Burmeister, David B, DO, FACEP, CPE

Chen, Zhe, MD

Miller, Matthew M, DO

Murphy, Robert X, JR, MD, MS

Russo, Nicholas, BS

English

Lehigh Valley Health Network: LVHN Scholarly Works

Lehigh Valley Health Network LVHN Scholarly Works Department of Medicine A Scoring Tool and Predictive Model to Detect Risk of Hospitalization from COVID-19 Nicholas Russo BS USF MCOM- LVHN Campus, nicholas.russo@lvhn.org Zhe Chen MD Lehigh Valley Health Network, zhe.chen@lvhn.org Matthew M. Miller DO Lehigh Valley Health Network, Matthew_M.Miller@lvhn.org Robert X. Murphy JR, MD, MS Lehigh Valley Health Network, Robert.Murphy@lvhn.org David B. Burmeister DO, FACEP, CPE Lehigh Valley Health Network, david_b.burmeister@lvhn.org Follow this and additional works at: https://scholarlyworks.lvhn.org/medicine  Part of the Medicine and Health Sciences Commons Published In/Presented At Russo, N., Chen, Z., Miller, M., Murphy, R., & Burmeister, D. (2021, February). A Scoring Tool and Predictive Model to Detect Risk of Hospitalization from COVID-19. [Poster presentation]. Health Datapalooza and National Health Policy Conference, Virtual. This Poster is brought to you for free and open access by LVHN Scholarly Works. It has been accepted for inclusion in LVHN Scholarly Works by an authorized administrator. For more information, please contact LibraryServices@lvhn.org. © 2020 Lehigh Valley Health NetworkLVHN.orgA Scoring Tool and Predictive Model to  Detect Risk of Hospitalization from COVID-19Nicholas W. Russo, BS;a Zhe Chen, MD;a Matthew M. Miller, DO, MBA, MS;a Robert X. Murphy Jr., MD;b David B. Burmeister, DO, MBAaaLehigh Valley Health Network Department of Emergency and Hospital Medicine/University of South Florida Morsani College of Medicine, Lehigh Valley Campus, Allentown, Pa.bLehigh Valley Health Network, Department of Surgery, Division of Plastic Surgery, Allentown, Pa.Health or Health Care Problem/Challenge Being TackledCOVID-19 has caused an unprecedented global health emergency.  While the typical intensive care unit occupancy is 60-80%, the strains  of a pandemic such as that of COVID-19 can overwhelm hospital capacity. Efficient clinical decision-making is crucial for proper health  care resource utilization in this crisis.Innovation/Advance AchievedWe created a machine learning (ML) predictive model using retrospective data to try to predict which COVID-19 patients will likely be admitted. This resulted in the subsequent creation of a feasible scoring tool which can be used in the clinical setting.acknowledgementsThe authors would like to acknowledge Erin Shigo, BA for her scholarly assistance with this abstract.Types of Data UsedWe used retrospective data of COVID-19 patients. Analysis was limited to age, gender, and historical variables. We created a Variable Importance Plot and chose a selection of the best predictors to train a logistic regression and random forest model. We then created a scoring tool  and validated the score on the test set data.Findings/Lessons LearnedA total of 6,485 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 and random forest models demonstrated an AUC of 0.81 and 0.76, respectively. A second logistic regression model was created which was used to develop a scoring tool. Patients with low-, intermediate-, and high-risk were deemed to have a 3.5%, 26%, and 38% chance of hospitalization, respectively.Next Steps/Market StrategyOur scoring needs validation at other sites in order to be generalizable. Next steps should involve validating our model in other institutions. Future direction can be aimed at the impact of closer monitoring may have in the different risk groups. Further data will inform the ML algorithm which may result in a more effective model. Additionally, age-adjusted risk stratification may provide more efficient predictive capabilities in the  non-elderly and elderly populations.6,485 COVID-19 PATIENTS  ANALYZED707 COVID-19 PATIENTS  HOSPITALIZED

A Scoring Tool and Predictive Model to Detect Risk of Hospitalization from COVID-19

https://scholarlyworks.lvhn.org/cgi/viewcontent.cgi?article=2854&context=medicine

A Scoring Tool and Predictive Model to Detect Risk of Hospitalization from COVID-19

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