Sex differences in the prevalence of peripheral artery disease in patients undergoing coronary catheterization

Rafie, Amir H Sadrzadeh; Stefanick, Marcia L.; Sims, Stacy T.; Phan, Tiffany; Higgins, Mamie; Gabriel, Andr e; Assimes, Themistocles L.; Narasimhan, Balasubramanian; Nead, Kevin T.; Myers, Jonathan; Olin, Jeffrey W.; Cooke, John P.

doi:10.1177/1358863x10388345

Cited by 33 publications

(30 citation statements)

References 35 publications

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“…Patient data were derived from the Genetic Determinants of Peripheral Artery Disease (GenePAD) study, a prospective observational study funded by the National Heart, Lung and Blood Institute with the goal of identifying key demographic, clinical and genomic factors that differentiate patients with PAD from those without disease 9, 10 . While PAD was the disease of interest in this observational study, it was not used as a factor in enrolling patients.…”

Section: Methodsmentioning

confidence: 99%

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

Ross

Shah

Dalman

et al. 2016

Journal of Vascular Surgery

119

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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.

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Section: Methodsmentioning

confidence: 99%

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

Ross

Shah

Dalman

et al. 2016

Journal of Vascular Surgery

119

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“…In turn, EC dysfunction is a feature common to multiple chronic diseases including diabetes mellitus, heart failure, and stroke. This same constellation of diseases displays male/female differences with respect to incidence, onset, severity, morbidity, and mortality [72, 100, 120]. …”

Section: The Common Constituent Separating “Inside From Out”: the Endmentioning

confidence: 99%

Sex-Specific Characteristics of the Microcirculation

Huxley

Kemp

2018

Advances in Experimental Medicine and Biology

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The requirements of metabolizing tissue are both continuous and variable; accordingly, the microvasculature serving that tissue must be similarly dynamic. Just as it is recognized that males and females of the same species have differing metabolic requirements, is it not likely that the microvasculature serving these tissues will differ by sex? This section focusing on the constituents of the microcirculation identifies what is known presently about the role sex plays in matching metabolic demand with microvascular function and areas requiring additional study. Many of the identified sex differences are subtle and easily ignored. In the aggregate, though, they can profoundly alter phenotype, especially under stressful conditions including pregnancy, exercise, and disease states ranging from diabetes to heart failure. Although the features presently identified to “have sex” range from differences in growth, morphology, protein expression, and intracellular signaling, males and females alike achieve homeostasis, likely by different means. Studies of microvascular sexual dimorphism are also identifying age as an independent but interacting factor requiring additional attention. Overall, attempting to ignore either sex and/or age is inappropriate and will prevent the design and implementation of appropriate interventions to present, ameliorate, or correct microvascular dysfunction.

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“…9, 23 As previously described 24 , a subgroup of 470 individuals was selected to characterize the role of biomarkers in PAD. The GenePAD study was approved by the Stanford University and Mount Sinai School of Medicine Committees for the Protection of Human Subjects.…”

Section: Methodsmentioning

confidence: 99%

“…4-8 Despite this, PAD remains highly underdiagnosed; for instance, over half the patients identified as having PAD in the PARTNERS study were newly diagnosed. 2 These low rates of diagnosis in non-specialty primary care clinics are similarly found among cardiologists 9 , and may be due to the low occurrence of the classic overt symptomology of intermittent claudication (11%) 2 or to practitioners lacking the appropriate equipment, training, or staff. 10, 11 …”

Section: Introductionmentioning

confidence: 98%

The combination of 9p21.3 genotype and biomarker profile improves a peripheral artery disease risk prediction model

et al. 2013

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Objective Peripheral artery disease (PAD) is a highly morbid condition affecting more than 8 million Americans. Frequently, PAD patients are unrecognized and therefore do not receive appropriate therapies. Therefore, new methods to identify PAD have been pursued, but have thus far had only modest success. Here we describe a new approach combining genomic and metabolic information to enhance the diagnosis of PAD. Methods and Results We measured the genotype of the chromosome 9p21 cardiovascular-risk polymorphism rs10757269 as well as the biomarkers C-reactive protein, cystatin C, beta-2-microglobulin, and plasma glucose in a study population of 393 patients undergoing coronary angiography. The rs10757269 allele was associated with PAD status (ankle-brachial index < 0.9) independent of biomarkers and traditional cardiovascular risk factors (odds ratio=1.92; 95% confidence interval, 1.29-2.85). Importantly, compared to a previously validated risk factor-based PAD prediction model, the addition of biomarkers and rs10757269 significantly and incrementally improved PAD risk prediction as assessed by the net reclassification index (NRI = 33.5%; p=0.001) and integrated discrimination improvement (IDI = 0.016; p=0.017). Conclusions A model including a panel of biomarkers, which includes both genomic information (which is reflective of heritable risk) and metabolic information (which integrates environmental exposures), predicts the presence or absence of PAD better than established risk models, suggesting clinical utility for the diagnosis of PAD.

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Sex differences in the prevalence of peripheral artery disease in patients undergoing coronary catheterization

Cited by 33 publications

References 35 publications

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

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

Sex-Specific Characteristics of the Microcirculation

The combination of 9p21.3 genotype and biomarker profile improves a peripheral artery disease risk prediction model

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