ArticleJournal of the American College of Emergency Physicians open2021
An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID-19.
Article in Journal of the American College of Emergency Physicians open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Hospital Utilization in Ambulatory Patients With COVID-19 With History of Tobacco Use.AJPM focus · 2025Article
- Controlling nosocomial transmission of respiratory infections in neurological wards: insights from COVID-19 pandemic data.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Development and validation of a predictive scoring system for in-hospital mortality in COVID-19 Egyptian patients: a retrospective study.Scientific reports · 2022Article
- Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Hospitalizations from covid-19: a health planning tool.Revista de saude publica · 2022Review
- An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID-19.Journal of the American College of Emergency Physicians open · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-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.
methodsWe 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.
resultsA 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).
conclusionsUsing 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.
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