Toxicity Due to ChemotherapyMERMAIDNCT02934971
Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure
An observational study of Toxicity Due to Chemotherapy, run by RWTH Aachen University at 1 site in Germany.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 100 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Toxicity Due to Chemotherapy
- How many people
- 470 participants
- Where
- 1 siteGermany
- Study dates
- Jan 2017to Jan 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- Women only
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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)
Show all 9Show fewer
- 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.
Who cannot join
- 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
Show all 10Show fewer
- Patient has been committed to an institution by legal or regulatory order
- Participation in a parallel interventional clinical trial
- The subject received an investigational drug within 30 days prior to inclusion into this study
- Relevant renal insufficiency
Trial locations and contacts
1 site in 1 country.
Germany1 site
Department of Cardiology, RWTH Aachen University Hospital
Aachen, 52074
About this study
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.
Read the 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:
- 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:
- 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).
- 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
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in LVEF from baseline to one year, as determined by MRI as gold standard according to random study group allocation
Measured: one year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Oct 2016 (estimated)
- Study startJan 2017
- Primary completionJan 2019 (estimated)
- Study completionJan 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Toxicity Due to ChemotherapyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 470 participants (estimated)
Sponsor and investigators
- Lead sponsor
- RWTH Aachen University
- Sponsor type
- University, hospital or other
- Collaborators
- Technion, Israel Institute of Technology
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02934971
- Sponsor's protocol ID
- 16-079
- Acronym
- MERMAID
- Status last verified
- Oct 2016
- First submitted
- 16 Sep 2016
- Last updated
- 17 Oct 2016
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Oct 2016. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure
- ClinicalTrials.gov ID
- NCT02934971
- Acronym / sponsor ID
- MERMAID / 16-079
- Conditions
- Toxicity Due to Chemotherapy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 470 participants (estimated)
- Lead sponsor
- RWTH Aachen UniversityUniversity, hospital or other
- Collaborators
- Technion, Israel Institute of Technology
- Study start
- Jan 2017
- Primary completion
- Jan 2019 (estimated)
- Study completion
- Jan 2019 (estimated)
- First posted
- 17 Oct 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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:
- 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).
- 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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| 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). |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (9 listed)
- 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):
- 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.
Key exclusion criteria (10 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Change in LVEF from baseline to one year, as determined by MRI as gold standard according to random study group allocation
Time frame: one year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Oct 2016.
Related trials for Toxicity Due to Chemotherapy
Recruiting
Effect of Sarcopenia on the Occurrence of Toxicity Related to Anti-cancer Treatments
Sarcopenia, Oncology
RecruitingObservational study
Impact of Gonadotoxic Therapies on Fertility
Cancer, Fertility Issues
Active, not recruitingObservational study
Investigating Cardiovascular Adverse Events Related to Cancer Treatment
Toxicity Due to Chemotherapy, Cardiovascular Morbidity
Not yet recruitingPhase 1/2
Dose Optimization and Efficacy Assessment of a Fluoropyrimidine Antidote
Cancer, Toxicity Due to Chemotherapy
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Toxicity Due to Chemotherapy, run by RWTH Aachen University at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 17 Oct 2016. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Michael Becker, Prof. Dr. med (0049 (0)241 80 ext. 36083, mbecker@ukaachen.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in LVEF from baseline to one year, as determined by MRI as gold standard according to random study group allocation (time frame: one year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Oct 2016.