Unsupervised mRNA-seq classification of heart transplant endomyocardial biopsies.

BACKGROUND: Endomyocardial biopsy (EMB) is currently considered the gold standard for diagnosing cardiac allograft rejection. However, significant limitations related to histological interpretation variability are well-recognized. We sought to develop a methodology to evaluate EMB solely based on gene expression, without relying on histology interpretation. METHODS: Sixty-four EMBs were obtained from 47 post-heart transplant recipients, who were evaluated for allograft rejection. EMBs were subjected to mRNA sequencing, in which an unsupervised classification algorithm was used to identify the molecular signatures that best classified the EMBs. Cytokine and natriuretic peptide peripheral blood profiling was also performed. Subsequently, we performed gene network analysis to identify the gene modules and gene ontology to understand their biological relevance. We correlated our findings with the unsupervised and histological classifications. RESULTS: Our algorithm classifies EMBs into three categories based solely on clusters of gene expression: unsupervised classes 1, 2, and 3. Unsupervised and histological classifications were closely related, with stronger gene module-phenotype correlations for the unsupervised classes. Gene ontology enrichment analysis revealed processes impacting on the regulation of cardiac and mitochondrial function, immune response, and tissue injury response. Significant levels of cytokines and natriuretic peptides were detected following the unsupervised classification. CONCLUSION: We have developed an unsupervised algorithm that classifies EMBs into three distinct categories, without relying on histology interpretation. These categories were highly correlated with mitochondrial, immune, and tissue injury response. Significant cytokine and natriuretic peptide levels were detected within the unsupervised classification. If further validated, the unsupervised classification could offer a more objective EMB evaluation.

Tissue Doppler derived biphasic velocities during the pre and post-ejection phases: patterns, concordance and hemodynamic significance in health and disease.

BACKGROUND: Pre-(PRE) and post-ejection (POE) velocities by mitral annular tissue Doppler (TD) are biphasic and may be related to myocardial deformations. We investigated the predominance and concordance of TD-PRE and POE velocities and their effect on myocardial functions in controls and in heart failure (HF) patients. METHODS: Retrospectively, 84 HF patients [57.6 years, 28(33%) females, NYHA: 2.3 ± 0.6, EF: 55 ± 15%, 52(62%) preserved EF, and 32(38%) reduced EF], 42 normal young controls, and 26 asymptomatic age matched controls were included. Echocardiography was done and from mitral annular tissue Doppler recordings, the biphasic PRE and POE velocity signals were identified and compared between groups. RESULTS: While controls had almost always predominantly positive PRE and negative POE, HF had more negative PRE and positive POE. Moreover, almost all controls exhibited normal concordance (positive PRE and negative POE). HF exhibited more abnormal concordance which was significantly associated with worse NYHA, and parameters of diastolic and systolic functions. Opposite PRE and POE velocities correlated significantly in all groups (PREp vs POEn: young:r = 0.52, p < 0.001, age controls:r = 0.79, p < 0.001, HFpEF: r = 0.56, p < 0.001, HFrEF: r = 0.42, p = 0.018; PREn vs POEp: young: r = 0.25,p = 0.1, age controls: r = 0.42, p = 0.04, HFpEF: r = 0.43, p = 0.004, HFrEF: r = 0.61, p < 0.001) and the ratios PRE-P/N and POE-N/P correlated significantly with E/e' in HF only. CONCLUSIONS: In physiological state, TD signals are predominantly positive during PRE and negative during POE. Opposite PRE and POE velocities corelate, representing the PRE-generation and POE-reversal of shortening-stretch relationships, the attenuation of which in HF may be related to elevated LV filling pressures. In HF, partially or completely reversed concordance of PRE and POE is associated with progressive worsening of clinical and hemodynamic profiles.

The Significance of Parametric Mapping in Advanced Cardiac Imaging.

Cardiac magnetic resonance imaging has witnessed a transformative shift with the integration of parametric mapping techniques, such as T1 and T2 mapping and extracellular volume fraction. These techniques play a crucial role in advancing our understanding of cardiac function and structure, providing unique insights into myocardial tissue properties. Native T1 mapping is particularly valuable, correlating with histopathological fibrosis and serving as a marker for various cardiac pathologies. Extracellular volume fraction, an early indicator of myocardial remodeling, predicts adverse outcomes in heart failure. Elevated T2 relaxation time in cardiac MRI indicates myocardial edema, enabling noninvasive and early detection in conditions like myocarditis. These techniques offer precise insights into myocardial properties, enhancing the accuracy of diagnosis and prognosis across a spectrum of cardiac conditions, including myocardial infarction, autoimmune diseases, myocarditis, and sarcoidosis. Emphasizing the significance of these techniques in myocardial tissue analysis, the review provides a comprehensive overview of their applications and contributions to our understanding of cardiac diseases.

Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening.

Aims: Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods and results: We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells' Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50-0.58, specificity 0.00-0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66-0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77-0.84) subgroups. Conclusion: Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.

Noninvasive biomarkers for prediction and diagnosis of heart transplantation rejection.

For most patients with end-stage heart failure, heart transplantation is the treatment of choice. Allograft rejection is one of the major post-transplantation complications affecting graft outcome and survival. Recent advancements in science and technology offer an opportunity to integrate genomic and other omics-based biomarkers into clinical practice, facilitating noninvasive evaluation of allograft for diagnostic and prognostic purposes. Omics, including gene expression profiling (GEP) of blood immune cell components and donor-derived cell-free DNA (dd-cfDNA) are of special interest to researchers. Several studies have investigated levels of dd-cfDNA and miroRNAs in blood as potential markers for early detection of allograft rejection. One of the achievements in the field of transcriptomics is AlloMap, GEP of peripheral blood mononuclear cells (PBMC), which can identify 11 differentially expressed genes and help with detection of moderate and severe acute cellular rejection in stable heart transplant recipients. In recent years, the utilization of GEP of PBMC for identifying differentially expressed genes to diagnose acute antibody-mediated rejection and cardiac allograft vasculopathy has yielded promising results. Advancements in the field of metabolomics and proteomics as well as their potential implications have been further discussed in this paper.

Combined atrioventricular longitudinal strain rate during isovolumic contraction predicts pulmonary capillary wedge pressure in patients with systolic dysfunction.

BACKGROUND: Reportedly, mitral annular velocities derived by tissue Doppler imaging (TDI)-during isovolumic contraction (IVV) can predict pulmonary capillary wedge pressure (PCWP) in heart failure patients with depressed ejection fraction (EF). We investigated the use of color TDI-derived left atrial (LA) and left ventricular (LV) longitudinal strain rate (SR) during isovolumic contraction (IC) to predict the invasively measured PCWP. METHODS AND RESULTS: Forty patients referred with symptoms of heart failure were prospectively studied [age: 56±8 years, 12 (30%) females, and mean LVEF: 51±14%]. PCWP was measured invasively immediately after echocardiography. Mitral annular IVV was measured for all patients and SR during the IC and ejection were measured for the LV (LVSR-IC, LVSR-Ej) as well as the LA (LASR-IC, and LASR-Ej). Atrioventricular SR during IC and Ej (AVSR-IC, AVSR-Ej) was calculated as the sum of the LV and LA values. Patients were classified and compared based on their EF into 19 (49%) with EF≥55%, and 21 (51%) with EF<55%. No significant differences were noted for age, sex, risk factors, and medications between both patients with EF≥55% and EF<55%. Compared to EF≥55%, patients with EF<55% had lower IVV (4.63±1.2 vs. 7.01±1.9 cm/s, P<0.001), LVSR-Ej (1±0.3 vs. 1.2±0.2, P=0.03), LASR-IC (1.3±0.6 vs. 1.9±1, P=0.03), LASR-Ej (1.5±0.5 vs. 2.6±1.3 s-1, P=0.001), AVSR-IC (2±0.8 vs. 2.7±1.06 s-1, P=0.023), and AVSR-Ej (2.5±0.6 vs. 3.9±1.1 s-1, <0.001). LVSR-IC, LVSR-Ej, LASR-IC, AVSR-IC, and IVV correlated with PCWP in only in EF<55%, with the strongest correlation noted for AVSR-IC (r=-0.72, <0.001). Other correlates with PCWP in EF<55% were E/e' and left atrial volume (r=0.47, 0.7, P=0.04, 0.001; respectively). Multivariate regression revealed that in patients with EF<55% AVSR-IC was the only independent predictor of PCWP. Finally, IVV correlated with LVSR-IC and LASR-IC and this correlation became strongest with AVSR-IC (r=0.77, 0.001). CONCLUSION: The combined LV and LA longitudinal SR during IC as represented by AVSR-IC showed a strong correlation with PCWP in patients with depressed EF. The correlation between mitral annular IVV and PCWP in those patients can be a product of this combination and may a function of atrioventricular mechanical coupling.