Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study

Can left ventricular entropy by cardiac magnetic resonance late gadolinium enhancement be a prognostic predictor in patients with left ventricular non-compaction?

PURPOSE

Left ventricular non-compaction (LVNC) is considered rare; however, the use of cardiac magnetic resonance (CMR) has shown that its incidence is not uncommon, and its clinical presentation remains variable, with an uncertain prognosis. Risk stratification of major adverse cardiac events (MACE) in patients with LVNC remains complex. Therefore, this study aims to determine whether tissue heterogeneity from late gadolinium enhancement-derived entropy is associated with MACE in patients with LVNC.

METHODS

This study was registered in the Clinical Trial Registry (CTR2200062045). Consecutive patients who underwent CMR imaging and were diagnosed with LVNC were followed up for MACE, which was defined by heart failure, arrhythmias, systemic embolism, and cardiac death. The patients were divided into MACE and non-MACE groups. The CMR parameters included left ventricular (LV) entropy, LV ejection fraction (LVEF), LV end-diastolic volume, LV end-systolic volume (LVESV), and LV mass (LVM).

RESULTS

Eighty-six patients (age: 45.48 ± 16.64 years; female: 62.7%; LVEF: 42.58 ± 17.20%) were followed up for a median of 18 months and experienced 30 MACE events (34.9%). The MACE group showed higher LV entropy, LVESV, and LVM and lower LVEF than the non-MACE group. LV entropy [hazard ratio (HR): 1.710, 95% confidence interval (CI): 1.078-2.714, P = 0.023] and LVEF (HR: 0.961, 95% CI: 0.936-0.988, P = 0.004) were independent predictors of MACE (P <0.050) according to the Cox regression analysis. Receiver operating characteristic curve analysis revealed that the area under the curve of LV entropy was 0.789 (95% CI: 0.687-0.869, P < 0.001), LVEF was 0.804 (95% CI: 0.699-0.878, P < 0.001), and the combined model of LV entropy and LVEF was 0.845 (95% CI: 0.751-0.914, P < 0.050).

CONCLUSION

LGE-derived LV entropy and LVEF are independent risk indicators of MACE in patients with LVNC. The combination of the two factors was more conducive to improving the prediction of MACE.

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

Heart failure: an update from the last years and a look at the near future

In the last years, major progress occurred in heart failure (HF) management. Quadruple therapy is now mandatory for all the patients with HF with reduced ejection fraction. Whilst verciguat is becoming available across several countries, omecamtiv mecarbil is waiting to be released for clinical use. Concurrent use of potassium-lowering agents may counteract hyperkalaemia and facilitate renin-angiotensin-aldosterone system inhibitor implementations. The results of the EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trial were confirmed by the Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction (DELIVER) trial, and we now have, for the first time, evidence for treatment of also patients with HF with preserved ejection fraction. In a pre-specified meta-analysis of major randomized controlled trials, sodium-glucose co-transporter-2 inhibitors reduced all-cause mortality, cardiovascular (CV) mortality, and HF hospitalization in the patients with HF regardless of left ventricular ejection fraction. Other steps forward have occurred in the treatment of decompensated HF. Acetazolamide in Acute Decompensated Heart Failure with Volume Overload (ADVOR) trial showed that the addition of intravenous acetazolamide to loop diuretics leads to greater decongestion vs. placebo. The addition of hydrochlorothiazide to loop diuretics was evaluated in the CLOROTIC trial. Torasemide did not change outcomes, compared with furosemide, in TRANSFORM-HF. Ferric derisomaltose had an effect on the primary outcome of CV mortality or HF rehospitalizations in IRONMAN (rate ratio 0.82; 95% confidence interval 0.66-1.02; P = 0.070). Further options for the treatment of HF, including device therapies, cardiac contractility modulation, and percutaneous treatment of valvulopathies, are summarized in this article.

Predictors of fatal arrhythmic events in patients with non-compaction cardiomyopathy: a systematic review

Left ventricular non-compaction cardiomyopathy (LVNC) is a congenital heart disease with autosomal dominant inheritance. This review aims to summarize the existing data about the predictors of fatal arrhythmias in patients with LVNC. Medline and Cochrane library databases were searched from inception to November 2021 for articles on LVNC. The reference lists of the relevant research studies as well as the relevant review studies and meta-analyses were also searched. Clinical symptoms and electrocardiogram findings such as left bundle branch block are significantly associated with ventricular arrhythmias. Other non-invasive tools such as Holter monitoring, echocardiography, and cardiac magnetic resonance (CMR) can provide additional value for risk stratification. CMR-derived left and right ventricular ejection fraction, left ventricular end-diastolic diameter, late gadolinium enhancement, and non-compacted to compacted myocardium ratio are predictive of ventricular arrhythmias. An electrophysiological study can provide additional prognostic data in patients with LVNC who are at moderate risk of ventricular arrhythmias. Risk stratification of LVNC patients with no prior history of a fatal arrhythmic event remains challenging. Symptoms assessment, electrocardiogram, Holter monitoring, and cardiac imaging should be performed on every patient, while an electrophysiological study should be performed for moderate-risk patients. Large cohort studies are needed for the construction of score models for arrhythmic risk stratification purposes.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

Ventricular arrhythmia management in patients with genetic cardiomyopathies

Genetic cardiomyopathies are associated with increased risk for cardiac arrhythmias and sudden cardiac death. The management of ventricular arrhythmias (VAs) in patients with these conditions can be nuanced due to particular disease-based considerations, yet data specifically addressing management in these patients are limited. Here we describe the current evidence-based approach to the management of ventricular rhythm disorders in patients with genetic forms of cardiomyopathy, namely, hypertrophic cardiomyopathy, arrhythmogenic cardiomyopathy, left ventricular noncompaction, and Brugada syndrome, including recommendations from consensus guideline statements when available.

A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy

Aims

Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based model.

Methods and results

Forty-nine outpatients with NCC diagnosis by echocardiography and magnetic resonance imaging (21 men, 42.8±14.8 years) were included. A comprehensive echocardiogram was performed. The layer-specific strain was analyzed from the apical two-, three, four-chamber views, short axis, and focused right ventricle views using 2D echocardiography (2DE) software. RBR was present in 44.9% of patients, and this group presented increased LV mass indexed (118±43.4 vs. 94.1±27.1g/m², P = 0.034), LV end-diastolic and end-systolic volumes (P< 0.001), E/e’ (12.2±8.68 vs. 7.69±3.13, P = 0.034), and decreased LV ejection fraction (40.7±8.71 vs. 58.9±8.76%, P < 0.001) when compared to patients without RBR. Also, patients with RBR presented a significant decrease of global longitudinal, radial, and circumferential strain. When ML model based on a random forest algorithm and a neural network model was applied, it found that twist, NC/C, torsion, LV ejection fraction, and diastolic dysfunction are the strongest predictors to RBR with accuracy, sensitivity, specificity, area under the curve of 0.93, 0.99, 0.80, and 0.88, respectively.

Conclusion

In this study, a random forest algorithm was capable of selecting the best echocardiographic predictors to RBR pattern in NCC patients, which was consistent with worse systolic, diastolic, and myocardium deformation indices. Prospective studies are warranted to evaluate the role of this tool for NCC risk stratification.

Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study

Aims

Left ventricular non‐compaction cardiomyopathy (LVNC) is a genetic heart disease, with heart failure, arrhythmias, and embolic events as main clinical manifestations. The goal of this study was to analyse a large set of echocardiographic (echo) and cardiac magnetic resonance imaging (CMRI) parameters using machine learning (ML) techniques to find imaging predictors of clinical outcomes in a long‐term follow‐up of LVNC patients.

Methods and results

Patients with echo and/or CMRI criteria of LVNC, followed from January 2011 to December 2017 in the heart failure section of a tertiary referral cardiologic hospital, were enrolled in a retrospective study. Two‐dimensional colour Doppler echocardiography and subsequent CMRI were carried out. Twenty‐four hour Holter monitoring was also performed in all patients. Death, cardiac transplantation, heart failure hospitalization, aborted sudden cardiac death, complex ventricular arrhythmias (sustained and non‐sustained ventricular tachycardia), and embolisms (i.e. stroke, pulmonary thromboembolism and/or peripheral arterial embolism) were registered and were referred to as major adverse cardiovascular events (MACEs) in this study. Recruited for the study were 108 LVNC patients, aged 38.3 ± 15.5 years, 48.1% men, diagnosed by echo and CMRI criteria. They were followed for 5.8 ± 3.9 years, and MACEs were registered. CMRI and echo parameters were analysed via a supervised ML methodology. Forty‐seven (43.5%) patients had at least one MACE. The best performance of imaging variables was achieved by combining four parameters: left ventricular (LV) ejection fraction (by CMRI), right ventricular (RV) end‐systolic volume (by CMRI), RV systolic dysfunction (by echo), and RV lower diameter (by CMRI) with accuracy, sensitivity, and specificity rates of 75.5%, 77%, 75%, respectively.

Conclusions

Our findings show the importance of biventricular assessment to detect the severity of this cardiomyopathy and to plan for early clinical intervention. In addition, this study shows that even patients with normal LV function and negative late gadolinium enhancement had MACE. ML is a promising tool for analysing a large set of parameters to stratify and predict prognosis in LVNC patients.