Role of nuclear cardiology for guiding device therapy in patients with heart failure

Heart failure is a dynamic condition with high morbidity and mortality and its prognosis should be reassessed frequently, particularly in patients for whom critical treatment decisions may depend on the results of prognostication. In patients with heart failure, nuclear cardiology techniques are useful to establish the etiology and the severity of the disease, while fewer studies have explored the potential capability of nuclear cardiology to guide cardiac resynchronization therapy(CRT) and to select patients for implantable cardioverter defibrillators(ICD). Left ventricular synchrony may be assessed by radionuclide angiography or gated singlephoton emission computed tomography myocardial perfusion scintigraphy. These modalities have shown promise as predictors of CRT outcome using phase analysis. Combined assessment of myocardial viability and left ventricular dyssynchrony is feasible using positron emission tomography and could improve conventional response prediction criteria for CRT. Preliminary data also exists on integrated positron emission tomography/computed tomography approach for assessing myocardial viability, identifying the location of biventricular pacemaker leads, and obtaining left ventricular functional data, including contractile phase analysis. Finally, cardiac imaging with autonomic radiotracers may be useful in predicting CRT response and for identifying patients at risk for sudden cardiac death, therefore potentially offering a way to select patients for both CRT and ICD therapy. Prospective trials where imaging is combined with image-test driven therapy are needed to better define the role of nuclear cardiology for guiding device therapy in patients with heart failure.

Current applications of big data and machine learning in cardiology

Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.

Cardiac hybrid imaging: novel tracers for novel targets

Non-invasive cardiac imaging has explored enormous advances in the last few decades.In particular,hybrid imaging represents the fusion of information from multiple imaging modalities,allowing to provide a more comprehensive dataset compared to traditional imaging techniques in patients with cardiovascular diseases.The complementary anatomical,functional and molecular information provided by hybrid systems are able to simplify the evaluation procedure of various pathologies in a routine clinical setting.The diagnostic capability of hybrid imaging modalities can be further enhanced by introducing novel and specific imaging biomarkers.The aim of this review is to cover the most recent advancements in radiotracers development for SPECT/CT,PET/CT,and PET/MRI for cardiovascular diseases.