ArticleJournal of emergency and critical care medicine (Hong Kong, China)2021
Severity of illness scores at presentation predict ICU admission and mortality in COVID-19.
Article in Journal of emergency and critical care medicine (Hong Kong, China), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Circulating mid-regional proadrenomedullin is a predictor of mortality in patients with COVID-19: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Four-organ SOFA score as a potential tool to triage use of continuous kidney replacement therapy in COVID-19 patients with acute kidney injury: a retrospective two-center study.Renal failure · 2026Article
- Assessment of muscle wasting in intensive care unit patients with and without COVID-19 using ultrasound imaging and bioimpedance analysis.BMC anesthesiology · 2026Article
- MDR Bacteremia in the Critically Ill During COVID-19: The MARTINI Study.Pathogens (Basel, Switzerland) · 2025Observational
- Multiplex Targeted Proteomic Analysis of Cytokine Ratios for ICU Mortality in Severe COVID-19.Proteomes · 2025Article
- A novel scoring system for evaluating mortality risk of patients with sepsis during early hospitalization.BMC infectious diseases · 2025Article
- Evaluation of score-based tertiary triage policies during the COVID-19 pandemic: simulation study with real-world intensive care data.Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Validation of the APACHE‑II scoring system in critically ill patients diagnosed with COVID‑19 and admitted at a regional‑level hospital intensive care unit: A retrospective study.The Southern African journal of critical care : the official journal of the Critical Care Society · 2025Article
- Comparative Analysis of qSOFA, PRIEST, PAINT, and ISARIC4C Scores in Predicting Severe COVID-19 Outcomes Among Patients Aged over 75 Years.Diseases (Basel, Switzerland) · 2024Article
- Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms.Scientific reports · 2024Article
- Lessons for the next pandemic: analysis of the timing and outcomes including post-discharge decannulation rates for tracheostomy in severe COVID-19 respiratory failure.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Article
- Profibrotic COVID-19 subphenotype exhibits enhanced localized ER-dependent HSP47Journal of molecular and cellular cardiology · 2023Article
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- Predicting medical events and ICU requirements using a multimodal multiobjective transformer network.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study.Clinical imaging · 2022Article
- A Multicenter Evaluation of the Seraph 100 Microbind Affinity Blood Filter for the Treatment of Severe COVID-19.Critical care explorations · 2022Article
- What we know and still ignore on COVID-19 immune pathogenesis and a proposal based on the experience of allergic disorders.Allergy · 2022Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe COVID-19 pandemic has overwhelmed hospital systems in multiple countries and necessitated caring for patients in atypical healthcare settings. The goal of this study was to ascertain if the conventional critical care severity scores qSOFA, SOFA, APACHE-II, and SAPS-II could predict which patients admitted to the hospital from an emergency department would eventually require intensive care.
methodsThis single-center, retrospective cohort study enrolled patients admitted to Vanderbilt University Hospital from the emergency room with symptomatic, confirmed COVID-19 infection between March 8, 2020 through May 15, 2020. Clinical phenotyping was performed by chart abstraction, and the correlation of the qSOFA, SOFA, APACHE-II, and SAPS-II scores for the primary endpoint of ICU admission and secondary endpoint of in-hospital mortality was evaluated.
resultsDuring the study period, 128 patients were admitted to Vanderbilt University Hospital from the emergency room with COVID-19. Of these, 39 patients eventually required intensive care; the remaining 89 were discharged from the medical ward. All severity of illness scores demonstrated at least moderate ability to identify patients who would die or require ICU admission. Of the three severity of illness scores assessed, the APACHE-II score performed best with an AUC of 0.851 (95% CI: 0.786 to 0.917) for identifying patient that would require ICU admission. No patient with an APACHE-II score at the time of presentation less than 8 or qSOFA of 0 required intensive care unit (ICU) admission. All patients with an APACHE-II score less than 10 or qSOFA score of 0 survived to hospital discharge.
conclusionsThe APACHE-II score accurately predicts the eventual need for ICU admission. This may allow for risk-stratification of patients safe to treat in alternative health care settings and prognostic enrichment to accelerate clinical trials of COVID-19 therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.