Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events.

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846-0.957) (clinical variables only) to 0.909 (95% CI: 0.856-0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002-0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations.

Evaluating prediction model performance.

This article highlights important performance metrics to consider when evaluating models developed for supervised classification or regression tasks using clinical data. When evaluating model performance, we detail the basics of confusion matrices, receiver operating characteristic curves, F1 scores, precision-recall curves, mean squared error, and other considerations. In this era, defined by the rapid proliferation of advanced prediction models, familiarity with various performance metrics beyond the area under the receiver operating characteristic curves and the nuances of evaluating model value upon implementation is essential to ensure effective resource allocation and optimal patient care delivery.

2022 ACC/AHA guideline for the diagnosis and management of aortic disease: A report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.

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.

2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.

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.

Disparities in peripheral artery disease care: A review and call for action.

Peripheral artery disease (PAD), the pathophysiologic narrowing of arterial blood vessels of the lower leg due to atherosclerosis, is a highly prevalent disease that affects more than 6 million individuals 40 years and older in the United States, with sharp increases in prevalence with age. Morbidity and mortality rates in patients with PAD range from 30% to 70% during the 5- to 15-year period after diagnosis and PAD is associated with poor health outcomes and reduced functionality and quality of life. Despite advances in medical, endovascular, and open surgical techniques, there is striking variation in care among population subgroups defined by sex, race and ethnicity, and socioeconomic status, with concomitant differences in preoperative medication optimization, amputation risk, and overall health outcomes. We reviewed studies from 1995 to 2021 to provide a comprehensive analysis of the current impact of disparities on the treatment and management of PAD and offer action items that require strategic partnership with primary care providers, researchers, patients, and their communities. With new technologies and collaborative approaches, optimal management across all population subgroups is possible.

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.

Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.

Development of a polygenic risk score to improve detection of peripheral artery disease.

INTRODUCTION: Peripheral artery disease (PAD) is a major cause of cardiovascular morbidity and mortality, yet timely diagnosis is elusive. Larger genome-wide association studies (GWAS) have now provided the ability to evaluate whether genetic data, in the form of genome-wide polygenic risk scores (PRS), can help improve our ability to identify patients at high risk of having PAD. METHODS: Using summary statistic data from the largest PAD GWAS from the Million Veteran Program, we developed PRSs with genome data from UK Biobank. We then evaluated the clinical utility of adding the best-performing PRS to a PAD clinical risk score. RESULTS: A total of 487,320 participants (5759 PAD cases) were included in our final genetic analysis. Compared to participants in the lowest 10% of PRS, those in the highest decile had 3.1 higher odds of having PAD (95% CI, 3.06-3.21). Additionally, a PAD PRS was associated with increased risk of having coronary artery disease, congestive heart failure, and cerebrovascular disease. The PRS significantly improved a clinical risk model (Net Reclassification Index = 0.07, p < 0.001), with most of the performance seen in downgrading risk of controls. Combining clinical and genetic data to detect risk of PAD resulted in a model with an area under the curve of 0.76 (95% CI, 0.75-0.77). CONCLUSION: We demonstrate that a genome-wide PRS can discriminate risk of PAD and other cardiovascular diseases. Adding a PAD PRS to clinical risk models may help improve detection of prevalent, but undiagnosed disease.

Collagen Fibril Orientation in Tissue Specimens From Atherosclerotic Plaque Explored Using Small Angle X-Ray Scattering.

Atherosclerotic plaques can gradually develop in certain arteries. Disruption of fibrous tissue in plaques can result in plaque rupture and thromboembolism, leading to heart attacks and strokes. Collagen fibrils are important tissue building blocks and tissue strength depends on how fibrils are oriented. Fibril orientation in plaque tissue may potentially influence vulnerability to disruption. While X-ray scattering has previously been used to characterize fibril orientations in soft tissues and bones, it has never been used for characterization of human atherosclerotic plaque tissue. This study served to explore fibril orientation in specimens from human plaques using small angle X-ray scattering (SAXS). Plaque tissue was extracted from human femoral and carotid arteries, and each tissue specimen contained a region of calcified material. Three-dimensional (3D) collagen fibril orientation was determined along scan lines that started away from and then extended toward a given calcification. Fibrils were found to be oriented mainly in the circumferential direction of the plaque tissue at the majority of locations away from calcifications. However, in a number of cases, the dominant fibril direction differed near a calcification, changing from circumferential to longitudinal or thickness (radial) directions. Further study is needed to elucidate how these fibril orientations may influence plaque tissue stress-strain behavior and vulnerability to rupture.

Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.

Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.

Evaluation of regional variations in length of stay after elective, uncomplicated carotid endarterectomy in North America.

OBJECTIVE: The objective of this study was to evaluate factors affecting regional variation in length of stay (LOS) after elective, uncomplicated carotid endarterectomy (CEA). METHODS: Data were obtained from the Vascular Quality Initiative database and included patients with complete data who received elective CEA without complications between 2012 and 2017 across 18 regions in North America and 294 centers. The main outcome measure was LOS >1 day after surgery (LOS >1 postoperative day [POD]). Using least absolute shrinkage and selection operator regression, multivariable modeling, and mixed-effects general linear modeling, we evaluated whether regional variations in LOS were independent of demographic, clinical, or center-related factors and to what extent these factors accounted for postoperative variation in LOS. RESULTS: A total of 36,004 patients were included. Mean postprocedure LOS was 1.6 ± 6.6 days. Overall, 24% of patients had an LOS >1 POD. After adjustment for important demographic, clinical, and center-related factors, the region in which a patient was treated independently and significantly affected LOS after elective, uncomplicated CEA. Region and center of treatment accounted for 18% of LOS variation. Demographic, clinical, and surgical factors accounted for another 32% of variation in LOS. Of these factors, postoperative discharge to a facility other than home (odds ratio [OR], 6.3; confidence interval [CI], 5.2-7.6), use of intravenous (IV) vasoactive agents (OR, 3.2; CI, 3-3.4), intraoperative drain placement (OR, 1.4; CI, 1.3-1.55), and female sex (OR, 1.4; CI, 1.3-1.5) were associated with longer LOS. Factors associated with LOS ≤1 POD included preoperative aspirin (OR, 0.88; CI, 0.8-0.96) and statin use (OR, 0.9; CI, 0.83-0.98), high surgeon volume (highest quartile: OR, 0.68; CI, 0.5-0.87), and completion evaluation after CEA (eg, Doppler, ultrasound; OR, 0.87; CI, 0.8-0.95). We also found that use of IV vasoactive medications varied significantly across regions, independent of demographic and clinical factors. CONCLUSIONS: Significant regional variation in LOS exists after elective, uncomplicated CEA even after controlling for a wide range of important factors, indicating that there remain unmeasured causes of longer LOS in some regions. Even so, modification of certain clinical practices may reduce overall LOS. Regional differences in use of IV vasoactive medications not driven by clinical factors warrant further analysis, given the strong association with longer LOS.

Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.

BACKGROUND: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. 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 data-to identify which PAD patients are most likely to develop major adverse cardiac and cerebrovascular events. METHODS AND RESULTS: Data were derived from patients diagnosed with PAD at 2 tertiary care institutions. Predictive models were built using a common data model 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 7686 patients were included in learning our predictive models. Utilizing almost 1000 variables, our best predictive model accurately determined which PAD patients would go on to develop major adverse cardiac and cerebrovascular events with an area under the curve of 0.81 (95% CI, 0.80-0.83). CONCLUSIONS: Machine learning algorithms applied to data in the electronic health record can learn models that accurately identify PAD patients at risk of future major adverse cardiac and cerebrovascular events, highlighting the great potential of electronic health records 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.

Enhanced Quality Measurement Event Detection: An Application to Physician Reporting.

The wide-scale adoption of electronic health records (EHR)s has increased the availability of routinely collected clinical data in electronic form that can be used to improve the reporting of quality of care. However, the bulk of information in the EHR is in unstructured form (e.g., free-text clinical notes) and not amenable to automated reporting. Traditional methods are based on structured diagnostic and billing data that provide efficient, but inaccurate or incomplete summaries of actual or relevant care processes and patient outcomes. To assess the feasibility and benefit of implementing enhanced EHR- based physician quality measurement and reporting, which includes the analysis of unstructured free- text clinical notes, we conducted a retrospective study to compare traditional and enhanced approaches for reporting ten physician quality measures from multiple National Quality Strategy domains. We found that our enhanced approach enabled the calculation of five Physician Quality and Performance System measures not measureable in billing or diagnostic codes and resulted in over a five-fold increase in event at an average precision of 88 percent (95 percent CI: 83-93 percent). Our work suggests that enhanced EHR-based quality measurement can increase event detection for establishing value-based payment arrangements and can expedite quality reporting for physician practices, which are increasingly burdened by the process of manual chart review for quality reporting.

The use of machine learning for the identification of peripheral artery disease and future mortality risk.

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 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used 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 (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .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.

Statin Intensity or Achieved LDL? Practice-based Evidence for the Evaluation of New Cholesterol Treatment Guidelines.

BACKGROUND: The recently updated American College of Cardiology/American Heart Association cholesterol treatment guidelines outline a paradigm shift in the approach to cardiovascular risk reduction. One major change included a recommendation that practitioners prescribe fixed dose statin regimens rather than focus on specific LDL targets. The goal of this study was to determine whether achieved LDL or statin intensity was more strongly associated with major adverse cardiac events (MACE) using practice-based data from electronic health records (EHR). METHODS: We analyzed the EHR data of more than 40,000 adult patients on statin therapy between 1995 and 2013. Demographic and clinical variables were extracted from coded data and unstructured clinical text. To account for treatment selection bias we performed propensity score stratification as well as 1:1 propensity score matched analyses. Conditional Cox proportional hazards modeling was used to identify variables associated with MACE. RESULTS: We identified 7,373 adults with complete data whose cholesterol appeared to be actively managed. In a stratified propensity score analysis of the entire cohort over 3.3 years of follow-up, achieved LDL was a significant predictor of MACE outcome (Hazard Ratio 1.1; 95% confidence interval, 1.05-1.2; P < 0.0004), while statin intensity was not. In a 1:1 propensity score matched analysis performed to more aggressively control for covariate balance between treatment groups, achieved LDL remained significantly associated with MACE (HR 1.3; 95% CI, 1.03-1.7; P = 0.03) while treatment intensity again was not a significant predictor. CONCLUSIONS: Using EHR data we found that on-treatment achieved LDL level was a significant predictor of MACE. Statin intensity alone was not associated with outcomes. These findings imply that despite recent guidelines, achieved LDL levels are clinically important and LDL titration strategies warrant further investigation in clinical trials.

The Promise and Challenge of Induced Pluripotent Stem Cells for Cardiovascular Applications.

The recent discovery of human-induced pluripotent stem cells (iPSCs) has revolutionized the field of stem cells. iPSCs 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 iPSCs, many translational challenges still remain. In this article, we review the basic concept of induction of pluripotency as a novel approach to understand cardiac regeneration, cardiovascular disease modeling and drug discovery. We critically reflect on the current results of preclinical and clinical studies using iPSCs for these applications with appropriate emphasis on the challenges facing clinical translation.