MedTrace Logo

A Deep Learning Method to Evaluate QT on Ribociclib

NCT05623397 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 70

Last updated 2026-05-26

No results posted yet for this study

Summary

"Deep-learning" is a fast-growing method of machine learning (artificial intelligence, AI) which is arousing the interest of the scientific committee in many medical fields. These methods make it possible to generate matches between raw inputs (such as the digital signal from the ECG) and the desired outputs (for example, the measurement of QTc). Unlike traditional machine learning methods, which require manual extraction of structured and predefined data from raw input, deep-learning methods learn these functionalities directly from raw data, without pre-defined guidelines. With the advent of big-data and the recent exponential increase in computing power, these methods can produce models with exceptional performance. The investigators recently used this type of method using multi-layered artificial neural networks, to create an application based on a model that directly transforms the raw digital data of ECGs (.xml) into a measure of QTc comparable to those respecting the highest standards concerning reproducibility.

The main purpose of this trial is to study the performance of our DL-AI model for QTc measurement (vs. best standards of QTc measurements, TCM) applied to the recommended ECG monitoring following ribociclib prescription for breast cancer patients in routine clinical care. The investigators will acquire ECG with diverse devices including simplified devices (one/three lead acquisition, low frequency sampling rate: 125-500 Htz) to determine if they'll be equally performant versus 12-lead acquisition machine to evaluate QTc in this setting.

Conditions

Interventions

OTHER

Acquisition of a digitized ECG by four modalities within 20 minutes

Patients will have three visits during the cycle for a given dose (600mg/day, 400mg/day or 200mg/day): Baseline , Day 14, Day 28 At each visit, the patient will have the acquisition of a digitized ECG by four modalities within 20 minutes (A 10 second triplicate ECG with WELCH-ALYN ELI-280® with the three 10 sec ECGs collected at approximatively 2-minute intervals, 3 min holter acquisition with a CGM HI-patch ®, a 3 minutes acquisition with AliveCore 6L® device and 10 seconds triplicate acquisition with QT-medical ® device collected at approximatively 2-minute intervals ). Concomitantly with the ECG acquisition, patients will have blood sampling for measurements of variables clinically important for assessment of QTc including potassium, fasting blood glucose, calcemia, magnesium, estradiol, progesterone, FSH, LH, D4-androstenedione, total and free testosterone, SHBG and TSH. Blood concentration of ribociclib will be also assessed.

Sponsors & Collaborators

  • Groupe Hospitalier Pitie-Salpetriere

    collaborator OTHER

  • CMC Ambroise Paré

    lead OTHER

Eligibility

Min Age
18 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-07-28
Primary Completion
2025-10-08
Completion
2025-10-08

Countries

  • France

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05623397 on ClinicalTrials.gov