Impact of Simulated Hyperventilation and Periodic Breathing on Sympatho-Vagal Balance and Hemodynamics in Patients with and without Heart Failure.

BACKGROUND: The effects of hyperventilation and hyperventilation in the context of periodic breathing (PB) on sympatho-vagal balance (SVB) and hemodynamics in conditions of decreased cardiac output and feedback resetting, such as heart failure (HF) or pulmonary arterial hypertension (PAH), are not completely understood. OBJECTIVES: To investigate the effects of voluntary hyperventilation and simulated PB on hemodynamics and SVB in healthy subjects, in patients with systolic HF and reduced or mid-range ejection fraction (HFrEF and HFmrEF) and in patients with PAH. METHODS: Study participants (n = 20 per group) underwent non-invasive recording of diastolic blood pressure, heart rate variability (HRV), baroreceptor-reflex sensitivity (BRS), total peripheral resistance index (TPRI) and cardiac index (CI). All measurements were performed at baseline, during voluntary hyperventilation and during simulated PB with different length of the hyperventilation phase. RESULTS: In healthy subjects, voluntary hyperventilation led to a 50% decrease in the mean BRS slope and a 29% increase in CI compared to baseline values (p < 0.01 and p < 0.05). Simulated PB did not alter TPRI or CI and showed heterogeneous effects on BRS, but analysis of dPBV revealed decreased sympathetic drive in healthy volunteers depending on PB cycle length (p < 0.05). In HF patients, hyperventilation did not affect BRS and TPRI but increased the CI by 10% (p < 0.05). In HF patients, simulated PB left all of these parameters unaffected. In PAH patients, voluntary hyperventilation led to a 15% decrease in the high-frequency component of HRV (p < 0.05) and a 5% increase in CI (p < 0.05). Simulated PB exerted neutral effects on both SVB and hemodynamic parameters. CONCLUSIONS: Voluntary hyperventilation was associated with sympathetic predominance and CI increase in healthy volunteers, but only with minor hemodynamic and SVB effects in patients with HF and PAH. Simulated PB had positive effects on SVB in healthy volunteers but neutral effects on SVB and hemodynamics in patients with HF or PAH.

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.

Non-invasive evaluation of the relationship between electrical and structural cardiac abnormalities in patients with myotonic dystrophy type 1.

BACKGROUND: Cardiac involvement in myotonic dystrophy type 1 (MD1) includes conduction disease, arrhythmias, and left-ventricular (LV) systolic dysfunction leading to an increased sudden cardiac death risk. An understanding of the interplay between electrical and structural myocardial changes could improve the prediction of adverse cardiac events. We aimed to explore the relationship between signs of cardiomyopathy by conventional and advanced cardiovascular magnetic resonance (CMR), and electrical abnormalities in MD1. METHODS: Fifty-seven MD1 patients (43 ± 13 years, 46% male) and 15 matched controls (41 ± 7 years, 53% male) underwent CMR including cine-imaging with feature-tracking strain analysis, late gadolinium enhancement (LGE), and native/post-contrast T1-mapping with extracellular volume calculation. Standard 12-lead and long-term ECG monitoring were performed as screening for rhythm and/or conduction abnormalities. RESULTS: Abnormal ECGs were recorded in 40% of MD1; a pathologic CMR was found in 44%: 21% had an impaired LV-EF and 32% showed non-ischemic LGE. When looking at MD1 patients with available long-term ECG monitoring (n = 39), those with atrial fibrillation (Afib)/flutter(Afl) episodes had lower LV-EF (52 ± 7 vs. 60 ± 5%, p = 0.002), lower global longitudinal strain (- 17 ± 3 vs. - 20 ± 3%, p = 0.034), a trend to lower left atrial emptying fraction (LA-EF) (44 ± 14 vs. 55 ± 8%, p = 0.08), and higher prevalence of LGE (88% vs. 23%, p = 0.001) with an intramural (75% vs. 23%, p = 0.01) and septal (63% vs. 13%, p = 0.009) pattern. In a model including LV-EF (OR 0.8, 95% CI 0.7-1.0, p = NS) and LGE presence (OR 14.8, 95% CI 1.4-159.0, p = 0.026), only LGE was independently associated with the occurrence of Afib/Afl episodes. CONCLUSION: Myocardial abnormalities depicted by non-ischemic LGE-CMR were the only independent predictor for the occurrence of Afib/Afl on ECG monitoring, previously shown to predict adverse cardiac events in MD1.

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<0.01) and diaphragm thickening ratio (DTR, 1.7 ± 0.5 vs. 2.8 ± 0.8, P<0.01) were significantly lower in PH patients versus controls. PH patients with worse exercise tolerance (6MWD <377 vs. ≥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.