Analysis of Clinical featuRes and Echocardiographic Characteristics for Diagnosis of Infiltrative cardiomyopaThy (ACREDIT): Retrospective Multi-center Observational Study
Trial Snapshot
- Phase
- Not Applicable
- Sponsor
- Yonsei University
- Enrollment
- 500
- Locations
- 1
- Primary Endpoint
- Area under the curve of the receiver operation characteristics
Study Overview
Brief Summary
This study sought to develop an algorithm by collecting echocardiographic image information and related clinical information capable of quantitatively evaluating changes of the myocardium through machine learning. Moreover, the researchers investigate the usefulness of an algorithm for early diagnosis and differential diagnosis of infiltrative cardiomyopathy.
Detailed Description
- Study Design: Multicenter Retrospective Observational Study
- Study method: If the above selection criteria are met, the index visit echocardiographic images which were performed immediately before or closest to the time of hospitalization for final diagnosis, echocardiographic images of the pre-visit and post-visit from the final diagnosis, and clinical information will be obtained. Chest X-ray, electrocardiogram, and echocardiography images are extracted in raw DICOM format and then analyzed in the core lab (Severance hospital). The characteristics of patients with infiltrative cardiomyopathy are identified through the collection of relevant clinical information, and a method for non-invasive early diagnosis and differential diagnosis of infiltrative cardiomyopathy is developed.
- Quantative analysis of echocardiographic images using Radiomics
- Radiomics is a method of extracting a large number of quantitative image features (300-500 features such as shape, entropy, volume, etc.) from non-invasive medical images (CT, MRI, etc.) and statistically analyzing the features. Its value has been demonstrated through the studies for prediction of breast cancer recurrence and lesion classification.
- Using the open source platform PyRadiomics19, we extract the radiomic characteristics for brightness (Energy, Entropy, Mean, Median, etc.) and texture (Gray Level Co-occurrence Matrix Contrast, Difference Variance, Maximal Correlation Coefficient, etc.) from the set region of interest.
- The differences between infiltrative cardiomyopathy and normal control are identified using clinical information and radiomics features extracted from echocardiography at the time of the diagnosis visit. The algorithms to distinguish the disease will be developed using machine learning methods such as support vector machine classifier.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Area under the curve of the receiver operation characteristics
Time Frame: until June 30, 2022
Sensitivity
Time Frame: until June 30, 2022
Sensitivity (True Positive Rate) refers to the proportion of those who have the infiltrative cardiomyopathy that received a positive result on the diagnostic algorythm by machine learning.
Specificity
Time Frame: until June 30, 2022
Specificity refers to the proportion of those who do not have the infiltrative cardiomyopathy that received a negative result on the test.
Secondary Outcomes
No secondary outcomes reported