Left ventricular shape predicts arrhythmic risk in fibrotic dilated cardiomyopathy

Prediction of sudden cardiac death using artificial intelligence: Current status and future directions

Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among people who suffer a SCD, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators for SCD prevention. In response, artificial intelligence (AI) holds promise for personalized SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate nonlinear patterns between complex data and defined end points, and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.

Investigating the relevance of nucleotide metabolism in the prognosis of glioblastoma through bioinformatics models

Nucleotide metabolism (NM) is a fundamental process that enables the rapid growth of tumors. Glioblastoma (GBM) primarily relies on NM for its invasion, leading to severe clinical outcomes. This study focuses on NM to identify potential biomarkers associated with GBM. Publicly available databases were used as the primary data source for this study, excluding biological tissue samples. We identified and evaluated key genes involved in NM, followed by developing and validating a prognostic model. Patients were classified into high- and low-risk groups based on this model, and the two groups were compared with respect to cellular immunity and mutation profiles. The biomarkers were confirmed using real-time reverse-transcriptase polymerase chain reaction. Our study identified UPP1, CDA, NUDT1, and ADSL as significant biomarkers associated with prognosis, all of which were upregulated in patients with GBM. The risk score and clinical factors such as age, sex, GBM stage, MGMT promoter status, and IDH mutation status were found to be independent prognostic factors. Patients with glioblastoma showed a higher overall mutation burden. Using bioinformatics, this study identifies key factors associated with NM in GBM that may influence patient prognosis. This study enhances our understanding of GBM, provides valuable insights for further research, and serves as a reference for evaluating patient outcomes.

Identifying left atrium and left atrial appendage prone to thrombus formation in patients with atrial fibrillation using statistical shape modeling

BACKGROUND

The morphology of the left atrium (LA) and left atrial appendage (LAA) is associated with LAA thrombus formation (LAAT) in patients with atrial fibrillation (AF). Statistical shape modeling (SSM) could be a comprehensive and objective method for evaluating LA/LAA shape, thereby improving LAAT risk assessment.

METHODS AND RESULTS

In this individual-matched case-control study, 110 pairs of AF patients with or without LAAT were compared. Using SSM of cardiac computed tomography angiography images, we developed a LA/LAA shape deformation score (LADS). LADS was significantly higher in the LAAT group (4.0 ± 5.0 vs. -4.0 ± 6.5, P < 0.01) and independently associated with LAAT (odds ratio for each point increase in LADS: 1.31, 95 % confidence interval: 1.21-1.42, P < 0.01). LAAT risk assessment was significantly improved by the addition of LADS to clinical characteristics plus traditional structural parameters, evaluated by the area under the receiver-operating characteristic curve (0.879 vs 0.788, P < 0.01), net reclassification improvement (30.9 %, 95 % CI: 23.6-38.1 %, P < 0.05) and integrated discrimination improvement (18.5 %, 95 %CI: 16.8-20.3 %, P < 0.05). The predictive role of LADS in LAAT was confirmed by an unmatched external validation study including 406 patients with AF (19 cases with LAAT). Computational fluid dynamics analysis of 20 patients showed a significant association between LADS and endothelial cell activation potential, time-averaged wall shear stress, relative residence time, and blood velocity in the LAA.

CONCLUSION

SSM-derived LADS was significantly associated with LAAT and improved LAAT risk assessment. The relationship between LAAT and LADS may be attributed to mechanisms involving endothelial dysfunction and blood stasis.

Beyond the root: Geometric characterization for the diagnosis of syndromic heritable thoracic aortic diseases

Syndromic heritable thoracic aortic diseases (sHTAD), such as Marfan (MFS) or Loeys-Dietz (LDS) syndromes, involve high risk of life threatening aortic events. Diagnosis of syndromic features alone is difficult, and negative genetic tests do not necessarily exclude a genetic or hereditary condition. Periodic 3D imaging of the aorta is recommended in patients with aortic disease. Thus, an imaging-based approach aimed at identifying unique features of aortic geometry can be highly effective for diagnosing sHTAD and assessing risk. In this study, we present a method that can help identify the manifestations of sHTAD by focusing on the entire geometry of the thoracic aorta, rather than only using measurements of dilation of the aortic root. We analyze the geometric phenotype of 97 patients with genetically confirmed sHTAD (79 MF and 18 LDS) and of 45 healthy volunteers, using 3D aorta meshes obtained from phase contrast-enhanced magnetic resonance angiograms computed from 4D flow cardiac magnetic resonance. We build a geometric encoding of the aorta, based on a vessel coordinate system, and use several mathematical models to discriminate between controls and patients with sHTAD: a baseline scenario, based on aortic root dimensions only, a descriptor typically used in sHTAD patients; a low dimensional scenario, with a reduce encoding using principal component analysis; and a high-dimensional scenario, which included the full coefficient representation for geometry encoding, aiming to capture finer geometric details. The results indicate that considering the anatomy of the whole thoracic aorta can improve predictive ability. We achieve precision and sensitivity values over 0.8, with a specificity of over 70% in all the models used, while a single value classifiers (based only on aortic root diameter) demonstrated a trade-off between sensitivity and specificity. Using the mathematical properties of the vessel coordinate system representation, feature importance is mapped onto a set of anatomical traits that are used by the models to do the classification, thus providing interpretability of the results. This analysis indicates that in addition to the diameter of the aortic root, aortic elongation and a narrowing of the descending thoracic aorta may be markers of positive sHTAD.

Arrhythmic risk stratification in patients with left ventricular ring-like scar

AIMS

Left ventricular (LV) ring-like scar on cardiac magnetic resonance (CMR) has been linked to malignant arrhythmias in patients with non-ischemic cardiomyopathy. This study aimed to perform a comprehensive evaluation of this phenotype and to identify risk factors for life-threatening arrhythmic events (LAEs), a composite of sudden cardiac death (SCD), aborted SCD, and sustained ventricular tachycardia.

METHODS AND RESULTS

One-hundred-fifteen patients (median age 39 [IQR 28-52], 42% females) were identified at 6 referral centres. Inclusion criteria were ring-like LV scar (≥ 3 contiguous segments with subepicardial/midwall late gadolinium enhancement (LGE) in the same slice) and one among: pathogenic/likely pathogenic genetic variant, family history for cardiomyopathy, or arrhythmogenic cardiomyopathy diagnosis. During the study follow-up, survival-free from LAEs was 60% (3.8 events/100 patients/year); at a median follow-up of 4.6 years (IQR 1.7-8.4) it was 84%. On multivariable analysis, anterior Q waves (HR:1.030, 95% CI:1.014-1.046, p < 0.001), QRS width (HR:4.642, 95% CI:1.296-16.628, p=0.018), and LV end-diastolic volume index (LVEDVi) (HR:1.011, 95% CI:1.001-1.021, per mL/m2 increase, p=0.040) were independently associated with LAEs; with good discrimination power (Harrell's C-index=0.796). Three risk categories were identified: normal ECG, abnormal ECG and no LAEs predictive variables, abnormal ECG and ≥ 1 LAEs predictive variables, with a decreasing survival from 100% to 65% and 49%, respectively (Log-rank test = 0.015).

CONCLUSIONS

In this study, the LV ring-like scar phenotype was associated with a high rate of malignant arrhythmias in presence of anterior Q waves, QRS prolongation, and increased LVEDVi. A normal ECG identified a lower risk subgroup.

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Preeclampsia Associated Differences in the Placenta, Fetal Brain, and Maternal Heart Can Be Demonstrated Antenatally: An Observational Cohort Study Using MRI

BACKGROUND

Preeclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multiorgan approach to magnetic resonance imaging (MRI) investigation of preeclampsia, with the acquisition of maternal cardiac, placental, and fetal brain anatomic and functional imaging.

METHODS

An observational study was performed recruiting 3 groups of pregnant women: those with preeclampsia, chronic hypertension, or no medical complications. All women underwent a cardiac MRI, and pregnant women underwent a placental-fetal MRI. Cardiac analysis for structural, morphological, and flow data were undertaken; placenta and fetal brain volumetric and T2* (which describes relative tissue oxygenation) data were obtained. All results were corrected for gestational age. A nonpregnant cohort was identified for inclusion in the statistical shape analysis.

RESULTS

Seventy-eight MRIs were obtained during pregnancy. Cardiac MRI analysis demonstrated higher left ventricular mass in preeclampsia with 3-dimensional modeling revealing additional specific characteristics of eccentricity and outflow track remodeling. Pregnancies affected by preeclampsia demonstrated lower placental and fetal brain T2*. Within the preeclampsia group, 23% placental T2* results were consistent with controls, these were the only cases with normal placental histopathology. Fetal brain T2* results were consistent with normal controls in 31% of cases.

CONCLUSIONS

We present the first holistic assessment of the immediate implications of preeclampsia on maternal heart, placenta, and fetal brain. As well as having potential clinical implications for the risk stratification and management of women with preeclampsia, this gives an insight into the disease mechanism.

The digital journey: 25 years of digital development in electrophysiology from an Europace perspective

AIMS

Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal.

RESULTS

In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP.

CONCLUSION

Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.

Characterisation of placental, fetal brain and maternal cardiac structure and function in pre-eclampsia using MRI

Background

Pre-eclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multi-system approach to MRI investigation of pre-eclampsia, with acquisition of maternal cardiac, placental, and fetal brain anatomical and functional imaging.

Methods

A prospective study was carried out recruiting pregnant women with pre-eclampsia, chronic hypertension, or no medical complications, and a non-pregnant female cohort. All women underwent a cardiac MRI, and pregnant women underwent a fetal-placental MRI. Cardiac analysis for structural, morphological and flow data was undertaken; placenta and fetal brain volumetric and T2* data were obtained. All results were corrected for gestational age.

Results

Seventy-eight MRIs were obtained during pregnancy. Pregnancies affected by pre-eclampsia demonstrated lower placental and fetal brain T2*. Within the pre-eclampsia group, three placental T2* results were within the normal range, these were the only cases with normal placental histopathology. Similarly, three fetal brain T2* results were within the normal range; these cases had no evidence of cerebral redistribution on fetal Dopplers. Cardiac MRI analysis demonstrated higher left ventricular mass in pre-eclampsia with 3D modelling revealing additional specific characteristics of eccentricity and outflow track remodelling.

Conclusions

We present the first holistic assessment of the immediate implications of pre-eclampsia on the placenta, maternal heart, and fetal brain. As well as having potential clinical implications for the risk-stratification and management of women with pre-eclampsia, this gives an insight into disease mechanism.

Primer on Machine Learning in Electrophysiology

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.

Machine learning to predict left ventricular reverse remodeling by guideline-directed medical therapy by utilizing texture feature of extracellular volume fraction in patients with non-ischemic dilated cardiomyopathy

Extracellular volume fraction (ECV) by cardiac magnetic resonance (CMR) allows for the non-invasive quantification of diffuse myocardial fibrosis. Texture analysis and machine learning are now gathering attention in the medical field to exploit the ability of diagnostic imaging for various diseases. This study aimed to investigate the predictive value of texture analysis of ECV and machine learning for predicting response to guideline-directed medical therapy (GDMT) for patients with non-ischemic dilated cardiomyopathy (NIDCM). A total of one-hundred and fourteen NIDCM patients [age: 63 ± 12 years, 91 (81%) males] were retrospectively analyzed. We performed texture analysis of ECV mapping of LV myocardium using dedicated software. We calculated nine histogram-based features (mean, standard deviation, maximum, minimum, etc.) and five gray-level co-occurrence matrices. Five machine learning techniques and the fivefold cross-validation method were used to develop prediction models for LVRR by GDMT based on 14 texture parameters on ECV mapping. We defined the LVRR as follows: LVEF increased ≥ 10% points and decreased LVEDV ≥ 10% on echocardiography after GDMT > 12 months. Fifty (44%) patients were classified as non-responders. The area under the receiver operating characteristics curve for predicting non-responder was 0.82 for eXtreme Gradient Boosting, 0.85 for support vector machine, 0.76 for multi-layer perception, 0.81 for Naïve Bayes, 0.77 for logistic regression, respectively. Mean ECV value was the most critical factor among texture features for differentiating NIDCM patients with LVRR and those without (0.28 ± 0.03 vs. 0.36 ± 0.06, p < 0.001). Machine learning analysis using the support vector machine may be helpful in detecting high-risk NIDCM patients resistant to GDMT. Mean ECV is the most crucial feature among texture features.

Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events

Background

Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD).

Objective

To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD.

Methods

Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling.

Results

Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38-2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08-1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29-1.99, p = <0.001.

Conclusion

Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.

From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy

Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs) later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) implantation to prevent VT-related sudden cardiac death is solely based on symptoms and left ventricular ejection fraction. Catheter ablation of scar-related VTs is performed following ICD therapy, reducing VTs, painful shocks, anxiety, depression and worsening heart failure. We postulate that better prediction of the occurrence and circuit of VT, will improve identification of patients at risk for VT and boost preventive ablation, reducing mortality and morbidity. For this purpose, multiple time-evolving aspects of the underlying pathophysiology, including the anatomical substrate, triggers and modulators, should be part of VT prediction models. We envision digital twins as a solution combining clinical expertise with three prediction approaches: evidence-based medicine (clinical practice), data-driven models (data science) and mechanistic models (biomedical engineering). This paper aims to create a mutual understanding between experts in the different fields by providing a comprehensive description of the clinical problem and the three approaches in an understandable manner, leveraging future collaborations and technological innovations for clinical decision support. Moreover, it defines open challenges and gains for digital twin solutions and discusses the potential of hybrid modelling.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.