Hypertrophic Cardiomyopathy (HCM), Left Ventricular Hypertrophy
Conditions
Brief summary
By harnessing artificial intelligence to decode the 12-lead electrocardiogram, the project will enable precise ECG-based phenotyping of hypertrophic cardiomyopathy-accurately classifying septal, apical, and other morphologic subtypes-while simultaneously differentiating HCM from hypertensive heart disease, aortic stenosis, and other phenocopy disorders.
Detailed description
To overcome the twin bottlenecks of late detection and poor inter-centre reproducibility, the project leverages a large, multicentre historical cohort and anchors its pipeline on the 12-lead ECG-an inexpensive, ubiquitously available signal that can be captured in any department. Using deep-learning architectures augmented with attention mechanisms, we will develop (1) a discriminative model that separates HCM from phenocopies and normal hearts, and (2) an algorithmic framework that remains stable across devices and populations. Model governance will be embedded through version-controlled releases, cloud-edge deployment, and an offline replay evaluation loop, producing an end-to-end evidence chain that mirrors real-world clinical workflows.
Interventions
None listed
Sponsors
Second Affiliated Hospital, School of Medicine, Zhejiang University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Adults aged ≥ 18 years. 2. HCM cohort: Adults diagnosed with hypertrophic cardiomyopathy in accordance with the \*2023 Chinese Guidelines for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy in Adults\*. 3. HCM phenocopy cohort: Adults with an LV wall thickness ≥ 13 mm at any site on echocardiography. 4. Healthy-control cohort: Adults with no history of cardiac disease and no evidence of myocardial hypertrophy on echocardiography.
Exclusion criteria
Patients from whom analyzable ECG data cannot be obtained.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| model diagnostic performance | year 2 | Model performance was evaluated using calculated metrics including accuracy, sensitivity, specificity, and the area under the ROC curve (AUC). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| model diagnostic performance | year 2 | The accuracy rate of the model's phenotype-specific classification for patients with different patterns of myocardial hypertrophy |
| the model's generalizability | year 2 | The model's diagnostic performance on the external, multicentre validation cohort, including overall accuracy, sensitivity, specificity, and area under the ROC curve (AUC). |
Countries
China
Contacts
Primary ContactXiaojie Xie, MD, PhD
Outcome results
None listed