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

Demsas, Falen; Joiner, Malachi M; Telma, Katherine A.; Flores, Alyssa M.; Teklu, Semhar; Ross, Elsie Gyang

doi:10.1053/j.semvascsurg.2022.05.003

Cited by 37 publications

(16 citation statements)

References 146 publications

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“…Given prior literature demonstrating the disproportionate impact of PAD on disadvantaged and minority populations, it is critical for future work to carefully consider aspects of diversity, equity, and inclusion in the management of patients with PAD. 28,47 Particularly, an increasing focus on addressing systemic and structural inequity related to social determinants of health, including economic stability, education, and access to health care, will be critical to improving PAD outcomes. 48 Our ML models can be applied in several ways.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, our analysis demonstrated associations between various demographic factors including age, sex, race, ethnicity, insurance status, ADI, and procedure setting and postoperative outcomes. Given prior literature demonstrating the disproportionate impact of PAD on disadvantaged and minority populations, it is critical for future work to carefully consider aspects of diversity, equity, and inclusion in the management of patients with PAD . Particularly, an increasing focus on addressing systemic and structural inequity related to social determinants of health, including economic stability, education, and access to health care, will be critical to improving PAD outcomes …”

Section: Discussionmentioning

confidence: 99%

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Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

Li,

Warren,

Eisenberg

et al. 2024

JAMA Netw Open

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ImportanceEndovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited.ObjectiveTo develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD.Design, Setting, and ParticipantsThis prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets.ExposuresA total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified.Main Outcomes and MeasuresUsing 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data.ResultsOverall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively.Conclusions and RelevanceIn this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

Li,

Warren,

Eisenberg

et al. 2024

JAMA Netw Open

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“…Data also indicate that there is a higher incidence of PAD in Black and Hispanic women compared with non-Hispanic White women. 54,55 In a study that identified and enrolled 403 patients from primary care clinic settings in the Houston area and included 55 White women, 28 Black women, and 71 Hispanic women, there were no significant differences by sex in PAD (ABI<0.9) prevalence. 56 In this cohort, the prevalence of PAD was 9.1% in White women, 21.9% in Black women, and 14.1% in Hispanic women ( P =0.11).…”

Section: Disparities In Pad By Sex and Race And Ethnicitymentioning

confidence: 98%

Sex and Racial Disparities in Peripheral Artery Disease

Divakaran,

Krawisz,

Secemsky

et al. 2023

ATVB

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Several studies have shown that women and racial and ethnic minority patients are at increased risk of developing lower extremity peripheral artery disease and suffering adverse outcomes from it, but a knowledge gap remains regarding the underlying causes of these increased risks. Both groups are more likely to be underdiagnosed, have poorly managed contributory comorbidities, and incur disparities in treatment and management postdiagnosis. Opportunities for improvement in the care of women and racial and ethnic minorities with peripheral artery disease include increased rates of screening, higher rates of clinical suspicion (particularly in the absence of typical symptoms of intermittent claudication), and more aggressive risk factor management before and after the diagnosis of peripheral artery disease.

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“…In addition, the female sex was associated with lower adherence to statin therapy. 14 Lower extremity revascularization. A social gradient is also discernible when examining the different outcomes of vascular procedures in patients with PAD.…”

Section: Ses and Pad Carementioning

confidence: 99%

“… 11 , 12 However, fewer reports exist on this association with peripheral artery disease (PAD). 13 , 14 Nevertheless, as PAD increases worldwide, 15 its consequences will likely raise demand for public health research and service. As an example, the need to reduce lower-limb amputations is worth highlighting.…”

Section: Introductionmentioning

confidence: 99%

The importance of socio-economic determinants of health in the care of patients with peripheral artery disease: A narrative review from VAS

et al. 2023

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Socio-economic determinants of health (SDoH) include various nonmedical factors in the socio-economic sphere with a potentially significant impact on health outcomes. Their effects manifest through several mediators/moderators (behavioral characteristics, physical environment, psychosocial circumstances, access to care, and biological factors). Various critical covariates (age, gender/sex, race/ethnicity, culture/acculturation, and disability status) also interact. Analyzing the effects of these factors is challenging due to their enormous complexity. Although the significance of SDoH for cardiovascular diseases is well documented, research regarding their impact on peripheral artery disease (PAD) occurrence and care is less well documented. This narrative review explores to what extent SDoH are multifaceted in PAD and how they are associated with its occurrence and care. Additionally, methodological issues that may hamper this effort are addressed. Finally, the most important question, whether this association may contribute to reasonable interventions aimed at SDoH, is analyzed. This endeavor requires attention to the social context, a whole systems approach, multilevel-thinking, and a broader alliance that reaches out to more stakeholders outside the medical sphere. More research is needed to justify the power in this concept to improve PAD-related outcomes like lower-extremity amputations. At the present time, some evidence, reasonable consideration, and intuitive reasoning support the implementation of various interventions in SDoH in this field.

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

Cited by 37 publications

References 146 publications

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

Sex and Racial Disparities in Peripheral Artery Disease

The importance of socio-economic determinants of health in the care of patients with peripheral artery disease: A narrative review from VAS

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