A Deep Learning Method to Evaluate QT on Ribociclib (QT-RIBRATING)

CMC Ambroise Paré

Status: Enrolling

Conditions: Breast Cancer; Ribociclib

Treatments: Other: Acquisition of a digitized ECG by four modalities within 20 minutes

Study type: Observational

Funder types: Other

Identifiers

NCT05623397

2021/03

Details and patient eligibility

About

"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.

Enrollment: 70 estimated patients

Sex: Female

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Adult female patients requiring start of ribociclib based therapy for a breast cancer in their standard of care, as per their summary of product characteristic's indications
• Association with hormone-based therapy in combination is authorized (aromatase inhibitors or fulvestrant)
• Able to provide an informed consent

Exclusion criteria

• Any allergy or contra-indication to ribociclib as mentioned in their as summary of product characteristic's
• Patients presenting a condition precluding accurate QTc measurements on electrocardiogram, i.e paced ventricular rhythm, multiples premature ventricular or supra-ventricular contractions, ventricular tachycardia, supraventricular arrhythmia (including atrial fibrillation, flutter or junctional rhythm)
• Patients with an atrial pacing and sinus dysfunction
• Patients presenting a contra-indication for ECG measurement, or with a device rendering ECG measurements impossible (i.e. Diaphragmatic pacing)
• Patients presenting a contra-indication to ribociclib start; including association with prohibited drug potentializing the risk of TdP

Trial design

70 participants in 1 patient group

Breast cancer patients administered ribociclib.

Description:

Prospective cohort of consecutive breast cancer patients requiring ribociclib for their standard of care at the clinically indicated dose, as per treating physician prescription (600mg to 200mg/day for 21 days per 28 days cycle). Association with other hormone-derived therapeutics will be allowed.

Treatment:

Other: Acquisition of a digitized ECG by four modalities within 20 minutes