Logistic Regression and Machine Learning Algorithms for the Risk Prediction of Perioperative Adverse Cardiovascular Events in Elderly Patients
This study established risk prediction models for patients with PACEs, based on the patients' medical records, with good predictive accuracy. This model is expected to provide a scientific basis for quickly formulating or adjusting the diagnostic and treatment plans for elderly patients and provide clinical strategies for PACEs prevention, intervention, and monitoring, which could potentially reduce the mortality risk of patients with PACEs.
Authors
- Silin Zhu (ORCID: https://orcid.org/0009-0007-0569-2658)
- Xiyezi Diao
- Xiao Yan Li
- Guang You Duan
- He Huang (ORCID: https://orcid.org/0000-0001-9307-871X)
- Lin Li (ORCID: https://orcid.org/0000-0003-0426-6546)
- Ning Wang (ORCID: https://orcid.org/0000-0003-3238-5083)
- Yan Gao (ORCID: https://orcid.org/0000-0002-1268-2999)
- Li Sun
Institutions
- Dalian Medical University (CN)
- National Health and Family Planning Commission (CN)
- Central China Normal University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Tianjin Municipal Research Institute for Family Planning (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Aging Medicine
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/agm2.70037
- Citations
- 1
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Key Research and Development Program of China