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In Journal of the American College of Emergency Physicians open

Background : COVID-19 has caused an unprecedented global health emergency. The strains of such a pandemic can overwhelm hospital capacity. Efficient clinical decision-making is crucial for proper healthcare resource utilization in this crisis. Using observational study data, we set out to create a predictive model that could anticipate which COVID-19 patients would likely be admitted and developed a scoring tool that could be used in the clinical setting and for population risk stratification.

Methods : We retrospectively evaluated data from COVID-19 patients across a network of 6 hospitals in northeastern Pennsylvania. Analysis was limited to age, gender, and historical variables. After creating a variable importance plot, we chose a selection of the best predictors to train a logistic regression model. Variable selection was done using a lasso regularization technique. Using the coefficients in our logistic regression model, we then created a scoring tool and validated the score on a test set data.

Results : A total of 6485 COVID-19 patients were included in our analysis, of which 707 were hospitalized. The biggest predictors of patient hospitalization included age, a history of hypertension, diabetes, chronic heart disease, gender, tobacco use, and chronic kidney disease. The logistic regression model demonstrated an AUC of 0.81. The coefficients for our logistic regression model were used to develop a scoring tool. Low-, intermediate-, and high-risk patients were deemed to have a 3.5%, 26%, and 38% chance of hospitalization, respectively. The best predictors of hospitalization included age (odds ratio [OR] = 1.03, confidence interval [CI] = 1.02-1.03), diabetes (OR = 2.08, CI = 1.69-2.57), hypertension (OR = 2.36, CI = 1.90-2.94), chronic heart disease (OR = 1.53, CI = 1.22-1.91), and male gender (OR = 1.32, CI = 1.11-1.58).

Conclusions : Using retrospective observational data from a 6-hospital network, we determined risk factors for admission and developed a predictive model and scoring tool for use in the clinical and population setting that could anticipate admission for COVID-19 patients.

Chen Zhe, Russo Nicholas W, Miller Matthew M, Murphy Robert X, Burmeister David B


COVID, machine learning, predictive model, risk of admission