Validation of The 4C Deterioration Model for COVID-19 in a UK Teaching Hospital During Wave 2

Cowan, Sarah L.; Wiegand, Martin; Preller, Jacobus; Goudie, Robert J. B.

doi:10.1016/j.amjms.2021.08.006

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…In line with other validation studies [20][21][22][23][24][25], the respective event rate in our cohort was lower than in the original studies. One reason might be that the patients in our cohort were younger than the original cohort.…”

Section: Discussionsupporting

confidence: 91%

External Validation of the Modified 4C Deterioration Model and 4C Mortality Score for COVID-19 Patients in a Swiss Tertiary Hospital

et al. 2022

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Prognostic models to predict the deterioration and mortality risk in COVID-19 patients are utterly needed to assist in informed decision making. Most of these models, however, are at high risk of bias, model overfitting, and unclear reporting. Here, we aimed to externally validate the modified (urea was omitted) 4C Deterioration Model and 4C Mortality Score in a cohort of Swiss COVID-19 patients and, second, to evaluate whether the inclusion of the neutrophil-to-lymphocyte ratio (NLR) improves the predictive performance of the models. We conducted a retrospective single-centre study with adult patients hospitalized with COVID-19. Both prediction models were updated by including the NLR. Model performance was assessed via the models’ discriminatory performance (area under the curve, AUC), calibration (intercept and slope), and their performance overall (Brier score). For the validation of the 4C Deterioration Model and Mortality Score, 546 and 527 patients were included, respectively. In total, 133 (24.4%) patients met the definition of in-hospital deterioration. Discrimination of the 4C Deterioration Model was AUC = 0.78 (95% CI 0.73–0.82). A total of 55 (10.44%) patients died in hospital. Discrimination of the 4C Mortality Score was AUC = 0.85 (95% CI 0.79–0.89). There was no evidence for an incremental value of the NLR. Our data confirm the role of the modified 4C Deterioration Model and Mortality Score as reliable prediction tools for the risk of deterioration and mortality. There was no evidence that the inclusion of NLR improved model performance.

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Section: Discussionsupporting

confidence: 91%

External Validation of the Modified 4C Deterioration Model and 4C Mortality Score for COVID-19 Patients in a Swiss Tertiary Hospital

et al. 2022

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“…This score performed well in both large development and validation cohorts (AUROC 0.77 and 0.79, respectively) and has now been externally validated in several countries 58–64. Similarly the point-of-admission ISARIC 4C Deterioration score3 has been externally validated 58 60 65 66. Promising machine-learning alternatives have also been recently proposed,67 68 although these have not yet been independently validated in contrast to the 4C scores.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

et al. 2022

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ObjectivesTo develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.DesignIn this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.SettingAll data used in this study were obtained from a single UK teaching hospital.ParticipantsWe developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.Primary and secondary outcome measuresThe primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.ResultsOur final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).ConclusionsOur model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.Trial registrationThe study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com).

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External validation of the 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score in patients hospitalized with COVID-19 pneumonia in Greece

Paraskevas

Michailides

Karalis

et al. 2022

Romanian Journal of Internal Medicine

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Background: Prognostic scores can be used to facilitate better management of patients suffering from life-threatening diseases, provided that they have been tested in the population of interest. Aim: To perform external validation of the 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score. Study Design: Prospective Observational Study. Methods: Patients hospitalized with COVID-19 pneumonia in a tertiary hospital in Greece were enrolled in the study. The prognostic scores were calculated based on hospital admission data and ROC curve analysis was performed. We assessed a composite outcome of either in-hospital death or need for invasive ventilation. Results: Both 4C and PRIEST scores showed good discriminative ability with an AUC value of 0.826 (CI 95%: 0.765-0.887) and 0.852 (CI 95%: 0.793-0.910) respectively. Based on the Youden Index the optimal cut-off for the 4C score was 11 (Sensitivity 75%, Specificity 75.5%) and 10 for the PRIEST score (Sensitivity 83% and Specificity 69.4%). Calibration was adequate for both scores, except for the low and very high risk groups in the PRIEST score. Conclusion: The 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score can be used for early identification of patients with poor prognosis in a Greek population cohort hospitalized with COVID-19.

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Validation of The 4C Deterioration Model for COVID-19 in a UK Teaching Hospital During Wave 2

Cited by 3 publications

References 9 publications

External Validation of the Modified 4C Deterioration Model and 4C Mortality Score for COVID-19 Patients in a Swiss Tertiary Hospital

External Validation of the Modified 4C Deterioration Model and 4C Mortality Score for COVID-19 Patients in a Swiss Tertiary Hospital

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

External validation of the 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score in patients hospitalized with COVID-19 pneumonia in Greece

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