Validation of clinical risk models for predicting COVID-19 severity

Aggarwal, Rahul; Anderson, Timothy S.; Mohanty, Aditya; Pinheiro, Adlin; Ngo, Long; Ahn, Andrew; Peterson, N. A.; Dunlop, Mark; Mawson, Thomas; Lantsman, Taliya; Forbath, Natalia; Stevens, Jennifer P.; Herzig, Shoshana J.

doi:10.1136/emermed-2021-211821

Emerg Med J

2022

DOI: 10.1136/emermed-2021-211821

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Validation of clinical risk models for predicting COVID-19 severity

Rahul Aggarwal

Timothy S. Anderson

Aditya Mohanty

et al.

Abstract: Contributors RA, TSA, JPS, SJH conceptualised the idea. AM, AP, AA, NP, MD, TM, TL, NF were involved in data collection. RA was involved in data analytics. LN was involved in analytical planning and statistical guidance. JPS and SJH supervised the study. All authors were involved in drafting the manuscript, intellectual design and critical revision of the manuscript for intellectual content.

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Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves

Miele¹,

Dajko²,

Savino³

et al. 2023

Intern Emerg Med

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Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83–7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.

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Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves

Miele¹,

Dajko²,

Savino³

et al. 2023

Intern Emerg Med

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show abstract

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Validation of clinical risk models for predicting COVID-19 severity

Cited by 1 publication

References 3 publications

Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves

Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves

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