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An Integrated Algorithm for Differentiating Hypertrophic Cardiomyopathy From Hypertensive Heart Disease
Ling-Cong Kong1, Lian-Ming Wu2, Zi Wang1
1Department of Cardiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China.
Journal of Magnetic Resonance Imaging : JMRI
|January 23, 2023
Summary
This study developed an MRI-based algorithm (IntA) to differentiate hypertrophic cardiomyopathy (HCM) from hypertensive heart disease (HHD). The integrated algorithm demonstrated high accuracy in distinguishing between these challenging cardiac conditions.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Differentiating hypertrophic cardiomyopathy (HCM) from hypertensive heart disease (HHD) presents a significant clinical challenge.
- Accurate differentiation is crucial for appropriate patient management and treatment strategies.
Purpose of the Study:
- To develop and validate a novel magnetic resonance imaging (MRI) based algorithm for differentiating HCM and HHD on a per-patient basis.
- To identify key MRI parameters that distinguish between these two cardiac conditions.
Main Methods:
- A retrospective study involving 219 subjects (148 in Phase I, 71 in Phase II) with diagnosed HCM, HHD, or controls.
- Utilized 3.0T MRI with contrast-enhanced inversion-prepared gradient echo and cine-balanced steady-state free precession sequences.
- Developed an integrated algorithm (IntA) using principal component analysis and multivariable regression on parameters including LVEF, LV volumes, wall thickness, and strain.
Main Results:
- The derived IntA algorithm incorporated LVEF, LVESV, LVEDV, MLVWT, and GCS.
- In LGE-positive subjects (Phase I), IntA achieved 83% sensitivity and 91% specificity (AUC 0.900).
- In LGE-negative subjects (Phase I), IntA showed 100% sensitivity and 82% specificity (AUC 0.947). Phase II validation demonstrated AUCs of 0.846 and 0.857.
Conclusions:
- An integrated MRI-based algorithm (IntA) effectively differentiates between HCM and HHD using a combination of functional, morphological, and late gadolinium enhancement (LGE) parameters.
- The developed algorithm shows promising performance for clinical application in distinguishing these cardiovascular diseases.
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