Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Introduction: The assessment of disease severity and the prediction of clinical outcomes at early disease stages can contribute to decreased mortality in patients with Coronavirus disease 2019 (COVID-19). This study was conducted to develop and validate a multivariable risk prediction model for mortality with using a combination of computed tomography severity score (CT-SS), national early warning score (NEWS), and quick sequential (sepsis-related) organ failure assessment (qSOFA) in COVID-19 patients.
Methods: We retrospectively collected medical data from 655 adult COVID-19 patients admitted to our hospital between July and November 2020. Data on demographics, clinical characteristics, and laboratory and radiological findings measured as part of standard care at admission were used to calculate NEWS, qSOFA score, CT-SS, peripheral perfusion index (PPI) and shock index (SI). Logistic regression and Cox proportional hazard models were used to predict mortality, which was our primary outcome. The predictive accuracy of distinct scoring systems was evaluated by the receiver-operating characteristic (ROC) curve analysis.
Results: The median age was 50.0 years [333 males (50.8%), 322 females (49.2%)]. Higher NEWS and SI was associated with time-to-death within 90-days, whereas higher age, CT-SS and lower PPI were significantly associated with time-to-death within both 14 days and 90 days in the adjusted Cox regression model. The CT-SS predicted different mortality risk levels within each stratum of NEWS and qSOFA and improved the discrimination of mortality prediction models. Combining CT-SS with NEWS score yielded more accurate 14 days (DBA: -0.048, p = 0.002) and 90 days (DBA: -0.066, p < 0.001) mortality prediction.
Conclusion: Combining severity tools such as CT-SS, NEWS and qSOFA improves the accuracy of predicting mortality in patients with COVID-19. Inclusion of these tools in decision strategies might provide early detection of high-risk groups, avoid delayed medical attention, and improve patient outcomes.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8411577 | PMC |
| http://dx.doi.org/10.1016/j.ajem.2021.08.079 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evaluating Sepsis Mortality Predictions from the Emergency Department: A Retrospective Cohort Study Comparing qSOFA, the National Early Warning Score, and the International Early Warning Score.
J Clin Med
July 2025
School of Medicine, Universidad del Rosario, Bogota 110111, Colombia.
: Sepsis has a high mortality rate, especially in low-income countries. Improving outcomes depends on the early recognition of patients at risk of death. Therefore, rapid and applicable prediction scores are needed in emergency triage.
View Article and Find Full Text PDFPerformance of Early Sepsis Screening Tools for Timely Diagnosis and Antibiotic Stewardship in a Resource-Limited Thai Community Hospital.
Antibiotics (Basel)
July 2025
Department of Preventive and Social Medicine, Faculty of Medicine, Srinakharinwirot University, Bangkok 10110, Thailand.
Background: Early identification of sepsis is critical for improving outcomes, particularly in low-resource emergency settings. In Thai community hospitals, where physicians may not always be available, triage is often nurse-led. Selecting accurate and practical sepsis screening tools is essential not only for timely clinical decision-making but also for timely diagnosis and promoting appropriate antibiotic use.
View Article and Find Full Text PDFClinical utility of severity scores in identifying severe influenza.
Am J Emerg Med
July 2025
Department of Infectious Diseases, Institute of Biomedicine, University of Gothenburg, Sweden; Department of Infectious Diseases, Sahlgrenska University Hospital, Gothenburg, Sweden. Electronic address:
Background: Seasonal influenza can cause substantial morbidity and mortality, but for most patients it is self-limiting with low risk of complications. We aimed to investigate the outcome of hospitalized adults with confirmed influenza and assess the clinical utility of previously developed scoring systems for risk stratification of severe influenza-associated illness in the Emergency Department.
Methods: A retrospective observational cohort study was conducted on adults hospitalized with laboratory-confirmed influenza between 2015 and 2019.
Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models.
Biomedicines
June 2025
The Institute of Computer Technology, Tu Wien University, 1040 Vienna, Austria.
Optimization algorithms are acknowledged to be critical in various fields and dynamical systems since they provide facilitation in identifying and retrieving the most possible solutions concerning complex problems besides improving efficiency, cutting down on costs, and boosting performance. Metaheuristic optimization algorithms, on the other hand, are inspired by natural phenomena, providing significant benefits related to the applicable solutions for complex optimization problems. Considering that complex optimization problems emerge across various disciplines, their successful applications are possible to be observed in tasks of classification and feature selection tasks, including diagnostic processes of certain health problems based on bio-inspiration.
View Article and Find Full Text PDFDiagnoses and critical care outcomes in a rural Tanzanian high dependency unit: A prospective cohort study.
PLoS One
June 2025
Department of Interventions and Clinical trials, Ifakara Health Institute, Ifakara, United Republic of Tanzania.
Background: Data on rural sub-Saharan African high-dependency units (HDU) are lacking. We describe patient's characteristics, diagnoses, and outcomes of patients admitted to a Tanzanian HDU, and identified factors associated with in-hospital mortality.
Methods: This prospective single-center cohort study was conducted in the HDU of a Tanzanian rural referral hospital.