Predicting COVID-19 prognosis in hospitalized patients based on early status

ABSTRACT Predicting which patients are at greatest risk of severe disease from COVID-19 has the potential to improve patient outcomes and improve resource allocation. We developed machine learning models for predicting COVID-19 prognosis from a retrospective chart review of 969 hospitalized COVID-19 patients at Robert Wood Johnson University Hospital during the first pandemic wave in the United States, focusing on 77 variables from patients’ first day of hospital admission. Our best 77-variable model was better able to predict mortality (receiver operating characteristic area under the curve [ROC AUC] = 0.808) than CURB-65, a commonly used clinical prediction rule for pneumonia severity (ROC AUC = 0.722). After identifying highly predictive variables in our full models using Sh apley a dditive ex p lanations values, we generated two models, platelet count, lactate, age, blood urea nitrogen, aspartate aminotransferase, and C-reactive protein (PLABAC) and platelet count, red blood cell distribution width, age, blood urea nitrogen, lactate, and eosinophil count (PRABLE), that use age and five common laboratory tests to predict mortality (PLABAC: ROC AUC = 0.796, PRABLE: ROC AUC = 0.793), which also outperformed CURB-65. We externally validated PLABAC using data from the National COVID Cohort Collaborative Data Enclave from 7901 hospitalized COVID-19 patients from the pre-vaccination period and 1547 from the vaccination period, yielding ROC AUCs of 0.755 and 0.766, respectively. This study demonstrates that our models can accurately predict COVID-19 outcomes from a small number of variables obtained early in a patient’s hospital stay in patients from institutions around the United States after the initial pandemic wave. These models can serve as a clinical prediction aid and accurately capture a patient’s prognosis using a small number of routinely obtained laboratory values. IMPORTANCE COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help efficiently allocate resources, including ventilators and intensive care unit beds, particularly when hospital systems are strained. Our PLABAC and PRABLE models are unique because they accurately assess a COVID-19 patient’s risk of death from only age and five commonly ordered laboratory tests. This simple design is important because it allows these models to be used by clinicians to rapidly assess a patient’s risk of decompensation and serve as a real-time aid when discussing difficult, life-altering decisions for patients. Our models have also shown generalizability to external populations across the United States. In short, these models are practical, efficient tools to assess and communicate COVID-19 prognosis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.