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Journal of Diabetes and Clinical Research

ISSN: 2689-2839

Research Article Open Access
Volume 4 | Issue 1 | DOI: https://doi.org/10.33696/diabetes.4.050

A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction for People Over 65

Chen Dun1,*, Christi M. Walsh1, Sunjae Bae1,3,4, Amesh Adalja5, Eric Toner5, Timothy A. Lash6, Farah Hashim1, Joseph Paturzo1, Dorry L. Segev1,3, Martin A. Makary1,2
  • 1Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  • 2Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public health, Baltimore, MD, USA
  • 3Department of Epidemiology, Johns Hopkins Bloomberg School of Public health, Baltimore, MD, USA
  • 4Department of Biostatistics, Johns Hopkins Bloomberg School of Public health, Baltimore, MD, USA
  • 5Center for Health Security, Johns Hopkins University, Baltimore, MD, USA
  • 6West Health Institute, San Diego, CA, USA
+ Affiliations - Affiliations

Corresponding Author

Chen Dun, cdun1@jhmi.edu

Received Date: January 14, 2022

Accepted Date: February 11, 2022

Citation:

Dun C, Walsh CM, Bae S, Adalja A, Toner E, Lash TA, et al. A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction for People Over 65. J Diabetes Clin Res. 2022;4(1):10-16.


Copyright: © 2022 Dun C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: The global outbreak of COVID-19 has resulted in over 378 million infections and worldwide casualties have surpassed 5.6 million. Identifying individuals at highest risk of COVID-19 death may help risk evaluation and health service provision. Personalized risk prediction that uses a broad range of comorbidities requires a cohort size larger than that reported in prior studies.

Methods: Medicare claims data was used to identify patients age 65 years or older with diagnosis of COVID-19 between April 1, 2020 and August 31, 2020. Demographic characteristics, chronic medical conditions, and other patient risk factors that existed before the advent of COVID-19 were identified. A random forest model was used to empirically explore factors associated with COVID-19 death. The independent impact of factors identified were quantified using multivariate logistic regression with random effects.

Results: We identified 534,023 COVID-19 patients of whom 38,066 had an inpatient death. Demographic characteristics associated with COVID-19 death included advanced age (85 years or older: aOR: 2.07; 95% CI, 1.99-2.16), male sex (aOR, 1.88; 95% CI, 1.82-1.94), and non-white race (Hispanic: aOR, 1.74; 95% CI, 1.66-1.83). Leading comorbidities associated with COVID-19 mortality included sickle cell disease (aOR, 1.73; 95% CI, 1.21-2.47), chronic kidney disease (aOR, 1.32; 95% CI, 1.29-1.36), leukemias and lymphomas (aOR, 1.22; 95% CI, 1.14-1.30), heart failure(aOR, 1.19; 95% CI, 1.16-1.22), and diabetes (aOR, 1.18; 95% CI, 1.15-1.22).

Conclusions: We created a personalized risk prediction calculator to identify candidates for early vaccine and therapeutics allocation (www. predictcovidrisk.com). These findings may be used to protect those at greatest risk of death from COVID-19.

Keywords

COVID-19, Machine learning, Risk prediction

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A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction for People Over 65

The global outbreak of COVID-19 has resulted in over 378 million infections and worldwide deaths have surpassed 5.6 million. In the United States, there have been over 75 million confirmed cases and over 886,000 deaths as of February 1, 2022.

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