5,000 Serial 12-Lead ECGs for QT-Interval Prolongation and Drug-Induced Arrhythmia Safety Monitoring

Overview

A pharmaceutical research organisation is compiling a reference dataset to train and benchmark automated QTc-interval measurement algorithms intended for use in ICH E14-compliant thorough QT studies and ongoing cardiac safety surveillance during drug development. We require serial 12-lead ECG recordings from adult patients or healthy volunteers collected under controlled, medically supervised conditions. Each subject should contribute at least three recordings at defined time points (pre-dose baseline, peak plasma concentration, and ≥4-hour post-dose or equivalent); paired time-point recordings are essential for QT correction modelling. Sampling rate must be ≥1000 Hz to support accurate automated beat detection and interval measurement; 500 Hz is the minimum acceptable threshold. Amplitude resolution must be ≥1 μV. Files must be provided in EDF or CSV format with explicit timestamp alignment between recordings from the same subject. Lead II and the precordial leads (V1–V6) are the primary measurement channels. For each recording we require: automated and over-read cardiologist QTc measurements (Bazett and Fridericia correction), individual beat-level RR intervals and QT intervals (minimum 10 beats averaged), morphology flags for T-wave alternans, U-wave presence, and bifid T-wave, and an overall interpretive statement. Keypoint annotations for P-wave onset, QRS onset, and T-wave offset (tangent method) on Lead II are mandatory for algorithm benchmarking. The annotation labeling protocol must comply with ISCE/ISHNE and ICH E14 guidance on ECG interval measurement in drug studies. Primary QT and QTc measurements must be performed by a trained ECG reader using a validated digital caliper tool; over-read must be performed by a board-certified cardiologist with clinical pharmacology or cardiology electrophysiology subspecialty. For each recording, at least 10 consecutive sinus beats must be individually measured and averaged; ectopic beats, paced beats, and beats following a pause must be excluded from the average. Inter-reader variability for QTc measurement must be ≤5 ms mean absolute difference across a randomly sampled 10% re-annotation subset; this metric must be reported in the dataset release documentation. De-identification must satisfy HIPAA Safe Harbour or equivalent GDPR pseudonymisation. Subject-level metadata must include age, sex, BMI, serum electrolyte values (potassium, magnesium, calcium) at time of recording, concomitant medication list at the drug class level, and heart rate at each time point. Any clinical-trial identifiers or site codes must be replaced with anonymised surrogate codes. Data originating from Phase I healthy-volunteer studies are particularly valuable as they represent a clean baseline population; data from patients with prolonged QTc at baseline (>450 ms men, >470 ms women) are equally important as high-sensitivity challenge cases. Data from patients currently receiving QT-prolonging agents (antiarrhythmics, antipsychotics, certain antibiotics) are especially valuable, as are recordings from subjects with known long-QT syndrome (congenital or acquired). Downstream use cases include regulatory-grade central ECG laboratory software for Phase I–III clinical trials, a precision-medicine tool stratifying drug candidates by proarrhythmic risk, and a QTc-monitoring dashboard embedded in hospital pharmacy systems to flag high-risk drug combinations in real time.

Progress

Data Specifications

Use Cases

• Training and validating Sensor / device data AI/ML models
• Benchmarking Sensor / device data detection and segmentation algorithms
• Building de-identified Sensor / device data research datasets for academic studies
• Augmenting existing Sensor / device data datasets to reduce class imbalance