Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?
A logistic regression model utilizing 12 easily accessible variables from EMRs outperformed ML models in predicting the risk probability of QTc prolongation in cancer patients newly treated with five oral TKIs. These findings serve as a valuable clinical reference for integrating digital monitoring into cardiovascular care for cancer survivors undergoing targeted therapy with TKIs. They also underscore the importance of screening baseline ECG before initiating TKIs to assess the risk of QTc prolongation, facilitating early intervention and prevention in the future.
Authors
- Hsiang‐Wen Lin (ORCID: https://orcid.org/0000-0002-6771-6746)
- Tzu‐Pei Yeh (ORCID: https://orcid.org/0000-0002-2620-0591)
- Chien‐Ning Hsu (ORCID: https://orcid.org/0000-0001-7470-528X)
- Tien-Chao Lin
- Liang‐Chih Liu (ORCID: https://orcid.org/0000-0002-2594-0109)
- Yu-Chieh Chen (ORCID: https://orcid.org/0000-0002-1838-276X)
- Chen-Yuan Lin
Institutions
- Kaohsiung Medical University (TW)
- China Medical University (TW)
- University of Illinois Chicago (US)
- Kaohsiung Chang Gung Memorial Hospital (TW)
- China Medical University Hospital (TW)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-08-15
- DOI
- https://doi.org/10.1186/s12911-025-03091-8
- Citations
- 1
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- China Medical University Hospital
- China Medical University
- National Science and Technology Council
- Division of Materials Research