Volume 58, Issue 4 pp. 1084-1097
Research Article

An Integrated Algorithm for Differentiating Hypertrophic Cardiomyopathy From Hypertensive Heart Disease

Ling-cong Kong MD

Ling-cong Kong MD

Department of Cardiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China

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Lian-ming Wu MD

Lian-ming Wu MD

Department of Radiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China

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Zi Wang PhD

Zi Wang PhD

Department of Cardiology, Renji Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China

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Chang Liu MM

Chang Liu MM

Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, China

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Ben He MD, PhD

Corresponding Author

Ben He MD, PhD

Department of Cardiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Xuhui Distinct, Shanghai, China

Address reprint requests to: B.H., Department of Cardiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Xuhui Distinct, Shanghai 200030, China. E-mail: [email protected]

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First published: 23 January 2023

Abstract

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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