Endovascular therapy versus bypass for chronic limb-threatening ischemia in a real-world practice.

OBJECTIVE: The recent Best Endovascular vs Best Surgical Therapy in Patients with Critical Limb Ischemia (BEST-CLI) study showed that bypass was superior to endovascular therapy (ET) in patients with chronic limb-threatening ischemia (CLTI) deemed suitable for either approach who had an available single-segment great saphenous vein (GSV). However, the superiority of bypass among those lacking GSV was not established. We aimed to examine comparative treatment outcomes from a real-world CLTI population using the Vascular Quality Initiative-Medicare-linked database. METHODS: We queried the Vascular Quality Initiative-Medicare-linked database for patients with CLTI who underwent first-time lower extremity revascularization (2010-2019). We performed two one-to-one propensity score matchings (PSMs): ET vs bypass with GSV (BWGSV) and ET vs bypass with a prosthetic graft (BWPG). The primary outcome was amputation-free survival. Secondary outcomes were freedom from amputation and overall survival (OS). RESULTS: Three cohorts were queried: BWGSV (N = 5279, 14.7%), BWPG (N = 2778, 7.7%), and ET (N = 27,977, 77.6%). PSM produced two sets of well-matched cohorts: 4705 pairs of ET vs BWGSV and 2583 pairs of ET vs BWPG. In the matched cohorts of ET vs BWGSV, ET was associated with greater hazards of death (hazard ratio [HR] = 1.34, 95% confidence interval [CI], 1.25-1.43; P < .001), amputation (HR = 1.30, 95% CI, 1.17-1.44; P < .001), and amputation/death (HR = 1.32, 95% CI, 1.24-1.40; P < .001) up to 4 years. In the matched cohorts of ET vs BWPG, ET was associated with greater hazards of death up to 2 years (HR = 1.11, 95% CI, 1.00-1.22; P = .042) but not amputation or amputation/death. CONCLUSIONS: In this real-world multi-institutional Medicare-linked PSM analysis, we found that BWGSV is superior to ET in terms of OS, freedom from amputation, and amputation-free survival up to 4 years. Moreover, BWPG was superior to ET in terms of OS up to 2 years. Our study confirms the superiority of BWGSV to ET as observed in the BEST-CLI trial.

Women Experience Higher Rates of Mortality Following Thoracic Endovascular Aneurysm Repair.

OBJECTIVES: Thoracic endovascular aortic repair (TEVAR) and complex endovascular thoraco-abdominal aneurysm repair (cEVAR) has been increasingly adopted in the treatment of thoracic and thoracoabdominal aorta aneurysms, offering a less invasive approach for patients with appropriate anatomy. Women usually present with smaller aortic diameter. However, they usually have greater aneurysm growth rates. How sex can affect postoperative and short-term outcomes after TEVAR is not well reported. The aim of this study was to assess outcomes in female versus male patients undergoing TEVAR for treatment of thoracic and thoracoabdominal aneurysms in a Medicare-linked database. METHODS: We retrospectively reviewed patients undergoing TEVAR for thoracic and thoracoabdominal aneurysm repair in the Vascular Quality Initiative (VQI) Vascular Implant Surveillance and Interventional Outcomes Network (VISION) database from 2003 to 2018. Patients were divided into males and females. Patients presented with ruptured aneurysm were excluded from the analysis. Postoperative outcomes included in-hospital stroke, myocardial infarction (MI), spinal cord ischemia, and 30-day mortality. One-year outcomes included mortality, aneurysmal rupture, and reintervention. Postoperative outcomes were assessed using multivariable logistic regression analysis and one-year outcomes were evaluated using Kaplan Meier Survival and Cox regression analyses. RESULTS: A total of 3,058 males and 1,843 females were available for the analysis. Female patients had smaller median aortic diameter, were more likely to be black, with chronic obstructive pulmonary disease, and chronic kidney disease, and to be symptomatic on presentation. Male patients were more likely to be on preoperative medications such as aspirin, beta-blockers, angiotensin-converting enzyme inhibitors, P2Y12 antagonists, and anticoagulants. After adjusting for potential confounders, female gender was associated with double the risk of in-hospital stroke (OR: 2.3, 95%CI ((1.5-3.7), P<0.001) and 80% increase in 30-day mortality (OR: 1.8, 95%CI (1.3-2.6), P=0.001). At one year, female gender was associated with a higher risk of mortality (HR: 1.2, 95%CI (1.05-1.4), P=0.011). There was a trend towards higher risk of reintervention (HR: 1.2, 95%CI (0.97-1.6), P=0.079). CONCLUSIONS: Mortality after TEVAR seems to be higher in female patients at 30 days and up to one year of follow-up. Female patients also face a two times higher risk of in-hospital stroke. Future studies with a larger female population should aim to identify and potentially ameliorate the factors associated with these unfavorable outcomes in females.

Association between Statin use and Incident Peripheral Artery Disease According to Race, Age, and Presence of Depression in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Peripheral artery disease (PAD) is associated with high morbidity and mortality and has been commonly described as a coronary heart disease equivalent. Statin medications are recommended for primary prevention of atherosclerotic cardiovascular disease (CVD) among other indications. Therefore, understanding the longitudinal relationship of incident PAD is necessary to inform future research on how to prevent the disease. Depression complicates CVD patients' ability to properly adhere to their medications, yet the effect of depression on the relationship between statin use and incident PAD is understudied. People with PAD have a higher incidence of depressive symptoms than people without PAD. Black American and Hispanic populations are disproportionately affected by both PAD and depression yet research on the modifying effect of either race or depression on the relationship between statin use and onset of PAD is minimal. While statin utilization is highest for ages 75-84 years, there is minimal evidence of favorable risk-benefit balance. Consequently, in this project, we examined the relationship between statin use and incident PAD and whether this relationship is modified by race/ethnicity, depressive symptoms, or age. METHODS: We used data on participants from the Multi-Ethnic Study of Atherosclerosis from visit 1 (2000) through study visit 6 (2020) who had three separate measurements of the ankle-brachial index (ABI) taken at visit 1, visit 3, and visit 5. Incident PAD was defined as 1) incident lower extremity amputation or revascularization or 2) ABI less than 0.90 coupled with ABI decrease greater than 0.15 over the follow-up period. Statin use was noted on the study visit prior to incident PAD diagnosis while depressive symptoms were measured at exam 1, visit 3, and visit 5. Propensity score matching was implemented to create balance between the participants in the two treatment groups, that is, statin-treated and statin-untreated groups, to reduce the problem of confounding by indication. Propensity scores were calculated using multivariate logistic regression model to estimate the probability of receiving statin treatment. We used Cox proportional hazards regression to investigate the relationship between time-dependent statin use as well as other risk factors with incident PAD, overall and stratified by 1) race, 2) depression status, and 3) age. RESULTS: A total of 4,210 participants were included in the final matched analytic cohort. There were 810 incident cases (19.3%) of PAD that occurred over an average (mean) of 11.3 years (SD = 5.7) of follow-up time. In the statin-treated group, and with an average follow-up time of 12.5 years (SD = 5.6), there were 281 cases (13.4%) of incident PAD with the average follow-up time of 10.1 years (SD = 5.5), whereas in the statin-untreated group, there were 531 cases (25.2%) (P < 0.001). Results demonstrate a lower risk of PAD event in the statin-treated group compared to the untreated group (hazard ratio [HR] = 0.45, 95% confidence interval [CI]: 0.33-0.62) over the span of 18.5 years. The interactions between 1) depression and 2) race with statin use for incident PAD were not significant. However, other risk factors which were significant included Black American race that had approximately 30% lower hazard of PAD compared to non-Hispanic White (HR = 0.70, 95% CI: 0.58-0.84); age-stratified models were also fitted, and stain use was still a significant treatment factor for ages 45-54 (HR = 0.45, 95% CI: 0.33-0.63), 55-64 (HR = 0.61, 95% CI: 0.46-0.79), and 65-74 years (HR = 0.61, 95% CI: 0.48-0.78) but not for ages 75-84 years. CONCLUSIONS: Statin use was associated with a decreased risk of incident PAD for those under the age of 75 years. Neither race nor depression significantly modified the relationship between statin use and incident PAD; however, the risk of incident PAD was lower among Black Americans. These findings highlight that the benefit of statin may wane for those over the age of 75 years. Findings also suggest that statin use may not be compromised in those living with depression.

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.

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.

APLUS: A Python library for usefulness simulations of machine learning models in healthcare.

Despite the creation of thousands of machine learning (ML) models, the promise of improving patient care with ML remains largely unrealized. Adoption into clinical practice is lagging, in large part due to disconnects between how ML practitioners evaluate models and what is required for their successful integration into care delivery. Models are just one component of care delivery workflows whose constraints determine clinicians' abilities to act on models' outputs. However, methods to evaluate the usefulness of models in the context of their corresponding workflows are currently limited. To bridge this gap we developed APLUS, a reusable framework for quantitatively assessing via simulation the utility gained from integrating a model into a clinical workflow. We describe the APLUS simulation engine and workflow specification language, and apply it to evaluate a novel ML-based screening pathway for detecting peripheral artery disease at Stanford Health Care.

Validity of the Global Vascular Guidelines in Predicting Outcomes Based on First-Time Revascularization Strategy.

BACKGROUND: The Global Vascular Guidelines (GVG) recommend selecting an endovascular versus open-surgical approach to revascularization for chronic limb-threatening ischemia (CLTI), based on the Global Limb Anatomic Staging System (GLASS) and wound, ischemia, and foot infection (WIfI) classification systems. We assessed the utility of GVG-recommended strategies in predicting clinical outcomes. METHODS: We conducted a single-center, retrospective review of first-time lower-extremity revascularizations within a comprehensive limb-preservation program from 2010 to 2018. Procedures were stratified by (1) treatment concordance with GVG-recommended strategy (concordant versus nonconcordant groups), (2) GLASS stages I-III, and (3) endovascular versus open strategies. The primary outcome was 5-year freedom from major adverse limb events (FF-MALE), defined as freedom from reintervention or major amputation, and secondary outcomes included 5-year overall survival, freedom from major amputation, freedom from reintervention, and immediate technical failure (ITF) during initial revascularization. Kaplan-Meier (KM) survival analysis and multivariate analysis with Cox proportional hazard models were performed on the primary and secondary outcomes. RESULTS: Of 281 first-time revascularizations for CLTI, 251 (89.3%) were endovascular and 186 (66.2%) were in the concordant group, with a mean clinical follow-up of 3.02 ± 2.40 years. Within the concordant group alone, 167 (89.8%) of revascularizations were endovascular. The concordant group had a higher rate of chronic kidney disease (60.8% vs. 45.3%, P = 0.02), WIfI foot infection grade (0.81 ± 1.1 vs. 0.56 ± 0.80, P = 0.03), and WIfI stage (3.1 ± 0.79 vs. 2.8 ± 1.2, P < 0.01) compared to the non-concordant group. After both KM and multivariate analyses, there were no significant differences in 5-year FF-MALE or overall survival between concordant and non-concordant groups. There was higher freedom from major amputation in the non-concordant group on KM analysis (83.9% vs. 74.2%, P = 0.025), though this difference was non-significant on multivariate analysis (hazard ratio [HR]: 0.49, 95% confidence interval [CI]: 0.21-1.15, P = 0.10). The open group had lower MALE compared to the endovascular group (HR: 0.39, 95% CI: 0.17-0.91, P = 0.029) attributed to a lower reintervention rate in the open group (HR: 0.31, 95% CI: 0.11-0.87, P = 0.026). GLASS stage was not associated with significant differences in outcomes, but the severity of GLASS stage was associated with ITF (2.1% in stage 1, 6.4% in stage 2, and 11.7% in stage 3, P = 0.01). CONCLUSIONS: In this study, CLTI treatment outcomes did not differ significantly based on whether treatment was received in concordance with GVG-recommended strategy. There was no difference in overall survival between the endovascular and open groups, though there was a higher reintervention rate in the endovascular group. The GVG guidelines are an important resource to help guide the management of CLTI patients. However, in this study, both concordance with GVG guidelines and GLASS staging were found to be indeterminate in differentiating outcomes between complex CLTI patients treated primarily with an endovascular-first approach. The revascularization approach for a CLTI patient is a nuanced decision that must take into account patient anatomy and clinical status, as well as physician skill and experience and institutional resources.

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.

Update on workforce diversity in vascular surgery.

OBJECTIVE: Creating a diverse workforce is paramount to the success of the surgical field. A diverse workforce allows us to meet the health needs of an increasingly diverse population and to bring new ideas to spur technical innovation. The purpose of this study was to assess trends in workforce diversity within vascular surgery (VS) and general surgery (GS) as compared with orthopedic surgery (OS)-a specialty that instituted a formal diversity initiative over a decade ago. METHODS: Data on the trainee pool for VS (fellowships and integrated residencies), GS, and OS were obtained from the U.S. Graduate Medical Education reports for 1999 through 2017. Medical student demographic data were obtained from the Association of American Medical Colleges U.S. medical school enrollment reports. The representation of surgical trainee populations (female, Hispanic, and black) was normalized by their representation in medical school. We also performed the χ2 test to compare proportions of residents over dichotomized time periods (1999-2005 and 2013-2017) as well as a more sensitive trend of proportions test. RESULTS: The proportion of female trainees increased significantly between the time periods for the three surgical disciplines examined (P < .001). Hispanic trainees also represented an increasing proportion of all three disciplines (P ≤ .001). The proportion of black trainees did not significantly change in any discipline between the two periods. Relative to their proportion in medical school, Hispanic trainees were well represented in all surgical specialties studied (normalized ratio [NR], 0.95-1.52: 0.95 OS, 1.00 GS, 1.53 VS fellowship, and 1.23 VS residency). Compared with their representation in medical school, women were under-represented as surgical trainees (NR: 0.32 OS, 0.82 GS, 0.56 VS fellowship, and 0.78 VS residency) as were black trainees (NR: 0.63 OS, 0.90 GS, 0.99 VS fellowship, and 0.81 VS residency). CONCLUSIONS: Although there were significant increases in the number of women and Hispanic trainees in these three surgical disciplines, only Hispanic trainees enter the surgical field at a rate higher than their proportion in medical school. The lack of an increase in black trainees across all specialties was particularly discouraging. Women and black trainees were under-represented in all specialties as compared with their representation in medical school. The data presented suggest potential problems with recruitment at multiple levels of the pipeline. Particular attention should be paid to increasing the pool of minority medical school graduates who are both interested in and competitive for surgical specialties.

Comparison of Pre-Amputation Evaluation in Patients with and without Chronic Kidney Disease.

INTRODUCTION: Patients with chronic kidney disease (CKD) and peripheral artery disease (PAD) are more likely to undergo lower extremity amputation than patients with preserved kidney function. We sought to determine whether patients with CKD were less likely to receive pre-amputation care in the 1-year prior to lower extremity amputation compared to patients without CKD. METHODS: We conducted a retrospective observational study of patients with PAD-related lower extremity amputation between January 2014 and December 2017 using a large commercial insurance database. The primary exposure was CKD identified using billing codes and laboratory values. The primary outcomes were receipt of pre-amputation care, defined as diagnostic evaluation (ankle-brachial index, duplex ultrasound, and computed tomographic angiography), specialty care (vascular surgery, cardiology, orthopedic surgery, and podiatry), and lower extremity revascularization in the 1-year prior to amputation. We conducted separate logistic regression models to estimate the adjusted odds ratio (aOR) and 95% confidence intervals (CIs) among patients with and without CKD. We assessed for effect modification by age, sex, Black race, and diabetes status. RESULTS: We identified 8,554 patients with PAD-related amputation. In fully adjusted models, patients with CKD were more likely to receive diagnostic evaluation (aOR 1.30; 95% CI 1.17-1.44) and specialty care (aOR 1.45, 95% CI 1.27-1.64) in the 1-year prior to amputation. There was no difference in odds of revascularization by CKD status (aOR 1.03, 0.90-1.19). Age, sex, Black race, and diabetes status did not modify these associations. DISCUSSION/CONCLUSION: Patients with CKD had higher odds of receiving diagnostic testing and specialty care and similar odds of lower extremity revascularization in the 1-year prior to amputation than patients without CKD. Disparities in access to pre-amputation care do not appear to explain the higher amputation rates seen among patients with CKD.

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.

Evaluation of Cell Therapy on Exercise Performance and Limb Perfusion in Peripheral Artery Disease: The CCTRN PACE Trial (Patients With Intermittent Claudication Injected With ALDH Bright Cells).

BACKGROUND: Atherosclerotic peripheral artery disease affects 8% to 12% of Americans >65 years of age and is associated with a major decline in functional status, increased myocardial infarction and stroke rates, and increased risk of ischemic amputation. Current treatment strategies for claudication have limitations. PACE (Patients With Intermittent Claudication Injected With ALDH Bright Cells) is a National Heart, Lung, and Blood Institute-sponsored, randomized, double-blind, placebo-controlled, phase 2 exploratory clinical trial designed to assess the safety and efficacy of autologous bone marrow-derived aldehyde dehydrogenase bright (ALDHbr) cells in patients with peripheral artery disease and to explore associated claudication physiological mechanisms. METHODS: All participants, randomized 1:1 to receive ALDHbr cells or placebo, underwent bone marrow aspiration and isolation of ALDHbr cells, followed by 10 injections into the thigh and calf of the index leg. The coprimary end points were change from baseline to 6 months in peak walking time (PWT), collateral count, peak hyperemic popliteal flow, and capillary perfusion measured by magnetic resonance imaging, as well as safety. RESULTS: A total of 82 patients with claudication and infrainguinal peripheral artery disease were randomized at 9 sites, of whom 78 had analyzable data (57 male, 21 female patients; mean age, 66±9 years). The mean±SEM differences in the change over 6 months between study groups for PWT (0.9±0.8 minutes; 95% confidence interval [CI] -0.6 to 2.5; P=0.238), collateral count (0.9±0.6 arteries; 95% CI, -0.2 to 2.1; P=0.116), peak hyperemic popliteal flow (0.0±0.4 mL/s; 95% CI, -0.8 to 0.8; P=0.978), and capillary perfusion (-0.2±0.6%; 95% CI, -1.3 to 0.9; P=0.752) were not significant. In addition, there were no significant differences for the secondary end points, including quality-of-life measures. There were no adverse safety outcomes. Correlative relationships between magnetic resonance imaging measures and PWT were not significant. A post hoc exploratory analysis suggested that ALDHbr cell administration might be associated with an increase in the number of collateral arteries (1.5±0.7; 95% CI, 0.1-2.9; P=0.047) in participants with completely occluded femoral arteries. CONCLUSIONS: ALDHbr cell administration did not improve PWT or magnetic resonance outcomes, and the changes in PWT were not associated with the anatomic or physiological magnetic resonance imaging end points. Future peripheral artery disease cell therapy investigational trial design may be informed by new anatomic and perfusion insights. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01774097.

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.

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.

CCL2-mediated endothelial injury drives cardiac dysfunction in long COVID.

Evidence linking the endothelium to cardiac injury in long coronavirus disease (COVID) is well documented, but the underlying mechanisms remain unknown. Here we show that cytokines released by endothelial cells (ECs) contribute to long-COVID-associated cardiac dysfunction. Using thrombotic vascular tissues from patients with long COVID and induced pluripotent stem cell-derived ECs (iPSC-ECs), we modeled endotheliitis and observed similar dysfunction and cytokine upregulation, notably CCL2. Cardiac organoids comprising iPSC-ECs and iPSC-derived cardiomyocytes showed cardiac dysfunction after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure, driven by CCL2. Profiling of chromatin accessibility and gene expression at a single-cell resolution linked CCL2 to 'phenotype switching' and cardiac dysfunction, validated by high-throughput proteomics. Disease modeling of cardiac organoids and exposure of human ACE2 transgenic mice to SARS-CoV-2 spike proteins revealed that CCL2-induced oxidative stress promoted post-translational modification of cardiac proteins, leading to cardiac dysfunction. These findings suggest that EC-released cytokines contribute to cardiac dysfunction in long COVID, highlighting the importance of early vascular health monitoring in patients with long COVID.

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.

The potential of chatbots in chronic venous disease patient management.

Objective: Health care providers and recipients have been using artificial intelligence and its subfields, such as natural language processing and machine learning technologies, in the form of search engines to obtain medical information for some time now. Although a search engine returns a ranked list of webpages in response to a query and allows the user to obtain information from those links directly, ChatGPT has elevated the interface between humans with artificial intelligence by attempting to provide relevant information in a human-like textual conversation. This technology is being adopted rapidly and has enormous potential to impact various aspects of health care, including patient education, research, scientific writing, pre-visit/post-visit queries, documentation assistance, and more. The objective of this study is to assess whether chatbots could assist with answering patient questions and electronic health record inbox management. Methods: We devised two questionnaires: (1) administrative and non-complex medical questions (based on actual inbox questions); and (2) complex medical questions on the topic of chronic venous disease. We graded the performance of publicly available chatbots regarding their potential to assist with electronic health record inbox management. The study was graded by an internist and a vascular medicine specialist independently. Results: On administrative and non-complex medical questions, ChatGPT 4.0 performed better than ChatGPT 3.5. ChatGPT 4.0 received a grade of 1 on all the questions: 20 of 20 (100%). ChatGPT 3.5 received a grade of 1 on 14 of 20 questions (70%), grade 2 on 4 of 16 questions (20%), grade 3 on 0 questions (0%), and grade 4 on 2/20 questions (10%). On complex medical questions, ChatGPT 4.0 performed the best. ChatGPT 4.0 received a grade of 1 on 15 of 20 questions (75%), grade 2 on 2 of 20 questions (10%), grade 3 on 2 of 20 questions (10%), and grade 4 on 1 of 20 questions (5%). ChatGPT 3.5 received a grade of 1 on 9 of 20 questions (45%), grade 2 on 4 of 20 questions (20%), grade 3 on 4 of 20 questions (20%), and grade 4 on 3 of 20 questions (15%). Clinical Camel received a grade of 1 on 0 of 20 questions (0%), grade 2 on 5 of 20 questions (25%), grade 3 on 5 of 20 questions (25%), and grade 4 on 10 of 20 questions (50%). Conclusions: Based on our interactions with ChatGPT regarding the topic of chronic venous disease, it is plausible that in the future, this technology may be used to assist with electronic health record inbox management and offload medical staff. However, for this technology to receive regulatory approval to be used for that purpose, it will require extensive supervised training by subject experts, have guardrails to prevent "hallucinations" and maintain confidentiality, and prove that it can perform at a level comparable to (if not better than) humans. (JVS-Vascular Insights 2023;1:100019.).

Artificial intelligence in clinical workflow processes in vascular surgery and beyond.

In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.