Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir's clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB's effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.

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.

Sociodemographic, clinical and laboratory characteristics and risk factors for mortality of hospitalized COVID-19 patients at alternate care site: a Latin American experience

BACKGROUND

The establishment of Alternate Care Sites (ACS) helped the most severely impacted countries expand their response capability. The aim of this study was to evaluate the clinical characteristics and risk factors associated with the mortality of hospitalized COVID-19 patients at Alternate Care Site in Mexico City.

PATIENTS AND METHODS

A monocentric cohort study was conducted at Mexico City's Temporary Unit COVID-19 (UTC-19). Sociodemographic, clinical, laboratory and treatment variables were included in the analysis.

RESULTS

A total of 4865 patients were included, with a mean age of 49.33 years ± SD 15.28 years (IQR 38 to 60 years); 50.53% were women. 63.53% of the patients presented at least one comorbidity, the most frequent being: obesity (39.94%), systemic arterial hypertension (25.14%), and diabetes mellitus (21.52%). A total of 4549 patients (93.50%) were discharged due to improvement, 64 patients (1.31%) requested voluntary discharge, 39 patients (0.80%) were referred to another unit, and 213 patients (4.37%) died. Factors that were independently and significantly associated with death included male gender (odds ratio [OR], 1.60), age ≥ 50 years (OR 14.75), null or low schooling (OR 3.47), have at least one comorbidity (OR 3.26), atrial fibrillation (OR 22.14). In the multivariate analysis, the lymphopenia ≤ 1 × 103/μL (OR 1.91), and having required steroid treatment (OR 2.85), supplemental oxygen with high-flow nasal cannula (OR 3.12) or invasive mechanical ventilation (OR 42.52), was significantly associated with an increased risk of death.

CONCLUSIONS

This study identified the clinical characteristics and risk factors for mortality of hospitalized COVID-19 patients at ACS in Mexico City.KEY MESSAGESAn Alternate Care Site (ACS) is any building or structure that is temporarily converted or constructed for healthcare use during a public health emergency.Factors associated with death included male gender, age over 50 years, and lower educational attainment (elementary school or less).The findings corroborate the utility of the CALL score as a predictor of mortality; lymphopenia ≤1 × 103/μL was the most relevant biomarker.

The accuracy of the National Early Warning Score 2 in predicting early death in prehospital and emergency department settings: a systematic review and meta-analysis

Background

Many studies have explored the accuracy of the National Early Warning Score 2 (NEWS2) in predicting mortality in prehospital and emergency settings, but their findings are inconsistent. Whether NEWS2 is reliable for the pre-examination and triage of patients in prehospital settings and emergency departments remains debatable. Hence, this study aimed to evaluate the accuracy of NEWS2 in predicting mortality in prehospital settings and emergency departments.

Methods

We searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wan Fang Data, Vip Database and SinoMed from the inception of each database to January 2023. The inclusion criteria: (I) patients in the prehospital settings or emergency departments; (II) the NEWS2 for predicting 2-day mortality, 30-day mortality, and in-hospital mortality; (III) sufficient data, such as sensitivity, specificity, overall survival, and deaths, were provided for the study; (IV) the type of study was accuracy prediction study. Two authors independently extracted data, including authors, year of publication, country of origin, study design, sample size, threshold cutoff values of NEWS2, and mortality. The PROBAST was used to assess the risk of bias in the included studies.

Results

Thirty studies with 185,835 participants were included. Among the 30 included studies, 13 have a high risk of bias, and 17 have a low risk of bias. The pooled sensitivity, specificity and AUC of 2-day mortality (early mortality), 30-day mortality and in-hospital mortality were 0.81 vs. 0.76 vs. 0.72 (95% CI: 0.61, 0.80), 0.81 vs. 0.69 vs. 0.78 (95% CI: 0.49, 0.93) and 0.88 vs. 0.80 vs. 0.78 (95% CI: 0.74, 0.82), respectively.

Conclusions

NEWS2 has excellent sensitivity and specificity in predicting early mortality in patients in the prehospitals setting and emergency departments. Nonetheless, it has poor performance in predicting in-hospital mortality and 30-day mortality. Our findings underpin the use of NEWS2 as a pre-examination and triage tool to predict early death in the prehospital settings and emergency departments. To improve the predictive accuracy, it should be used to monitor patients continuously rather than at a single point-in-time.

One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19

OBJECTIVE

To compare the predictive value of the quick COVID-19 Severity Index (qCSI) and the National Early Warning Score (NEWS) for 90-day mortality amongst COVID-19 patients.

METHODS

Multicenter retrospective cohort study conducted in adult patients transferred by ambulance to an emergency department (ED) with suspected COVID-19 infection subsequently confirmed by a SARS-CoV-2 test (polymerase chain reaction). We collected epidemiological data, clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and use of supplemental oxygen) and hospital variables. The primary outcome was cumulative all-cause mortality during a 90-day follow-up, with mortality assessment monitoring time points at 1, 2, 7, 14, 30 and 90 days from ED attendance. Comparison of performances for 90-day mortality between both scores was carried out by univariate analysis.

RESULTS

From March to November 2020, we included 2,961 SARS-CoV-2 positive patients (median age 79 years, IQR 66-88), with 49.2% females. The qCSI score provided an AUC ranging from 0.769 (1-day mortality) to 0.749 (90-day mortality), whereas AUCs for NEWS ranging from 0.825 for 1-day mortality to 0.777 for 90-day mortality. At all-time points studied, differences between both scores were statistically significant (p < .001).

CONCLUSION

Patients with SARS-CoV-2 can rapidly develop bilateral pneumonias with multiorgan disease; in these cases, in which an evacuation by the EMS is required, reliable scores for an early identification of patients with risk of clinical deterioration are critical. The NEWS score provides not only better prognostic results than those offered by qCSI at all the analyzed time points, but it is also better suited for COVID-19 patients.KEY MESSAGESThis work aims to determine whether NEWS is the best score for mortality risk assessment in patients with COVID-19.AUCs for NEWS ranged from 0.825 for 1-day mortality to 0.777 for 90-day mortality and were significantly higher than those for qCSI in these same outcomes.NEWS provides a better prognostic capacity than the qCSI score and allows for long-term (90 days) mortality risk assessment of COVID-19 patients.

CovHos score for predicting severe respiratory failure in COVID-19 patients presenting at the emergency department

Hospitalization of COVID-19 patients in low-intensity wards may put patients at risk in case of clinical deterioration. We tested CovHos score in predicting severe respiratory failure (SFR) at emergency department (ED) admission. This is a monocentric observational prospective study enrolling adult COVID-19 patients admitted to the ED of IRCCS AOU di Bologna Policlinico S.Orsola in October 2020, both discharged and hospitalized. Patients were then dichotomized based on days from symptoms onset. Main outcome was the occurrence of SRF. Receiver operating characteristic (ROC) analysis was used to identify cut-off and corresponding accuracy. A CovHos cut-off of 22 yielded a sensitivity of 84.7% and specificity of 75.3% in predicting SRF (AUROC 0.856; CI 95% 0.813-0.898). In patients with symptoms onset up to 8 days, a CovHos cut-off of 22 was able to predict SRF with a sensitivity of 91.7% and a specificity of 78.6% (AUROC 0.901; CI 95% 0.861-0.941). Negative predictive value (NPV) was 97.1%. A CovHos score lower than 22, in patients with COVID-19 symptoms onset dated 8 or less days prior to the ED admittance, had a NPV of 97.1% for the development of SRF, meaning that almost none of those patients will evolve into SRF and could be therefore suitable for a lower intensity of care.

Ability of the National Early Warning Score and its respiratory and haemodynamic subcomponents to predict short-term mortality on general wards: a prospective three-centre observational study in Finland

OBJECTIVES

To validate the ability of the National Early Warning Score (NEWS) to predict short-term mortality on hospital wards, with a special reference to the NEWS's respiratory and haemodynamic subcomponents.

DESIGN

A large, 1-year, prospective, observational three-centre study. First measured vital sign datasets on general wards were prospectively collected using a mobile solution system during routine patient care. Area under receiver operator characteristic curves were constructed, and comparisons between ROC curves were conducted with Delong's test for two correlated ROC curves.

SETTING

One university hospital and two regional hospitals in Finland.

PARTICIPANTS

All 19 001 adult patients admitted to 45 general wards in the three hospitals over the 1-year study period. After excluding 102/19 001 patients (0.53%) with data on some vital signs missing, the final cohort consisted of 18 889 patients with full datasets.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was 1-day mortality and secondary outcomes were 2-day and 30-day mortality rates.

RESULTS

Patients' median age was 70 years, 51% were male and 31% had a surgical reason for admission. The 1-day mortality was 0.36% and the 30-day mortality was 3.9%. The NEWS discriminated 1-day non-survivors with excellent accuracy (AUROC 0.91, 95% CI 0.87 to 0.95) and 30-day mortality with acceptable accuracy (0.75, 95% CI 0.73 to 0.77). The NEWS's respiratory rate component discriminated 1-day non-survivors better (0.78, 95% CI 0.72 to 0.84) as compared with the oxygen saturation (0.66, 95% CI 0.59 to 0.73), systolic blood pressure (0.65, 95% CI 0.59 to 0.72) and heart rate (0.67, 95% CI 0.61 to 0.74) subcomponents (p<0.01 in all ROC comparisons). As with the total NEWS, the discriminative performance of the individual score components decreased substantially for the 30-day mortality.

CONCLUSIONS

NEWS discriminated general ward patients at risk for acute death with excellent statistical accuracy. The respiratory rate component is especially strongly associated with short-term mortality.

TRIAL REGISTRATION NUMBER

NCT04055350.

A systematic review of the discrimination and absolute mortality predicted by the National Early Warning Scores according to different cut-off values and prediction windows

BACKGROUND

Although early warning scores were intended to simply identify patients in need of life-saving interventions, prediction has become their commonest metric. This review examined variation in the ability of the National Early Warning Scores (NEWS) in adult patients to predict absolute mortality at different times and cut-offs values.

METHOD

Following PRISMA guidelines, all studies reporting NEWS and NEWS2 providing enough information to fulfil the review's aims were included.

RESULTS

From 121 papers identified, the average area under the Receiver Operating Characteristic curve (AUC) for mortality declined from 0.90 at 24-hours to 0.76 at 30-days. Studies with a low overall mortality had a higher AUC for 24-hour mortality, as did general ward patients compared to patients seen earlier in their treatment. 24-hour mortality increased from 1.8% for a NEWS ≥3 to 7.8% for NEWS ≥7. Although 24-hour mortality for NEWS <3 was only 0.07% these deaths accounted for 9% of all deaths within 24-hours; for NEWS <7 24-hour mortality was 0.23%, which accounted for 44% of all 24-hour deaths. Within 30-days of a NEWS recording 22% of all deaths occurred in patients with a NEWS <3, 52% in patients with a NEWS <5, and 75% in patient with a NEWS <7.

CONCLUSION

NEWS reliably identifies patients most and least likely to die within 24-hours, which is what it was designed to do. However, many patients identified to have a low risk of imminent death die within 30-days. NEWS mortality predictions beyond 24-hours are unreliable.

National early warning score (NEWS) 2 predicts hospital mortality from COVID-19 patients

Background

COVID-19 has a high risk of mortality, especially in patients with comorbid diseases such as cardiac disease, type 2 diabetes mellitus, chronic kidney disease, and hypertension. The National Early Warning Score (NEWS) is a tool that helps in identifying changes in patient conditions that require intensive treatment.

Objective

Analyzing NEWS-2 to identify the risk of death in COVID-19 patients.

Methods

This research was conducted from June to July 2020 by using quota sampling. The number of participants in this study was 112 participants (case group = 56 participants and control group = 56 participants). Participants were assessed for NEWS-2 and evaluated for their treatment outcomes. The analysis used in this study was the Chi-squared test and logistic regression with p < 0.05.

Results

45 participants died of having NEWS-2 score >5, and as many as 50 participants showed an improvement in their condition by having NEWS-2 score 5 (OR = 34.091; p < 0.001). The accuracy of NEWS-2's assessment of mortality of COVID-19 patients had a sensitivity of 80.4% and a specificity of 89.3%. There were several comorbid diseases that had a significant relationship on mortality of COVID-19 patients such as cardiac disease (β = 5.907; 1.107-31.527 95% CI; p = 0.038), T2DM (β = 3.143; 1.269-7.783 95% CI; p = 0.013), CKD (β = 3.851; 1.195-12.416 95% CI; p = 0.024), and hypertension (β = 2.820; 1.075-7.399 95% CI; p = 0.035).

Conclusion

The NEWS-2 can be used to identify the risk of death of COVID-19 patients.

Prediction models for severe manifestations and mortality due to COVID-19: A systematic review

BACKGROUND

Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.

OBJECTIVE

This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.

METHODS

Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.

RESULTS

A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability.

CONCLUSION

Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,¹⁵ and Knight et al.,¹⁸ which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.

Rapid establishment of a dedicated COVID-19 hospital in Mexico city during a public health crisis

Healthcare systems worldwide have adapted and reorganized during the coronavirus disease 2019 (COVID-19) pandemic. Here, we provide a framework based on a public-private partnership that funded, developed, and operated a temporary COVID-19 hospital in Mexico City. We describe the creation of a collaborative network of primary healthcare triage centers and hospitals distributed throughout the city in recognition of demographic and geographic patterns that correlate with COVID-19 infections, including marginalized and impoverished areas of Mexico City. Additionally, we also report the hospital's cumulative outcomes over the 14 months of operation and show that it is feasible to transform a large public venue into a specialized hospital that incorporates a digital platform with robust clinical protocols to provide positive clinical outcomes.

National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in India

Background

While several parameters have emerged as predictors of prognosis of COVID-19, a simple clinical score at baseline might help early risk stratification. We determined the ability of National Early Warning Score 2 (NEWS2) to predict poor outcomes among adults with COVID-19.

Methods

A prospective study was conducted on 399 hospitalised adults with confirmed SARS-CoV-2 infection between August and December 2020. Baseline NEWS2 score was determined. Primary outcome was poor outcomes defined as need for mechanical ventilation or death within 28 days. The sensitivity, specificity and Area under the curve were determined for NEWS2 scores of 5 and 6.

Results

Mean age of patients was 55.5 ± 14.8 years and 275 of 399 (68.9%) were male. Overall mortality was 3.8% and 7.5% had poor outcomes. Median (interquartile range) NEWS2 score at admission was 2 (0–6). Sensitivity and specificity of NEWS 2 of 5 or more in predicting poor outcomes was 93.3% (95% CI: 76.5–98.8) and 70.7% (95% CI: 65.7–75.3) respectively [area under curve 0.88 (95% CI: 0.847–0.927)]. Age, baseline pulse rate, baseline oxygen saturation, need for supplemental oxygen and ARDS on chest X ray were independently associated with poor outcomes.

Conclusions

NEWS2 score of 5 or more at admission predicts poor outcomes in patients with COVID-19 with good sensitivity and can easily be applied for risk stratification at baseline. Further studies are needed in the Indian setting to validate this simple score and recommend widespread use.

Predictive Value of an Age-Based Modification of the National Early Warning System in Hospitalized Patients With COVID-19

BACKGROUND

Early recognition of high-risk patients with coronavirus disease 2019 (COVID-19) may improve outcomes. Although many predictive scoring systems exist, their complexity may limit utility in COVID-19. We assessed the prognostic performance of the National Early Warning Score (NEWS) and an age-based modification (NEWS+age) among hospitalized COVID-19 patients enrolled in a prospective, multicenter US Military Health System (MHS) observational cohort study.

METHODS

Hospitalized adults with confirmed COVID-19 not requiring invasive mechanical ventilation at admission and with a baseline NEWS were included. We analyzed each scoring system's ability to predict key clinical outcomes, including progression to invasive ventilation or death, stratified by baseline severity (low [0-3], medium [4-6], and high [≥7]).

RESULTS

Among 184 included participants, those with low baseline NEWS had significantly shorter hospitalizations (P < .01) and lower maximum illness severity (P < .001). Most (80.2%) of low NEWS vs 15.8% of high NEWS participants required no or at most low-flow oxygen supplementation. Low NEWS (≤3) had a negative predictive value of 97.2% for progression to invasive ventilation or death; a high NEWS (≥7) had high specificity (93.1%) but low positive predictive value (42.1%) for such progression. NEWS+age performed similarly to NEWS at predicting invasive ventilation or death (NEWS+age: area under the receiver operating characteristics curve [AUROC], 0.69; 95% CI, 0.65-0.73; NEWS: AUROC, 0.70; 95% CI, 0.66-0.75).

CONCLUSIONS

NEWS and NEWS+age showed similar test characteristics in an MHS COVID-19 cohort. Notably, low baseline scores had an excellent negative predictive value. Given their easy applicability, these scoring systems may be useful in resource-limited settings to identify COVID-19 patients who are unlikely to progress to critical illness.

Lactate improves the predictive ability of the National Early Warning Score 2 in the emergency department

AIMS

The aim of this study was to compare the ability to predict 2-, 7-, 14-, and 30-day in-hospital mortality of lactate vs the National Early Warning Score 2 (NEWS2) vs the arithmetic sum of the NEWS2 plus the numerical value of lactate (NEWS2-L).

METHODS

This was a prospective, multicentric, emergency department delivery, pragmatic cohort study. To determine the predictive capacity of lactate, we calculated the NEWS2 and NEWS2-L in adult patients (aged >18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon (Spain) between November 1, 2019, and September 30, 2020. The area under the receiver operating characteristic curve of each of the scales was calculated in terms of mortality for every time frame (2, 7, 14, and 30 days). We determined the cut-off point of each scale that offered highest sensitivity and specificity using the Youden index.

RESULTS

A total of 1716 participants were included, and the in-hospital mortality rates at 2, 7, 14, and 30 days were of 7.8% (134 cases), 11.6% (200 cases), 14.2% (243 cases), and 17.2% (295 cases), respectively. The best cut-off point determined in the NEWS2 was 6.5 points (sensitivity of 97% and specificity of 59%), and for lactate, the cut-off point was 3.3 mmol/L (sensitivity of 79% and specificity of 72%). Finally, the combined NEWS2-L showed a cut-off point of 11.7 (sensitivity of 86% and a specificity of 85%). The area under the receiver operating characteristic curve of the NEWS2, lactate, and NEWS2-L in the validation cohort for 2-day mortality was 0.889, 0.856, and 0.923, respectively (p<0.001 in all cases).

CONCLUSIONS

The new score generated, NEWS2-L, obtained better statistical results than its components (NEWS2 and lactate) separately.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

CONCLUSIONS

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

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.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

CONCLUSION

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

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.

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

We sought to validate prognostic scores in coronavirus disease 2019 including National Early Warning Score, Modified Early Warning Score, and age-based modifications, and define their performance characteristics.

DESIGN

We analyzed prospectively collected data from the Adaptive COVID-19 Treatment Trial. National Early Warning Score was collected daily during the trial, Modified Early Warning Score was calculated, and age applied to both scores. We assessed prognostic value for the end points of recovery, mechanical ventilation, and death for score at enrollment, average, and slope of score over the first 48 hours.

SETTING

A multisite international inpatient trial.

PATIENTS

A total of 1,062 adult nonpregnant inpatients with severe coronavirus disease 2019 pneumonia.

INTERVENTIONS

Adaptive COVID-19 Treatment Trial 1 randomized participants to receive remdesivir or placebo. The prognostic value of predictive scores was evaluated in both groups separately to assess for differential performance in the setting of remdesivir treatment.

MEASUREMENTS AND MAIN RESULTS

For mortality, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.60-0.68), and improved with addition of age (c-index, 0.66-0.74). For recovery, baseline National Early Warning Score and Modified Early Warning Score demonstrated somewhat better prognostic ability (c-index, 0.65-0.69); however, National Early Warning Score+age and Modified Early Warning Score+age further improved performance (c-index, 0.68-0.71). For deterioration, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.59-0.69) and improved with addition of age (c-index, 0.63-0.70). All prognostic performance improvements due to addition of age were significant (p < 0.05).

CONCLUSIONS

In the Adaptive COVID-19 Treatment Trial 1 cohort, National Early Warning Score and Modified Early Warning Score demonstrated moderate prognostic performance in patients with severe coronavirus disease 2019, with improvement in predictive ability for National Early Warning Score+age and Modified Early Warning Score+age. Area under receiver operating curve for National Early Warning Score and Modified Early Warning Score improved in patients receiving remdesivir versus placebo early in the pandemic for recovery and mortality. Although these scores are simple and readily obtainable in myriad settings, in our data set, they were insufficiently predictive to completely replace clinical judgment in coronavirus disease 2019 and may serve best as an adjunct to triage, disposition, and resourcing decisions.

Modified PRIEST score for identification of very low-risk COVID patients

Background

COVID-19 transmission remains high around the world, and severe local outbreaks continue to occur. Prognostic tools may be useful in crisis conditions as risk stratification can help determine resource allocation. One published tool, the Pandemic Respiratory Infection Emergency System Triage Severity Score, seems particularly promising because of its predictive ability and ease of application at the bedside. We sought to understand the performance of a modified version of this score (mPRIEST) in our institution for identifying patients with a greater than minimal risk for adverse outcome (death or organ support) at 30 days after index visit.

Methods

Consecutive visits at two northern Manhattan EDs with a new diagnosis of symptomatic COVID-19 were identified between November and December of 2020. Demographic variables and clinical characteristics were obtained from chart review. Outcomes were obtained from chart review and follow-up phone call.

Results

Outcomes were available on 306 patients. The incidence of death or mechanical ventilation at 30 days for patients in patients with mPRIEST above the threshold value was 43/181 (23.8%), and for patients below 1/125 (0.8%). The sensitivity of the score for adverse outcome was 97.7% (95% CI: 93.3% to 100%).

Conclusions

This data suggests the mPRIEST score, which can be calculated from clinical variables alone, has potential for use in EDs to identify patients at very low risk for adverse outcomes within 30 days of COVID diagnosis. This should be confirmed in larger formal validation studies in diverse settings.