Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.

PURPOSE OF REVIEW: In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health. RECENT FINDINGS: Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.

Significance of indeterminate and abnormal stress electrocardiography despite normal imaging in patients with suspected coronary artery disease - An analysis of the PROMISE trial.

BACKGROUND: Abnormal and indeterminate electrocardiographic (ECG) changes during cardiac stress imaging are not uncommon. While the prognostic importance of abnormal ECG despite normal imaging has been previously studied, prognosis of indeterminate stress ECG changes is uncertain. METHODS: We studied the prognostic value of stress ECG changes in symptomatic patients without known CAD and normal stress imaging from the PROMISE trial. Patients with normal ECG (concordant), indeterminate ECG and abnormal ECG (discordant) were identified among those with negative exercise imaging stress test (EIST) and negative vasodilator nuclear stress test (PIST). Outcomes of interest were major adverse cardiovascular endpoint (MACE, including all-cause mortality, myocardial infarction, and unstable angina hospitalization) and likelihood of coronary revascularization. RESULTS: In EIST, indeterminate stress ECG [1.1% vs. 0.2% adjusted hazard ratio (aHR) 4.2, (95% CI 1.11-15.6), p = 0.034] and discordant ECG [7.2% vs. 0.2% adjusted hazard ratio (aHR) 27.6, (95% CI 9.6-79.8), p < 0.0001] were associated with increased likelihood of revascularization compared to normal stress ECG. Similar findings were observed with PIST [indeterminate vs concordant [1.7% vs. 0.5% adjusted hazard ratio (aHR) 5.9, (95% CI 1.1-31.7), p = 0.041; discordant vs concordant 15.4% vs. 0.5% adjusted hazard ratio (aHR) 24.2, (95% CI 4.6-127.7), p = 0.0002]. MACE rates were similar between ECG subgroups, in both EIST and PIST. CONCLUSION: In symptomatic patients without known CAD undergoing stress imaging, indeterminate and discordant ECG changes results may indicate presence of obstructive CAD despite normal imaging results and predict increased likelihood of coronary revascularization despite no significant difference in MACE.

Cardiac magnetic resonance for prophylactic implantable-cardioverter defibrillator therapy international study: prognostic value of cardiac magnetic resonance-derived right ventricular parameters substudy.

AIMS: Right ventricular systolic dysfunction (RVSD) is an important determinant of outcomes in heart failure (HF) cohorts. While the quantitative assessment of RV function is challenging using 2D-echocardiography, cardiac magnetic resonance (CMR) is the gold standard with its high spatial resolution and precise anatomical definition. We sought to investigate the prognostic value of CMR-derived RV systolic function in a large cohort of HF with reduced ejection fraction (HFrEF). METHODS AND RESULTS: Study cohort comprised of patients enrolled in the CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DefibrillAtor ThErapy registry who had HFrEF and had simultaneous baseline CMR and echocardiography (n = 2449). RVSD was defined as RV ejection fraction (RVEF) <45%. Kaplan-Meier curves and cox regression were used to investigate the association between RVSD and all-cause mortality (ACM). Mean age was 59.8 ± 14.0 years, 42.0% were female, and mean left ventricular ejection fraction (LVEF) was 34.0 ± 10.8. Median follow-up was 959 days (interquartile range: 560-1590). RVSD was present in 936 (38.2%) and was an independent predictor of ACM (adjusted hazard ratio = 1.44; 95% CI [1.09-1.91]; P = 0.01). On subgroup analyses, the prognostic value of RVSD was more pronounced in NYHA I/II than in NYHA III/IV, in LVEF <35% than in LVEF ≥35%, and in patients with renal dysfunction when compared to those with normal renal function. CONCLUSION: RV systolic dysfunction is an independent predictor of ACM in HFrEF, with a more pronounced prognostic value in select subgroups, likely reflecting the importance of RVSD in the early stages of HF progression.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016-2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

Role of Vasodilator Therapy in Acute Heart Failure.

Over the past decade, several trials have questioned the efficacy of vasodilator therapy in acute heart failure (AHF) in the absence of uncontrolled hypertension. In this article, we provide a unique review of the most valuable four trials that present the role of vasodilator therapy in the management of patients with AHF. These four trials have evaluated the efficacy of different types of vasodilators such as nesiritide, ulatritide, and serelaxin in the setting of AHF. Also, we compared comprehensive vasodilator therapy versus standard therapy to see if there is any effect on mortality and re-hospitalization.