Clinical trial NCT05623397: A Deep Learning Method to Evaluate QT on Ribociclib
Official title: QT on RIBociclib measuRed by ArTificial INteliGence
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Breast Cancer
Ribociclib
Keywords: Deep Learning
Ribociclib
QTc values
Lead sponsor: CMC Ambroise Paré
Sponsor class: OTHER
First posted: 2022-11-21
Enrollment: 70
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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

Primary outcomes:
measure: 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
timeFrame: One visit the day of ribociclib start (before ribociclib intake)
Secondary outcomes:
measure: 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
timeFrame: One visit at day 14+/-3 and day 28+/-3 after start of ribociclib
measure: 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
timeFrame: One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start
measure: The clinico-demographic predictors of amplitude of QTc prolongation on ribociclib.
timeFrame: One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start
measure: Learn ECG features at baseline using deep-learning predictors of magnitude of QTc prolongation on ribociclib
timeFrame: One visit at baseline before ribociclib start and then day 14+/-3 and day 28+/-3 after ribociclib start
Other outcomes:
Locations:
city: Neuilly-sur-Seine
country: France
facility: Groupe Ambroise Paré, Hartmann
city: Paris
country: France
facility: Hôpital Tenon
city: Paris
country: France
facility: CIC - Hôpitaux Universitaires Pitié Salpêtrière, Paris, FRANCE
city: Villejuif
country: France
facility: Institut Gustave Roussy
Study officials:
References: