Skip to main content
OpenTrials
Completed

NCT Number: NCT05623397

A Deep Learning Method to Evaluate QT on Ribociclib

"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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Breast Cancer Breast Diseases Breast Neoplasms Neoplasms Neoplasms by Site Ribociclib Skin Diseases Skin and Connective Tissue Diseases

Age range

18 year and older

Sex eligibility

Female

Study type

Observational

Primary location

Groupe Ambroise Paré, Hartmann, Neuilly-sur-Seine, France

Loading trial locations.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Acquisition of a digitized ECG by four modalities within 20 minutes

Other

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.

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

    Time frame: One visit the day of ribociclib start (before ribociclib intake)

    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.

Secondary outcomes

  1. 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

    Time frame: One visit at day 14+/-3 and day 28+/-3 after start of ribociclib

    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).

  2. 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

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

    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®).

  3. The clinico-demographic predictors of amplitude of QTc prolongation on ribociclib.

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

    Nonlinear mixed models will be used to study clinico-demographic determinants associated with magnitude of QTc prolongation on ribociclib.

  4. Learn ECG features at baseline using deep-learning predictors of magnitude of QTc prolongation on ribociclib

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

    Using deep-learning seeking for a model using ECG raw data at baseline to predict magnitude of QTc prolongation on ribociclib

Sponsors and collaborators

Lead sponsor

CMC Ambroise Paré

Other

Registry information

Official study title

QT on RIBociclib measuRed by ArTificial INteliGence

Acronym: QT-RIBRATING

Important dates

Study start
2023
Primary completion
2025
Study completion
2025
First posted
Nov 21, 2022
Registry last updated
Jun 3, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.