Breast Cancer, Ribociclib
Conditions
Keywords
Deep Learning, Ribociclib, QTc values
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.
Interventions
OTHERAcquisition 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.
Sponsors
CMC Ambroise Paré
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 | 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
| Measure | Time frame | Description |
|---|---|---|
| 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 | 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). |
| 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 | 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®). |
| 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 | Nonlinear mixed models will be used to study clinico-demographic determinants associated with magnitude of QTc prolongation on ribociclib. |
| 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 | Using deep-learning seeking for a model using ECG raw data at baseline to predict magnitude of QTc prolongation on ribociclib |
Countries
France
Contacts
PRINCIPAL_INVESTIGATORJoe Elie SALEM, MD.PhD
Groupe Hospitalier Pitie-Salpetriere
PRINCIPAL_INVESTIGATORJean Michel VANNETZEL, MD
Groupe Ambroise Paré, Hartmann
PRINCIPAL_INVESTIGATORAlessandro VIANSONE, MD
Gustave Roussy, Cancer Campus, Grand Paris
PRINCIPAL_INVESTIGATORJoseph GLIGOROV, MD, PhD
Hôpital Tenon
Outcome results
None listed