MR in hypertension

An Integrated Algorithm for Differentiating Hypertrophic Cardiomyopathy From Hypertensive Heart Disease

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.

Will advances in functional renal magnetic resonance imaging translate to the nephrology clinic?

Characterizing structural and tissue abnormalities of the kidney is fundamental to understanding kidney disease. Functional multi-parametric renal magnetic resonance imaging (MRI) is a noninvasive imaging strategy whereby several sequences are employed within a single session to quantify renal perfusion, tissue oxygenation, fibrosis, inflammation, and oedema without using ionizing radiation. In this review, we discuss evidence surrounding its use in several clinical settings including acute kidney injury, chronic kidney disease, hypertension, polycystic kidney disease and around renal transplantation. Kidney size on MRI is already a validated measure for making therapeutic decisions in the setting of polycystic kidney disease. Functional MRI sequences, T1 mapping and apparent diffusion coefficient, can non-invasively quantify interstitial fibrosis and so may have a near-future role in the nephrology clinic to stratify the risk of progressive chronic kidney disease or transplant dysfunction. Beyond this, multi-parametric MRI may be used diagnostically, for example differentiating inflammatory versus ischaemic causes of renal dysfunction, but this remains to be proven. Changes in MRI properties of kidney parenchyma may be useful surrogate markers to use as end points in clinical trials to assess if drugs prevent renal fibrosis or alter kidney perfusion. Large, multi-centre studies of functional renal MRI are ongoing which aim to provide definitive answers as to its role in the management of patients with renal dysfunction.