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

NCT ID: NCT02934971

Last Updated: 2016-10-17

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 470 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2017-01-31
• Study Completion Date: 2019-01-31

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.

Detailed Description

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 Prof. Adam who leads the Technion, the institut for biomedical engineering.

Specific aims:

1. To collect all achievable data from patients scheduled for cardiotoxic chemotherapy at baseline, up to 6 months after ending therapy - regarding imaging (MRI, echocardiography with conventional and strain parameter), electrocardiography, biomedical markers (to define the function of liver, kidney, heart and hematopoietic bone marrow), clinical parameter and quality of life questionnaire:

2. To optimize and evaluate a robust machine learning approach that integrate and assess all these data to detect early myocardial damage and to identify an optimal parameter (single or in combination) for prediction of subclinical left ventricular (LV) dysfunction (stage 1 of the current study).

3. To perform a clinical study (stage 2 of the current study) of chemotherapy patients, and to identify subclinical LV dysfunction, which will be used to guide cardioprotective therapy using the new machine learning approach in comparison to the actual standard procedure using only echocardiographic left ventricular ejection fraction (LVEF).

The purpose of this study is to evaluate and optimize a machine learning approach to combine and integrate data from different imaging modalities with laboratory, electrocardiography and questionnaire information to define the value of all these parameter in patient management, by identification of subclinical LV dysfunction, which will be used to guide cardioprotective therapy in comparison to a standard approach using only conventional echocardiographic parameters.

MRI, conventional echocardiographic parameters and echocardiographic myocardial deformation imaging are employing different modalities and approaches to obtain insight into myocardial tissue and deformation. We hypothesize that a new and optimized automated algorithm using these modalities and integrating laboratory, electrocardiography and questionnaire information will improve the detection of early LV dysfunctions, and will bring new insight to the potential response of chemo patients to cardiotoxic therapy. We expect that this algorithm leads to the use of adjunctive therapy that will limit the development of LV dysfunction, interruptions of chemotherapy and development of heart failure in follow-up and thus will reduce morbidity and costs.

Conditions

Toxicity Due to Chemotherapy

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Supervised cohort of 200 chemo-treated patients

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

No interventions assigned to this group

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

No interventions assigned to this group

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).

No interventions assigned to this group

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).

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

1. 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)

2. Female aged > 18 years

3. Written informed consent prior to study participation

4. 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.

Exclusion Criteria

1. Valvular stenosis or regurgitation of >moderate severity

2. History of previous heart failure (baseline New York Heart Association - NYHA >2)

3. Inability to acquire interpretable images (identified from baseline echo)

4. Contraindication to perform a MRI

5. Oncologic (or other) life expectancy <12 months

6. Pregnant and lactating females

7. Patient has been committed to an institution by legal or regulatory order

8. Participation in a parallel interventional clinical trial

9. The subject received an investigational drug within 30 days prior to inclusion into this study

10. Relevant renal insufficiency

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Technion, Israel Institute of Technology

OTHER

Sponsor Role: collaborator

RWTH Aachen University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Department of Cardiology, RWTH Aachen University Hospital

Aachen, , Germany

Countries

Germany

Central Contacts

Michael Becker, Prof. Dr. med

Role: CONTACT

mbecker@ukaachen.de

0049 (0)241 80 ext. 36083

Other Identifiers

16-079

Identifier Type: -

Identifier Source: org_study_id