Recovery of left ventricular systolic function in peripartum cardiomyopathy: an observational study from rural Tanzania.

BACKGROUND: The aim of this study was to evaluate the recovery rate of the left ventricular systolic function of women diagnosed with peripartum cardiomyopathy receiving specialized care in rural Tanzania. METHODS: In this observational study, women diagnosed with peripartum cardiomyopathy at a referral center in rural Tanzania between December 2015 and September 2021 were included. Women diagnosed between February and September 2021 were followed prospectively, those diagnosed between December 2015 and January 2021 were tracked back for a follow-up echocardiography. All participants received a clinical examination, a comprehensive echocardiogram, and a prescription of guideline-directed medical therapy. The primary outcome was recovery of the left ventricular systolic function (left ventricular ejection fraction > 50%). RESULTS: Median age of the 110 participants was 28.5 years (range 17-45). At enrolment, 49 (45%) participants were already on cardiac medication, 50 (45%) had severe eccentric hypertrophy of the left ventricle, and the median left ventricular ejection fraction was 30% (range 15-46). After a median follow-up of 8.98 months (IQR 5.72-29.37), 61 (55%) participants were still on cardiac medication. Full recovery of the left ventricular systolic function was diagnosed in 76 (69%, 95% CI 59.6-77.6%) participants. In the multivariate analysis, a higher left ventricular ejection fraction at baseline was positively associated with full recovery (each 5% increase; OR 1.7, 95% CI 1.10-2.62, p = 0.012), while higher age was inversely associated (each 10 years increase; OR 0.40, 95% CI 0.19-0.82, p = 0.012). CONCLUSION: Left ventricular systolic function recovered completely in 69% of study participants with peripartum cardiomyopathy from rural Tanzania under specialized care.

Definition and management of arrhythmia-induced cardiomyopathy: findings from the European Heart Rhythm Association survey.

AIMS: Arrhythmia-induced cardiomyopathy (AiCM) represents a subtype of acute heart failure (HF) in the context of sustained arrhythmia. Clear definitions and management recommendations for AiCM are lacking. The European Heart Rhythm Association Scientific Initiatives Committee (EHRA SIC) conducted a survey to explore the current definitions and management of patients with AiCM among European and non-European electrophysiologists. METHODS AND RESULTS: A 25-item online questionnaire was developed and distributed among EP specialists on the EHRA SIC website and on social media between 4 September and 5 October 2023. Of the 206 respondents, 16% were female and 61% were between 30 and 49 years old. Most of the respondents were EP specialists (81%) working at university hospitals (47%). While most participants (67%) agreed that AiCM should be defined as a left ventricular ejection fraction (LVEF) impairment after new onset of an arrhythmia, only 35% identified a specific LVEF drop to diagnose AiCM with a wide range of values (5-20% LVEF drop). Most respondents considered all available therapies: catheter ablation (93%), electrical cardioversion (83%), antiarrhythmic drugs (76%), and adjuvant HF treatment (76%). A total of 83% of respondents indicated that adjuvant HF treatment should be started at first HF diagnosis prior to antiarrhythmic treatment, and 84% agreed it should be stopped within six months after LVEF normalization. Responses for the optimal time point for the first LVEF reassessment during follow-up varied markedly (1 day-6 months after antiarrhythmic treatment). CONCLUSION: This EHRA Survey reveals varying practices regarding AiCM among physicians, highlighting a lack of consensus and heterogenous care of these patients.

(Non)-Exertional Variables of Cardiopulmonary Exercise Testing in Heart Failure with and Without Cardiac Amyloidosis.

PURPOSE OF REVIEW: Cardiac amyloidosis (CA) constitutes an important etiology of heart failure with preserved ejection fraction (HFpEF) or heart failure with mildly reduced ejection fraction (HFmrEF). Since patients with CA show early exhaustion, we aimed to investigate whether non-exertional variables of cardiopulmonary exercise testing (CPET) provide additional information in comparison to traditional peak oxygen consumption (VO2peak). RECENT FINDINGS: We retrospectively investigated CPET variables of patients with HFpEF and HFmrEF with (n = 21) and without (n = 21, HF) CA at comparable age and ejection fraction. Exertional and non-exertional CPET variables as well as laboratory and echocardiographic markers were analyzed. The primary outcome was the difference in CPET variables between groups. The secondary outcome was rehospitalization in patients with CA during a follow-up of 24 months. Correlations between CPET, NTproBNP, and echocardiographic variables were calculated to detect patterns of discrimination between the groups. HF patients with CA were inferior to controls in most exertional and non-exertional CPET variables. Patients with CA were hospitalized more often (p = 0.002), and rehospitalization was associated with VE/VCO2 (p = 0.019), peak oxygen pulse (p = 0.042), the oxygen equivalent at the first ventilatory threshold (p = 0.003), circulatory (p = 0.024), and ventilatory power (p < .001), but not VO2peak (p = 0.127). Higher performance was correlated with lower E/e' and NTproBNP as well as higher resting heart rate and stroke volume in CA. Patients with CA displayed worse non-exertional CPET performance compared to non-CA HF patients, which was associated with rehospitalization. Differences between correlations of resting echocardiography and CPET variables between groups emphasize different properties of exercise physiology despite comparable ejection fraction.

Clinical implications of septic cardiomyopathy: A narrative review.

Sepsis is caused by the body's dysregulated response to infection, which can lead to multiorgan injury and death. Patients with sepsis may develop acute cardiac dysfunction, termed septic cardiomyopathy, which is a global but reversible dysfunction of both sides of the heart. This narrative review discusses the mechanistic changes in the heart during septic cardiomyopathy, its diagnosis, existing treatment options regarding severity and course, and emerging treatment approaches. Although no standardized definition for septic cardiomyopathy exists, it is described as a reversible myocardial dysfunction that typically resolves within 7 to 10 days. Septic cardiomyopathy is often diagnosed based on electrocardiography, cardiac magnetic resonance imaging, biomarkers, and direct invasive and noninvasive measures of cardiac output. Presently, the treatment of septic cardiomyopathy is similar to that of sepsis, primarily focusing on acute interventions. Treatments for cardiomyopathy often include angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and diuretics. However, because of profound hypotension in sepsis, many cardiomyopathy treatments are contraindicated in patients with septic cardiomyopathy. Substantial efforts have been made to study the pathophysiological mechanisms and diagnostic options; however, the lack of a uniform definition for septic cardiomyopathy is challenging for physicians when considering treatments. Another challenge for physicians is that the treatment for septic cardiomyopathy has only focused on acute intervention, whereas the treatment for other cardiomyopathies has been provided on a long-term basis. A better understanding of the underlying mechanisms of septic cardiomyopathy may contribute to the development of a unified definition of the condition and novel treatment options.

Presence and utility of electrocardiographic abnormalities in long-term childhood cancer survivors.

BACKGROUND: We assessed the prevalence and diagnostic value of ECG abnormalities for cardiomyopathy surveillance in childhood cancer survivors. METHODS: In this cross-sectional study, 1381 survivors (≥5 years) from the Dutch Childhood Cancer Survivor Study part 2 and 272 siblings underwent a long-term follow-up ECG and echocardiography. We compared ECG abnormality prevalences using the Minnesota Code between survivors and siblings, and within biplane left ventricular ejection fraction (LVEF) categories. Among 880 survivors who received anthracycline, mitoxantrone or heart radiotherapy, logistic regression models using least absolute shrinkage and selection operator identified ECG abnormalities associated with three abnormal LVEF categories (<52% in male/<54% in female, <50% and <45%). We assessed the overall contribution of these ECG abnormalities to clinical regression models predicting abnormal LVEF, assuming an absence of systolic dysfunction with a <1% threshold probability. RESULTS: 16% of survivors (52% female, mean age 34.7 years) and 14% of siblings had major ECG abnormalities. ECG abnormalities increased with decreasing LVEF. Integrating selected ECG data into the baseline model significantly improved prediction of sex-specific abnormal LVEF (c-statistic 0.66 vs 0.71), LVEF <50% (0.66 vs 0.76) and LVEF <45% (0.80 vs 0.86). While no survivor met the preset probability threshold in the first two models, the third model used five ECG variables to predict LVEF <45% and was applicable for ruling out (sensitivity 93%, specificity 56%, negative predictive value 99.6%). Calibration and internal validation tests performed well. CONCLUSION: A clinical prediction model with ECG data (left bundle branch block, left atrial enlargement, left heart axis, Cornell's criteria for left ventricular hypertrophy and heart rate) may aid in ruling out LVEF <45%.

Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.

BACKGROUND: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy. OBJECTIVE: This study aimed to assess the efficacy of an artificial intelligence-based heart failure detection model for peripartum cardiomyopathy detection. STUDY DESIGN: We first built a deep-learning model for heart failure detection using retrospective data at the University of Tennessee Health Science Center. Cases were adult and nonpregnant female patients with a heart failure diagnosis; controls were adult nonpregnant female patients without heart failure. The model was then tested on an independent cohort of pregnant women at the University of Tennessee Health Science Center with or without peripartum cardiomyopathy. We also tested the model in an external cohort of pregnant women at Atrium Health Wake Forest Baptist. Key outcomes were assessed using the area under the receiver operating characteristic curve. We also repeated our analysis using only lead I electrocardiogram as an input to assess the feasibility of remote monitoring via wearables that can capture single-lead electrocardiogram data. RESULTS: The University of Tennessee Health Science Center heart failure cohort comprised 346,339 electrocardiograms from 142,601 patients. In this cohort, 60% of participants were Black and 37% were White, with an average age (standard deviation) of 53 (19) years. The heart failure detection model achieved an area under the curve of 0.92 on the holdout set. We then tested the ability of the heart failure model to detect peripartum cardiomyopathy in an independent University of Tennessee Health Science Center cohort of pregnant women and an external Atrium Health Wake Forest Baptist cohort of pregnant women. The independent University of Tennessee Health Science Center cohort included 158 electrocardiograms from 115 patients; our deep-learning model achieved an area under the curve of 0.83 (0.77-0.89) for this data set. The external Atrium Health Wake Forest Baptist cohort involved 80 electrocardiograms from 43 patients; our deep-learning model achieved an area under the curve of 0.94 (0.91-0.98) for this data set. For identifying peripartum cardiomyopathy diagnosed ≥10 days after delivery, the model achieved an area under the curve of 0.88 (0.81-0.94) for the University of Tennessee Health Science Center cohort and of 0.96 (0.93-0.99) for the Atrium Health Wake Forest Baptist cohort. When we repeated our analysis by building a heart failure detection model using only lead-I electrocardiograms, we obtained similarly high detection accuracies, with areas under the curve of 0.73 and 0.93 for the University of Tennessee Health Science Center and Atrium Health Wake Forest Baptist cohorts, respectively. CONCLUSION: Artificial intelligence can accurately detect peripartum cardiomyopathy from electrocardiograms alone. A simple electrocardiographic artificial intelligence-based peripartum screening could result in a timelier diagnosis. Given that results with 1-lead electrocardiogram data were similar to those obtained using all 12 leads, future studies will focus on remote screening for peripartum cardiomyopathy using smartwatches that can capture single-lead electrocardiogram data.

[Application of computational fluid dynamics in the evaluation of left ventricular function in cardiomyopathies and coronary disease].

Computational fluid dynamics (CFD) is an emerging technology applied in the field of cardiovascular medicine, which can obtain hemodynamic data by simulating the blood flow in the patient's heart for cardiac function assessment and disease diagnosis. Left ventricular function plays a key role in the occurrence and development of cardiomyopathies and coronary disease. CFD can reconstruct the left ventricular anatomic structures of patients to clarify pathophysiologic mechanisms and analyze hemodynamic parameters to evaluate left ventricular function, verify surgical efficacy, and guide surgical strategy, which has a positive effect on achieving early diagnosis and reducing mortality from cardiomyopathies and coronary disease. At present, there are still technical limitations in the large-scale clinical application of CFD, and various solutions are being developed and tested, and further improvement and refinement are needed.

Paced QRS morphology mimicking complete left bundle branch block induced by right ventricular pacing is associated with pacing-induced cardiomyopathy.

INTRODUCTION: Right ventricular (RV) pacing sometimes causes left ventricular (LV) systolic dysfunction, also known as pacing-induced cardiomyopathy (PICM). However, the association between specifically paced QRS morphology and PICM development has not been elucidated. This study aimed to investigate the association between paced QRS mimicking a complete left bundle branch block (CLBBB) and PICM development. METHODS: We retrospectively screened 2009 patients who underwent pacemaker implantation from 2010 to 2020 in seven institutions. Patients who received pacemakers for an advanced atrioventricular block or bradycardia with atrial fibrillation, baseline LV ejection fraction (LVEF) ≥ 50%, and echocardiogram recorded at least 6 months postimplantation were included. The paced QRS recorded immediately after implantation was analyzed. A CLBBB-like paced QRS was defined as meeting the CLBBB criteria of the American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society in 2009. PICM was defined as a ≥10% LVEF decrease, resulting in an LVEF of <50%. RESULTS: Among the 270 patients analyzed, PICM was observed in 38. Baseline LVEF was lower in patients with PICM, and CLBBB-like paced QRS was frequently observed in PICM. Multivariate analysis revealed that low baseline LVEF (odds ratio [OR]: 0.93 per 1% increase, 95% confidence interval [CI]: 0.89-0.98, p = 0.006) and CLBBB-like paced QRS (OR: 2.69, 95% CI: 1.25-5.76, p = 0.011) were significantly associated with PICM development. CONCLUSION: CLBBB-like paced QRS may be a novel risk factor for PICM. RV pacing, which causes CLBBB-like QRS morphology, may need to be avoided, and patients with CLBBB-like paced QRS should be followed-up carefully.

Disease features and management of cardiomyopathies in women.

Over the last years, there has been a growing interest in the clinical manifestations and outcomes of cardiomyopathies in women. Peripartum cardiomyopathy is the only women-specific cardiomyopathy. In cardiomyopathies with X-linked transmission, women are not simply healthy carriers of the disorder, but can show a wide spectrum of clinical manifestations ranging from mild to severe manifestations because of heterogeneous patterns of X-chromosome inactivation. In mitochondrial disorders with a matrilinear transmission, cardiomyopathy is part of a systemic disorder affecting both men and women. Even some inherited cardiomyopathies with autosomal transmission display phenotypic and prognostic differences between men and women. Notably, female hormones seem to exert a protective role in hypertrophic cardiomyopathy (HCM) and variant transthyretin amyloidosis until the menopausal period. Women with cardiomyopathies holding high-risk features should be referred to a third-level center and evaluated on an individual basis. Cardiomyopathies can have a detrimental impact on pregnancy and childbirth because of the associated hemodynamic derangements. Genetic counselling and a tailored cardiological evaluation are essential to evaluate the likelihood of transmitting the disease to the children and the possibility of a prenatal or early post-natal diagnosis, as well as to estimate the risk associated with pregnancy and delivery, and the optimal management strategies.

Nonsense Variant PRDM16-Q187X Causes Impaired Myocardial Development and TGF-ß Signaling Resulting in Noncompaction Cardiomyopathy in Humans and Mice.

BACKGROUND: PRDM16 plays a role in myocardial development through TGF-ß (transforming growth factor-beta) signaling. Recent evidence suggests that loss of PRDM16 expression is associated with cardiomyopathy development in mice, although its role in human cardiomyopathy development is unclear. This study aims to determine the impact of PRDM16 loss-of-function variants on cardiomyopathy in humans. METHODS: Individuals with PRDM16 variants were identified and consented. Induced pluripotent stem cell-derived cardiomyocytes were generated from a proband hosting a Q187X nonsense variant as an in vitro model and underwent proliferative and transcriptional analyses. CRISPR (clustered regularly interspaced short palindromic repeats)-mediated knock-in mouse model hosting the Prdm16Q187X allele was generated and subjected to ECG, histological, and transcriptional analysis. RESULTS: We report 2 probands with loss-of-function PRDM16 variants and pediatric left ventricular noncompaction cardiomyopathy. One proband hosts a PRDM16-Q187X variant with left ventricular noncompaction cardiomyopathy and demonstrated infant-onset heart failure, which was selected for further study. Induced pluripotent stem cell-derived cardiomyocytes prepared from the PRDM16-Q187X proband demonstrated a statistically significant impairment in myocyte proliferation and increased apoptosis associated with transcriptional dysregulation of genes implicated in cardiac maturation, including TGF-ß-associated transcripts. Homozygous Prdm16Q187X/Q187X mice demonstrated an underdeveloped compact myocardium and were embryonically lethal. Heterozygous Prdm16Q187X/WT mice demonstrated significantly smaller ventricular dimensions, heightened fibrosis, and age-dependent loss of TGF-ß expression. Mechanistic studies were undertaken in H9c2 cardiomyoblasts to show that PRDM16 binds TGFB3 promoter and represses its transcription. CONCLUSIONS: Novel loss-of-function PRDM16 variant impairs myocardial development resulting in noncompaction cardiomyopathy in humans and mice associated with altered TGF-ß signaling.