Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure

NCT02934971 | Unknown

• 1 other identifier: 16-079
• Study Type: observational
• Target: 470
• Locations: 1 country
• Sites: 1

Brief Summary

The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding and prediction of arising heart failure in patients scheduled for cardio-toxic chemotherapy. This algorithmus will be developed by the technical cooperation partner at Technion, the institut for biomedical engineering in Haifa, Israel.

Conditions

Toxicity Due to Chemotherapy

Outcome Measures

Primary Outcomes (1)

• Change in LVEF from baseline to one year, as determined by MRI as gold standard according to random study group allocation

  one year

Study Arms (4)

Supervised cohort of 200 chemo-treated patients

cohort of 200 patients undergoing a chemo therapy accordingly to the inclusion criteria patients

Age matched control group of 200 normal subjects

200 age matched control group of subjects from the outpatient clinic who are not chemo-treated and who fit the inclusion and exclusion criteria

A:machine learning approach (N=35)

70 female patients undergoing cardiotoxic chemotherapy accordingly to the inclusion criteria will be randomized into two arms (group A and B).

B: conventional echocardiographic parameters (N=35)

70 female patients undergoing cardiotoxic chemotherapy accordingly to the inclusion criteria will be randomized into two arms (group A and B).

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The machine learning based algorithm will be trained on a supervised cohort of 200 chemo-treated patients and an age matched control group of 200 normal subjects will achieve the desired accuracy to detect subtle changes in LV function (stage 1 of the current study). The stage 2 part of the current study will be performed in patients undergoing cardiotoxic chemotherapy (N=70), randomized for comparing a surveillance strategy using machine learning approach (group A, N=35) from conventional surveillance based on conventional echocardiographic parameter as LVEF (group B, N=35). Patients coming to the echo lab for echo surveillance of LV function will be randomized to optimized automated algorithm or receive standard LVEF alone.

You may qualify if:

• Patients Patients scheduled for chemotherapy at increased risk of cardiotoxicity (regarding 200 Chemo patients in stage 1 study and 70 Chemo patients in stage 2 study):
• use of anthracycline with
• trastuzumab (Herceptin) in breast-cancer with the HER2 mutation OR
• tyrosine kinase inhibitors (eg sunitinib) OR
• cumulative anthracycline dose \>450g/m2 of doxorubicin, or equivalent other anthracycline cumulative dose (eg for epirubicine \>900g/m2) OR
• increased risk of heart failure (HF) (age \>65y, type 2 diabetes mellitus, hypertension, previous cardiac injury eg. myocardial infarction)
• Female aged \> 18 years
• Written informed consent prior to study participation
• The subject is willing and able to follow the procedures outlined in the protocol The department of gynecology at the RWTH University hospital will inform the principal investigator about these patients.

You may not qualify if:

• Valvular stenosis or regurgitation of \>moderate severity
• History of previous heart failure (baseline New York Heart Association - NYHA \>2)
• Inability to acquire interpretable images (identified from baseline echo)
• Contraindication to perform a MRI
• Oncologic (or other) life expectancy \<12 months
• Pregnant and lactating females
• Patient has been committed to an institution by legal or regulatory order
• Participation in a parallel interventional clinical trial
• Relevant renal insufficiency

Sponsors & Collaborators

• RWTH Aachen University: lead
• Technion, Israel Institute of Technology: collaborator

Study Sites (1)

Department of Cardiology, RWTH Aachen University Hospital

Aachen, 52074, Germany

Location

Central Study Contacts

Michael Becker, Prof. Dr. med

CONTACT

0049 (0)241 80; mbecker@ukaachen.de

Study Design

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

Study Record Dates

• First Submitted: September 16, 2016
• First Posted: October 17, 2016
• Study Start: January 1, 2017
• Primary Completion: January 1, 2019
• Study Completion: January 1, 2019
• Last Updated: October 17, 2016

Record last verified: 2016-10

Locations

DE (1)