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

QT on RIBociclib measuRed by ArTificial INteliGence

"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.

Study Overview

• Status: Completed
• Conditions: Breast Cancer; Ribociclib
• Intervention / Treatment: Other: Acquisition of a digitized ECG by four modalities within 20 minutes

• Study Type: Observational
• Enrollment (Actual): 70

Contacts and Locations

Study Locations

• France
  • Neuilly-sur-Seine, France, 92200
    • Groupe Ambroise Paré, Hartmann
  • Paris, France, 75020
    • Hôpital Tenon
  • Paris, France, 75651
    • CIC - Hôpitaux Universitaires Pitié Salpêtrière, Paris, FRANCE
  • Villejuif, France, 94805
    • Institut Gustave Roussy

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Breast cancer patients requiring ribociclib for their standard of care at the clinically indicated dose, as per treating physician. Association with other hormone-derived therapeutics will be allowed.

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Breast cancer patients administered ribociclib.; 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.

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Compare the values of QTc generated by method 1 (overlap method on triplicate of 10 seconds ECG concatenated, TCM; the method of reference) versus method 2 relying on AI methodology in patients' candidate for ribociclib start	Comparison of the 2 methods (TCM vs. DL-AI) to demonstrate if there is a clinically relevant mean QTc difference ≥ 5msec between the 2 methods.	One visit the day of ribociclib start (before ribociclib intake)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Compare the values of QTc generated by method 1 (overlap method after triplicate concatenation, TCM) versus method 2 (DL-AI) in patients' on/off ribociclib using a digitized 12-lead acquisition ECG device	Bland-Altman plots and intra-class correlation will be generated to compare QTc values obtained by TCM vs. DL-AI on ribociclib (Day 14+/-3 days after start) and off-ribociclib (Day 28 +/-3 of ribociclib cycles).	One visit at day 14+/-3 and day 28+/-3 after start of ribociclib
Compare the values of QTc generated using method 2 (DL-AI) in patients' on/off ribociclib using a miniaturized and/or simplified ECG acquisition device (QT-Medical®, AliveCor®, a holter system (CGM HI-patch) versus using a digitized 12-lead acquisition	Compare QTc values obtained by DL-AI on/off ribociclib using a standard digitized 12-lead acquisition device (WELCH-ALYN ELI-280) versus each of three other miniaturized and/or simplified ECG acquisition devices (QT- Medical®, AliveCor®, CGM HI-patch®).	One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start
The clinico-demographic predictors of amplitude of QTc prolongation on ribociclib.	Nonlinear mixed models will be used to study clinico-demographic determinants associated with magnitude of QTc prolongation on ribociclib.	One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start
Learn ECG features at baseline using deep-learning predictors of magnitude of QTc prolongation on ribociclib	Using deep-learning seeking for a model using ECG raw data at baseline to predict magnitude of QTc prolongation on ribociclib	One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start

Collaborators and Investigators

• Sponsor: CMC Ambroise Paré
• Collaborators: Groupe Hospitalier Pitie-Salpetriere
• Investigators: Principal Investigator: Joe Elie SALEM, MD.PhD, Groupe Hospitalier Pitie-Salpetriere; Principal Investigator: Jean Michel VANNETZEL, MD, Groupe Ambroise Paré, Hartmann; Principal Investigator: Alessandro VIANSONE, MD, Gustave Roussy, Cancer Campus, Grand Paris; Principal Investigator: Joseph GLIGOROV, MD, PhD, Hôpital Tenon

Study record dates

Study Major Dates

• Study Start (Actual): July 28, 2023
• Primary Completion (Actual): October 8, 2025
• Study Completion (Actual): October 8, 2025

Study Registration Dates

• First Submitted: September 1, 2022
• First Submitted That Met QC Criteria: November 18, 2022
• First Posted (Actual): November 21, 2022

Study Record Updates

• Last Update Posted (Actual): June 3, 2026
• Last Update Submitted That Met QC Criteria: June 1, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Keywords: Ribociclib; Deep Learning; QTc values
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Skin and Connective Tissue Diseases; Breast Neoplasms
• Other Study ID Numbers: 2021/03

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No