Diaphragm function does not independently predict exercise intolerance in patients with precapillary pulmonary hypertension after adjustment for right ventricular function

Background: Several determinants of exercise intolerance in patients with precapillary pulmonary hypertension (PH) due to pulmonary arterial hypertension and /or chronic thromboembolic PH (CTEPH) have been suggested, including diaphragm dysfunction. However, these have rarely been evaluated in a multimodal manner. Methods: Forty-three patients with PH (age 58 +/- 17 years, 30% male) and 43 age- and gender-matched controls (age 54 +/- 13 years, 30% male) underwent diaphragm function (excursion and thickening) assessment by ultrasound, standard spirometry, arterial blood gas analysis, echocardiographic assessment of pulmonary artery pressure (PAP), assay of amino-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and cardiac magnetic resonance (CMR) imaging to evaluate right ventricular systolic ejection fraction (RVEF). Exercise capacity was determined using the 6-min walk distance (6MWD). Results: Excursion velocity during a sniff maneuver (SniffV, 4.5 +/- 1.7 vs. 6.8 +/- 2.3 cm /s, P= 377 m) were characterized by worse SniffV, worse DTR, and higher NT-pro-BNP levels as well as by lower arterial carbon dioxide levels and RVEF, which were all univariate predictors of exercise limitation. On multivariate analysis, the only independent predictors of exercise limitation were RVEF (r = 0.47, P= 0.001) and NT-proBNP (r = -0.27, P= 0.047). Conclusion: Patients with PH showed diaphragm dysfunction, especially as exercise intolerance progressed. However, diaphragm dysfunction does not independently contribute to exercise intolerance, beyond what can be explained from right heart failure

Cardiac Outcomes in Adults With Mitochondrial Diseases

International audienceBackground: Patients with mitochondrial diseases are at risk of heart failure (HF) and arrhythmic major adverse cardiac events (MACE). Objectives: We developed prediction models to estimate the risk of HF and arrhythmic MACE in this population. Methods: We determined the incidence and searched for predictors of HF and arrhythmic MACE using Cox regression in 600 adult patients from a multicenter registry with genetically confirmed mitochondrial diseases. Results: Over a median follow-up time of 6.67 years, 29 patients (4.9%) reached the HF endpoint, including 19 hospitalizations for nonterminal HF, 2 cardiac transplantations, and 8 deaths from HF. Thirty others (5.1%) reached the arrhythmic MACE, including 21 with third-degree or type II second-degree atrioventricular blocks, 4 with sinus node dysfunction, and 5 sudden cardiac deaths. Predictors of HF were the m.3243A>G variant (HR: 4.3; 95% CI: 1.8-10.1), conduction defects (HR: 3.0; 95% CI: 1.3-6.9), left ventricular (LV) hypertrophy (HR: 2.6; 95% CI: 1.1-5.8), LV ejection fraction <50% (HR: 10.2; 95% CI: 4.6-22.3), and premature ventricular beats (HR: 4.1; 95% CI: 1.7-9.9). Independent predictors for arrhythmia were single, large-scale mtDNA deletions (HR: 4.3; 95% CI: 1.7-10.4), conduction defects (HR: 6.8; 95% CI: 3.0-15.4), and LV ejection fraction <50% (HR: 2.7; 95% CI: 1.1-7.1). C-indexes of the Cox regression models were 0.91 (95% CI: 0.88-0.95) and 0.80 (95% CI: 0.70-0.90) for the HF and arrhythmic MACE, respectively. Conclusions: We developed the first prediction models for HF and arrhythmic MACE in patients with mitochondrial diseases using genetic variant type and simple cardiac assessments

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry

: Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024