Trial Title:
A Deep Learning Method to Evaluate QT on Ribociclib
NCT ID:
NCT05623397
Condition:
Breast Cancer
Ribociclib
Conditions: Keywords:
Deep Learning
Ribociclib
QTc values
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Other
Intervention name:
Acquisition of a digitized ECG by four modalities within 20 minutes
Description:
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.
Arm group label:
Breast cancer patients administered ribociclib.
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.
Criteria for eligibility:
Study pop:
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.
Sampling method:
Non-Probability Sample
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
Gender:
Female
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Groupe Ambroise Paré, Hartmann
Address:
City:
Neuilly-sur-Seine
Zip:
92200
Country:
France
Status:
Recruiting
Contact:
Last name:
Jean Michel VANNETZEL, MD
Phone:
(+33)1 47 59 00 00
Email:
dr.vannetzel@gmail.com
Investigator:
Last name:
Jean Michel VANNETZEL, MD
Email:
Principal Investigator
Facility:
Name:
Hôpital Tenon
Address:
City:
Paris
Zip:
75020
Country:
France
Status:
Recruiting
Contact:
Last name:
Joseph GLIGOROV, MD, PhD
Phone:
(+33)1 56 01 60 24
Email:
joseph.gligorov@aphp.fr
Investigator:
Last name:
Joseph GLIGOROV
Email:
Principal Investigator
Facility:
Name:
CIC - Hôpitaux Universitaires Pitié Salpêtrière, Paris, FRANCE
Address:
City:
Paris
Zip:
75651
Country:
France
Status:
Recruiting
Contact:
Last name:
Joe Elie SALEM, MD, PhD
Phone:
+33 (0)1.42.17.85.32
Email:
joeelie.salem@gmail.com
Investigator:
Last name:
Joe Elie SALEM, MD, PhD
Email:
Principal Investigator
Facility:
Name:
Institut Gustave Roussy
Address:
City:
Villejuif
Zip:
94805
Country:
France
Status:
Recruiting
Contact:
Last name:
Alessandro VIANSONE, MD
Phone:
(+33)1 42 31 53 47
Email:
alessandro.viansone@gustaveroussy.fr
Investigator:
Last name:
Alessandro VIANSONE, MD
Email:
Principal Investigator
Start date:
July 28, 2023
Completion date:
September 28, 2026
Lead sponsor:
Agency:
CMC Ambroise Paré
Agency class:
Other
Source:
CMC Ambroise Paré
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05623397
