NCT05623397

Brief 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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
70

participants targeted

Target at P25-P50 for all trials

Timeline
Completed

Started Jul 2023

Typical duration for all trials

Geographic Reach
1 country

4 active sites

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

September 1, 2022

Completed
3 months until next milestone

First Posted

Study publicly available on registry

November 21, 2022

Completed
8 months until next milestone

Study Start

First participant enrolled

July 28, 2023

Completed
2.2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

October 8, 2025

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

October 8, 2025

Completed
Last Updated

December 4, 2025

Status Verified

November 1, 2025

Enrollment Period

2.2 years

First QC Date

September 1, 2022

Last Update Submit

November 27, 2025

Conditions

Keywords

Deep LearningRibociclibQTc values

Outcome Measures

Primary Outcomes (1)

  • 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 Outcomes (4)

  • 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

    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

    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.

    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

    One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start

Study Arms (1)

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

Interventions

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.

Breast cancer patients administered ribociclib.

Eligibility Criteria

Age18 Years+
Sexfemale
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-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.

You may qualify if:

  • 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

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (4)

CIC - Hôpitaux Universitaires Pitié Salpêtrière, Paris, FRANCE

Paris, PARIS, 75651, France

Location

Groupe Ambroise Paré, Hartmann

Neuilly-sur-Seine, 92200, France

Location

Hôpital Tenon

Paris, 75020, France

Location

Institut Gustave Roussy

Villejuif, 94805, France

Location

Biospecimen

Retention: SAMPLES WITH DNA

A plasma biobanking and a DNA biobanking will be constitued at each visit (30ml of blood), drawn as a complementary volume to sampling performed in the standard of care

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

September 1, 2022

First Posted

November 21, 2022

Study Start

July 28, 2023

Primary Completion

October 8, 2025

Study Completion

October 8, 2025

Last Updated

December 4, 2025

Record last verified: 2025-11

Locations