| Title: | Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population |
| Journal: | npj Digital Medicine |
| Published: | 24 Aug 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39181999/ |
| DOI: | https://doi.org/10.1038/s41746-024-01218-1 |
| URL: | http://dx.doi.org/10.1038/s41746-024-01218-1 |
Publication 13172
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval in patients with high-risk genotypes. We developed a deep learning approach combining electrocardiogram (ECG) waveform and electronic health record data to assess whether patients had pathogenic variants causing LQTS. We defined patients with high-risk genotypes as having ≥1 pathogenic variant in one of the LQTS-susceptibility genes. We trained the model using data from United Kingdom Biobank (UKBB) and then fine-tuned in a racially/ethnically diverse cohort using Mount Sinai BioMe Biobank. Following group-stratified 5-fold splitting, the fine-tuned model achieved area under the precision-recall curve of 0.29 (95% confidence interval [CI] 0.28-0.29) and area under the receiver operating curve of 0.83 (0.82-0.83) on independent testing data from BioMe. Multimodal fusion learning has promise to identify individuals with pathogenic genetic mutations to enable patient prioritization for further work up.</p>
13 Authors
- Joy Jiang
- Ha My Thi Vy
- Alexander Charney
- Patricia Kovatch
- Vivek Reddy
- Pushkala Jayaraman
- Ron Do
- Rohan Khera
- Sumeet Chugh
- Deepak L. Bhatt
- Akhil Vaid
- Joshua Lampert
- Girish Nitin Nadkarni
Enabling scientific discoveries that improve human health