Fibrosis-4 Predicts the Need for Mechanical Ventilation in a National Multiethnic Cohort of Corona Virus Disease 2019.
Richard K SterlingDongho ShinYongyun ShinEvan FrenchMichael P StevensJasmohan Singh BajajMarjolein DeWitArun J SanyalPublished in: Hepatology communications (2021)
Simple tests of routine data are needed for those with severe acute respiratory syndrome coronavirus 2, which causes corona virus disease 2019 (COVID-19), to help identify those who may need mechanical ventilation (MV). In this study, we aimed to determine if fibrosis-4 (FIB-4) is associated with the need for MV in patients with COVID-19 and if there is an association to determine the optimal FIB-4 cutoff. This was a retrospective, national, multiethnic cohort study of adults seen in an ambulatory or emergency department setting who were diagnosed with COVID-19. We used the TriNetX platform for analysis. Measures included demographics, comorbid diseases, and routine laboratory tests. A total of 4,901 patients with COVID-19 were included. Patients had a mean age of 56, 48% were women, 42% were obese, 38% were white, 40% were black, 15% had cardiac disease, 39% had diabetes mellitus, 20% had liver disease, and 50% had respiratory disease. The need for MV was 6%. The optimal FIB-4 cutoff for the need for MV was 3.04 (area under the curve, 0.735), which had sensitivity, specificity, and positive and negative predictive values of 42%, 77%, 11%, and 95%, respectively, with 93% accuracy. When stratified by race, increased FIB-4 remained associated with the need for MV in both white and black patients. Conclusion: FIB-4 can be used by frontline providers to identify patients that may require MV.
Keyphrases
- mechanical ventilation
- end stage renal disease
- emergency department
- newly diagnosed
- coronavirus disease
- ejection fraction
- chronic kidney disease
- respiratory syndrome coronavirus
- intensive care unit
- peritoneal dialysis
- type diabetes
- prognostic factors
- blood pressure
- acute respiratory distress syndrome
- pregnant women
- metabolic syndrome
- skeletal muscle
- machine learning
- quality improvement
- left ventricular
- patient reported outcomes
- clinical practice
- patient reported
- respiratory failure
- obese patients