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Zhang D, Tong J, Jing N, Yang Y, Luo C, Lu Y, Christakis DA, Güthe D, Hornig M, Kelleher KJ, Morse KE, Rogerson CM, Divers J, Carroll RJ, Forrest CB, Chen Y. Learning competing risks across multiple hospitals: one-shot distributed algorithms. J Am Med Inform Assoc 2024; 31:1102-1112. [PMID: 38456459 PMCID: PMC11031234 DOI: 10.1093/jamia/ocae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/30/2023] [Accepted: 02/03/2024] [Indexed: 03/09/2024] Open
Abstract
OBJECTIVES To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents. MATERIALS AND METHODS Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data. RESULTS The meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data. DISCUSSION Our proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions. CONCLUSION Our study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework.
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Affiliation(s)
- Dazheng Zhang
- The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Jiayi Tong
- The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Naimin Jing
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, NJ 07065, United States
| | - Yuchen Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Chongliang Luo
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- Division of Public Health Sciences, Washington University School of Medicine in St Louis, St Louis, MO 63110, United States
| | - Yiwen Lu
- The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States
| | | | - Diana Güthe
- Survivor Corps, Washington, DC 20814, United States
| | - Mady Hornig
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, United States
| | - Kelly J Kelleher
- Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, United States
| | - Keith E Morse
- Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA 94304, United States
| | - Colin M Rogerson
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Jasmin Divers
- Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, NY 11501, United States
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, College Station, TX 77843, United States
| | - Christopher B Forrest
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Yong Chen
- The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
- The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States
- Penn Institute for Biomedical Informatics (IBI), Philadelphia, PA 19104, United States
- Leonard Davis Institute of Health Economics, Philadelphia, PA 19104, United States
- Penn Medicine Center for Evidence-based Practice (CEP), Philadelphia, PA 19104, United States
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