Aim: The “2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease” provides recommendations to guide clinicians in the diagnosis, genetic evaluation and family screening, medical therapy, endovascular and surgical treatment, and long-term surveillance of patients with aortic disease across its multiple clinical presentation subsets (ie, asymptomatic, stable symptomatic, and acute aortic syndromes). Methods: A comprehensive literature search was conducted from January 2021 to April 2021, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, CINHL Complete, and other selected databases relevant to this guideline. Additional relevant studies, published through June 2022 during the guideline writing process, were also considered by the writing committee, where appropriate. Structure: Recommendations from previously published AHA/ACC guidelines on thoracic aortic disease, peripheral artery disease, and bicuspid aortic valve disease have been updated with new evidence to guide clinicians. In addition, new recommendations addressing comprehensive care for patients with aortic disease have been developed. There is added emphasis on the role of shared decision making, especially in the management of patients with aortic disease both before and during pregnancy. The is also an increased emphasis on the importance of institutional interventional volume and multidisciplinary aortic team expertise in the care of patients with aortic disease.

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Objective A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret ‘big data’ sets in an automated and adaptive fashion, while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk, and to determine whether such models perform better than classical statistical analyses. Methods Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1,755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and utilized diverse clinical, demographic, imaging and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Results Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (AUC 0.87 versus 0.76, respectively, P=0.03), and predicting future mortality (AUC 0.76 versus 0.65, respectively, P=0.10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Conclusions Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes.

Background: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events (MACCE). There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, making it difficult to identify those who might benefit from more aggressive intervention. Thus, we aimed to develop a novel predictive model -using machine learning methods on electronic health record (EHR) data -to identify which PAD patients are most likely to develop MACCE.Methods and Results: Data were derived from patients diagnosed with PAD at two tertiary care institutions. Predictive models were built using a common data model (CDM) that allowed for utilization of both structured (coded) and unstructured (text) data. Only data from time of entry into the health system up to PAD diagnosis were used for modeling. Models were developed and tested using nested cross-validation. A total of 7,686 patients were included in learning our predictive models. Utilizing almost 1,000 variables, our best predictive model accurately determined which PAD patients would go on to develop MACCE with an area under the curve (AUC) of 0.81 (95% Confidence Interval, 0.80-0.83). Conclusions:Machine learning algorithms applied to data in the EHR can learn models that accurately identify PAD patients at risk of future MACCE, highlighting the great potential of EHR to provide automated risk stratification for cardiovascular diseases. Common data models that can enable cross-institution research and technology development could potentially be an important aspect of widespread adoption of newer risk-stratification models.

SummaryThe recent discovery of human-induced pluripotent stem cells (iPSC) has revolutionized the field of stem cells. iPSC have demonstrated that biological development is not an irreversible process and that mature adult somatic cells can be induced to become pluripotent. This breakthrough is projected to advance our current understanding of many disease processes and revolutionize the approach to effective therapeutics. Despite the great promise of iPSC, many translational challenges still remain. The authors review the basic concept of induction of pluripotency as a novel approach to understand cardiac regeneration, cardiovascular disease modeling, and drug discovery. They critically reflect on the current results of pre-clinical and clinical studies using iPSC for these applications with appropriate emphasis on the challenges facing clinical translation.