-19 related SARI across all three secondary care sites in our NHS trust over 3 months were assessed in this retrospective cohort study. Of those surviving 1 year, we prospectively collected 36-item short form (SF-36) HRQoL questionnaires, comparing three age groups (<49, 49-69, and the over 69-year-olds), the relative impact of frailty (using the Clinical Frailty Score; CFS), and disease severity (using National Early Warning Score; NEWS) on HRQoL domains.

RESULTS

Overall mortality was 46.5% in admitted patients. In our SF-36 cohort (n=169), there was a significant reduction in all HRQoL domains versus normative data; the most significant reductions were in the physical component (p<0.001) across all ages and the emotional component (p<0.01) in the 49-69 year age group, with age having no additional impact on HRQoL. However, there was a significant correlation between physical well-being versus CFS (the correlation coefficient=-0.37, p<0.05), though not NEWS, with no gender difference observed.

CONCLUSION

There was a significant reduction in all SF-36 domains at 1 year. Poor CFS at admission was associated with a significant and prolonged impact on physical parameters at 1 year. Age had little impact on the severity of HRQoL, except in the domains of physical functioning and the overall physical component.

The National Early Warning Score 2 with Age and Body Mass Index (NEWS2 Plus) to Determine Patients with Severe COVID-19 Pneumonia

(1) Background: Early identification of severe coronavirus disease 2019 (COVID-19) pneumonia at the initial phase of hospitalization is very crucial. To address this, we validated and updated the National Early Warning Score 2 (NEWS2) for this purpose. (2) Methods: We conducted a study on adult patients with COVID-19 infection in Chiang Mai, Thailand, between May 2021 and October 2021. (3) Results: From a total of 725 COVID-19 adult patients, 350 (48.3%) patients suffered severe COVID-19 pneumonia. In determining severe COVID-19 pneumonia, NEWS2 and NEWS2 + Age + BMI (NEWS2 Plus) showed the C-statistic values of 0.798 (95% CI, 0.767-0.830) and 0.821 (95% CI, 0.791-0.850), respectively. The C-statistic values of NEWS2 Plus were significantly improved compared to those of NEWS2 alone (p = 0.012). Utilizing a cut-off point of five, NEWS2 Plus exhibited better sensitivity and negative predictive value than the traditional NEWS2, with values of 99.7% vs. 83.7% and 98.9% vs. 80.7%, respectively. (4) Conclusions: The incorporation of age and BMI into the traditional NEWS2 score enhanced the efficacy of determining severe COVID-19 pneumonia. Physicians can rely on NEWS2 Plus (NEWS2 + Age + BMI) as a more effective decision-making tool for triaging COVID-19 patients during early hospitalization.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Background

The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection.

Methods

We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients.

Results

The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients.

Conclusions

The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves.

Funding

University of Vienna.

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.

Temporal Improvements in COVID-19 Outcomes for Hospitalized Adults: A Post Hoc Observational Study of Remdesivir Group Participants in the Adaptive COVID-19 Treatment Trial

BACKGROUND

The COVID-19 standard of care (SOC) evolved rapidly during 2020 and 2021, but its cumulative effect over time is unclear.

OBJECTIVE

To evaluate whether recovery and mortality improved as SOC evolved, using data from ACTT (Adaptive COVID-19 Treatment Trial).

DESIGN

ACTT is a series of phase 3, randomized, double-blind, placebo-controlled trials that evaluated COVID-19 therapeutics from February 2020 through May 2021. ACTT-1 compared remdesivir plus SOC to placebo plus SOC, and in ACTT-2 and ACTT-3, remdesivir plus SOC was the control group. This post hoc analysis compared recovery and mortality between these comparable sequential cohorts of patients who received remdesivir plus SOC, adjusting for baseline characteristics with propensity score weighting. The analysis was repeated for participants in ACTT-3 and ACTT-4 who received remdesivir plus dexamethasone plus SOC. Trends in SOC that could explain outcome improvements were analyzed. (ClinicalTrials.gov: NCT04280705 [ACTT-1], NCT04401579 [ACTT-2], NCT04492475 [ACTT-3], and NCT04640168 [ACTT-4]).

SETTING

94 hospitals in 10 countries (86% U.S. participants).

PARTICIPANTS

Adults hospitalized with COVID-19.

INTERVENTION

SOC.

MEASUREMENTS

28-day mortality and recovery.

RESULTS

Although outcomes were better in ACTT-2 than in ACTT-1, adjusted hazard ratios (HRs) were close to 1 (HR for recovery, 1.04 [95% CI, 0.92 to 1.17]; HR for mortality, 0.90 [CI, 0.56 to 1.40]). Comparable patients were less likely to be intubated in ACTT-2 than in ACTT-1 (odds ratio, 0.75 [CI, 0.53 to 0.97]), and hydroxychloroquine use decreased. Outcomes improved from ACTT-2 to ACTT-3 (HR for recovery, 1.43 [CI, 1.24 to 1.64]; HR for mortality, 0.45 [CI, 0.21 to 0.97]). Potential explanatory factors (SOC trends, case surges, and variant trends) were similar between ACTT-2 and ACTT-3, except for increased dexamethasone use (11% to 77%). Outcomes were similar in ACTT-3 and ACTT-4. Antibiotic use decreased gradually across all stages.

LIMITATION

Unmeasured confounding.

CONCLUSION

Changes in patient composition explained improved outcomes from ACTT-1 to ACTT-2 but not from ACTT-2 to ACTT-3, suggesting improved SOC. These results support excluding nonconcurrent controls from analysis of platform trials in rapidly changing therapeutic areas.

PRIMARY FUNDING SOURCE

National Institute of Allergy and Infectious Diseases.

Machine learning and predictive models: 2 years of Sars-CoV-2 pandemic in a single-center retrospective analysis

BACKGROUND

Since January 2020, coronavirus disease 19 (COVID-19) has rapidly spread all over the world. An early assessment of illness severity is crucial for the stratification of patients in order to address them to the right intensity path of care. We performed an analysis on a large cohort of COVID-19 patients (n=581) hospitalized between March 2020 and May 2021 in our intensive care unit (ICU) at Policlinico Riuniti di Foggia hospital. Through an integration of the scores, demographic data, clinical history, laboratory findings, respiratory parameters, a correlation analysis, and the use of machine learning our study aimed to develop a model to predict the main outcome.

METHODS

We deemed eligible for analysis all adult patients (age >18 years old) admitted to our department. We excluded all the patients with an ICU length of stay inferior to 24 h and the ones that declined to participate in our data collection. We collected demographic data, medical history, D-dimers, NEWS2, and MEWS scores on ICU admission and on ED admission, PaO2/FiO2 ratio on ICU admission, and the respiratory support modalities before the orotracheal intubation and the intubation timing (early vs late with a 48-h hospital length of stay cutoff). We further collected the ICU and hospital lengths of stay expressed in days of hospitalization, hospital location (high dependency unit, HDU, ED), and length of stay before and after ICU admission; the in-hospital mortality; and the in-ICU mortality. We performed univariate, bivariate, and multivariate statistical analyses.

RESULTS

SARS-CoV-2 mortality was positively correlated to age, length of stay in HDU, MEWS, and NEWS2 on ICU admission, D-dimer value on ICU admission, early orotracheal intubation, and late orotracheal intubation. We found a negative correlation between the PaO2/FiO2 ratio on ICU admission and NIV. No significant correlations with sex, obesity, arterial hypertension, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, diabetes mellitus, dyslipidemia, and neither MEWS nor NEWS on ED admission were observed. Considering all the pre-ICU variables, none of the machine learning algorithms performed well in developing a prediction model accurate enough to predict the outcome although a secondary multivariate analysis focused on the ventilation modalities and the main outcome confirmed how the choice of the right ventilatory support with the right timing is crucial.

CONCLUSION

In our cohort of COVID patients, the choice of the right ventilatory support at the right time has been crucial, severity scores, and clinical judgment gave support in identifying patients at risk of developing a severe disease, comorbidities showed a lower weight than expected considering the main outcome, and machine learning method integration could be a fundamental statistical tool in the comprehensive evaluation of such complex diseases.

Rapid Quantum Magnetic IL-6 Point-of-Care Assay in Patients Hospitalized with COVID-19

Interleukin-6 (IL-6) has been linked to several life-threatening disease processes. Developing a point-of-care testing platform for the immediate and accurate detection of IL-6 concentrations could present a valuable tool for improving clinical management in patients with IL-6-mediated diseases. Drawing on an available biobank of samples from 35 patients hospitalized with COVID-19, a novel quantum-magnetic sensing platform is used to determine plasma IL-6 concentrations. A strong correlation was observed between IL-6 levels measured by QDTI10x and the Luminex assay (r = 0.70, p-value < 0.001) and between QDTI80x and Luminex (r = 0.82, p-value < 0.001). To validate the non-inferiority of QDTI to Luminex in terms of the accuracy of IL-6 measurement, two clinical parameters—the need for intensive care unit admission and the need for mechanical intubation—were chosen. IL-6 concentrations measured by the two assays were compared with respect to these clinical outcomes. Results demonstrated a comparative predictive performance between the two assays with a significant correlation coefficient. Conclusion: In short, the QDTI assay holds promise for implementation as a potential tool for rapid clinical decision in patients with IL-6-mediated diseases. It could also reduce healthcare costs and enable the development of future various biomolecule point-of-care tests for different clinical scenarios.

Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study

Effective triage tools are indispensable for doctors to make a prompt decision for the treatment of multiple trauma patients in emergency departments (EDs). The Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), standardized early warning score (SEWS), Modified Rapid Emergency Medicine Score (mREMS), and Revised Trauma Score (RTS) are five common triage tools proposed for trauma management. However, few studies have compared these tools in a multiple trauma cohort and investigated the influence of nighttime admission on the performance of these tools. This retrospective study was aimed at evaluating and comparing the performance of MEWS, NEWS, SEWS, mREMS, and RTS for identifying the mortality risk and trauma severity of patients with multiple trauma admitted to the ED during the daytime and nighttime. Retrospective data were collected from the medical records of patients with multiple trauma admitted in the daytime or nighttime to calculate scores for each triage tool. Logistic regression analysis was conducted on each triage tool for identifying in-hospital mortality and severe trauma (injury severity score > 15) in the daytime and nighttime. The performance of the tools was evaluated and compared by calculating area under the receiver operating characteristic curve (AUROC) of the retrospective logistic model of each tool. We collected data for 1,818 admissions, including 1,070 daytime and 748 nighttime admissions. A comparison of performance for identifying in-hospital mortality between daytime and nighttime yielded the following results (AUROC): MEWS (0.95 vs. 0.93, p = 0.384), NEWS (0.95 vs. 0.94, p = 0.708), SEWS (0.95 vs. 0.94, p = 0.683), mREMS (0.94 vs. 0.92, p = 0.286), and RTS (0.93 vs. 0.93, p = 0.87). Similarly, a comparison of performance for identifying trauma severity between daytime and nighttime yielded the following results (AUROC): MEWS (0.78 vs. 0.78, p = 0.95), NEWS (0.8 vs. 0.8, p = 0.885), SEWS (0.78 vs. 0.78, p = 0.818), mREMS (0.75 vs. 0.69, p = 0.019), and RTS (0.75 vs. 0.74, p = 0.619). All five scores are excellent triage tools (AUROC ≥ 0.9) for identifying in-hospital mortality for both daytime and nighttime admissions. However, they have only moderate effectiveness (AUROC < 0.9) at identifying severe trauma. The NEWS is the best triage tool for identifying severe trauma for both daytime and nighttime admissions. The MEWS, NEWS, SEWS, and RTS exhibited no significant differences in performance for identifying in-hospital mortality or severe trauma during the daytime or nighttime. However, the mREMS was better at identifying severe trauma during the daytime.