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Published on: January 14, 2014
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.
Insights
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.
Background:
Differentiating hypertrophic cardiomyopathy (HCM) from hypertensive heart disease (HHD) is challenging.
Purpose:
To identify differences between HCM and HHD on a patient basis using MRI.
Study Type:
Retrospective.
Population:
A total of 219 subjects, 148 in phase I (baseline data and algorithm development: 75 HCM, 33 HHD, and 40 controls) and 71 in phase II (algorithm validation: 56 HCM and 15 HHD).
Field Strength/Sequence:
Contrast-enhanced inversion-prepared gradient echo and cine-balanced steady-state free precession sequences at 3.0 T.
Assessment:
MRI parameters assessed included left ventricular (LV) ejection fraction (LVEF), LV end systolic and end diastolic volumes (LVESV and LVEDV), mean maximum LV wall thickness (MLVWT), LV global longitudinal and circumferential strain (GRS, GLS, and GCS), and native T1. Parameters, which were significantly different between HCM and HHD in univariable analysis, were entered into a principal component analysis (PCA). The selected components were then introduced into a multivariable regression analysis to model an integrated algorithm (IntA) for screening the two disorders. IntA performance was assessed for patients with and without LGE in phase I (development) and phase II (validation).
Statistical Tests:
Univariable regression, PCA, receiver operating curve (ROC) analysis. A P value <0.05 was considered statistically significant.
Results:
Derived IntA formulation included LVEF, LVESV, LVEDV, MLVWT, and GCS. In LGE-positive subjects in phase l, the cutoff point of IntA ≥81 indicated HCM (83% sensitivity and 91% specificity), with the area under the ROC curve (AUC) of 0.900. In LGE-negative subjects, a higher possibility of HCM was indicated by a cutoff point of IntA ≥84 (100% sensitivity and 82% specificity), with an AUC of 0.947. Validation of IntA in phase II resulted in an AUC of 0.846 in LGE-negative subjects and 0.857 in LGE-positive subjects.
Data Conclusion:
A per-patient-based IntA algorithm for differentiating HCM and HHD was generated from MRI data and incorporated FT, LGE and morphologic parameters.
Evidence Level:
3.
Technical Efficacy:
Stage 2.
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