Performance of the CURB‐65, ISARIC‐4C and COVID‐GRAM scores in terms of severity for COVID‐19 patients

Doğanay, Fatih; Ak, Rohat

doi:10.1111/ijcp.14759

Cited by 18 publications

(11 citation statements)

References 30 publications

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“…Five scores were assessed: two general scores (qSOFA and SOFA) used at ICU, two respiratory scores (CURB-65 and ROX index) and a recently created COVID-19-specific score (4C). The 4C score performed the best to predict 30-day mortality in our cohort with a discriminative performance similar to previous studies including derivation and internal and external validation cohorts 11 14 25–27. In the original derivation cohort and internal validation cohort, the reported AUCs were 0.79 (95% CI 0.78 to 0.79) and 0.77 (95% CI 0.76 to 0.77), respectively.…”

Section: Discussionsupporting

confidence: 84%

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study

et al. 2022

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BackgroundThe SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection.Methods and analysisConsecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses.ResultsA total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62).ConclusionScores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.

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Section: Discussionsupporting

confidence: 84%

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study

et al. 2022

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“…The disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become an international issue due to its social, economic, and health impact [1,2]. Most patients present a mild disease; however, the infection may evolve to pneumonia and critical infection in some cases [1,3].…”

Section: Introductionmentioning

confidence: 99%

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables

Munera¹,

García-Gallo

González³

et al. 2022

ERJ Open Res

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BACKGROUNDPatients with COVID-19 could develop severe disease requiring admission to the Intensive Care Unit (ICU). This manuscript presents a novel method that predicts whether a patient will need admission to the ICU and assess the risk of in-hospital mortality by training a deep learning model that combines a set of clinical variables and features in the Chest-X-Rays.METHODSThis was a prospective diagnostic test study. Patients with confirmed SARS-CoV-2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for Chest-X-ray images using an artificial intelligence (AI) tool and a Random Forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.RESULTSA total of 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, the fraction of inspired oxygen - FiO2 on admission, dyspnoea on admission, and obesity. Moreover, the variables associated with hospital mortality were age, the fraction of inspired oxygen - FiO2 on admission, and dyspnoea. When implementing the AI model to interpret the Chest-X-rays and the clinical variable identified by random forest, we developed a model that accurately predicts ICU admission (AUC:0.92±0.04) and hospital mortality (AUC:0.81±0.06) in patients with confirmed COVID-19.CONCLUSIONSThis automated Chest-X-ray interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 that might require admission to the ICU.

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“…According to Armiñanzas et al [8], COVID-GRAM was more successful than the CURB-65 score in estimating the severity of COVID-19 disease, but both scorings can be used for risk classification. In the study conducted by Doğanay et al [9], the CURB-65 score was found to be more successful than the COVID-GRAM. Rodriguez-Nava et al [10] found that qCSI was successful in predicting ICU hospitalization in COVID-19 patients.…”

Section: Discussionmentioning

confidence: 86%

Evaluation of the effectiveness of quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Score in determining mortality and severity in COVID-19

et al. 2022

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Background/Aim: With the COVID-19 pandemic, the increase in the number of patients admitted to the emergency department has led to an increase in the need for intensive care and mechanical ventilation. Methods that can predict the development of serious disease will allow for a more accurate use of resources. This study was conducted to test the ability of the Quick COVID-19 Severity Index and the COVID-GRAM Critical Illness Risk Score to predict serious disease development and mortality. Methods: This is a prospective cohort study. Among the patients admitted to the emergency department, those hospitalized due to COVID-19 were included in the study. The Quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Scores of the patients were calculated, and the ability of these scores to predict serious illness and mortality was investigated. Results: A total of 556 patients were included in this study. Development of critical illness, described as the need for non-invasive / invasive ventilation or the need for intensive care unit admission, was found significant when the Quick COVID-19 Severity Index was above 5 and the COVID-GRAM Critical Illness Risk Score showed high risk (AUC: 0.927; P < 0.001, AUC: 0.986; P < 0.001, respectively). A Quick COVID-19 Severity Index over 6 and COVID-GRAM Critical Illness Risk Score indicating high risk were found to be associated with mortality (AUC: 0.918, P < 0.001, AUC: 0.982, P < 0.001, respectively). Conclusion: Both the Quick COVID-19 Severity Index and the COVID-GRAM Critical Illness Risk Score can be used to assess severity in COVID-19 patients in the emergency room. However, the COVID-GRAM Critical Illness Risk Score was more successful in differentiating low- and high-risk patients.

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Performance of the CURB‐65, ISARIC‐4C and COVID‐GRAM scores in terms of severity for COVID‐19 patients

Cited by 18 publications

References 30 publications

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables

Evaluation of the effectiveness of quick COVID-19 Severity Index and COVID-GRAM Critical Illness Risk Score in determining mortality and severity in COVID-19

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