Risk prediction model for early outcomes of revascularization for chronic limb-threatening ischaemia

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning

BACKGROUND

Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization.

METHODS AND RESULTS

The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure.

CONCLUSIONS

Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease

OBJECTIVE

To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass.

BACKGROUND

Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited.

METHODS

The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores.

RESULTS

Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative).

CONCLUSIONS

ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

2020 JAPAN Critical Limb Ischemia Database (JCLIMB) Annual Report

Since 2013, the Japanese Society for Vascular Surgery has started the project of nationwide registration and tracking database for patients with critical limb ischemia (CLI) who are treated by vascular surgeons. The purpose of this project is to clarify the current status of the medical practice for the patients with CLI to contribute to the improvement of the quality of medical care. This database, called JAPAN Critical Limb Ischemia Database (JCLIMB), is created on the National Clinical Database and collects data of patients' background, therapeutic measures, early results, and long-term prognosis as long as 5 years after the initial treatment. The limbs managed conservatively are also registered in JCLIMB, together with those treated by surgery and/or endovascular treatment. In 2020, 1299 CLI limbs (male 890 limbs: 69%) were registered by 85 facilities. Arteriosclerosis obliterans has accounted for 99% of the pathogenesis of these limbs. In this manuscript, the background data and the early prognosis of the registered limbs are reported. (This is a translation of Jpn J Vasc Surg 2023; 32: 363-391.).

Development and validation of a major adverse limb events prediction model for peripheral arterial disease with frailty

OBJECTIVE

To investigate the risk factors for major limb adverse events (MALE) in peripheral arterial disease (PAD) combined with frailty and to develop and validate a risk prediction model of MALE.

METHODS

This prospective study was performed in the vascular surgery department of patients in six hospitals in southwest China. Prospective collection of patients with PAD combined with frailty from February 1 to December 20, 2021, with MALE as the primary outcome, and followed for 1 year. The cohort was divided into a development cohort and a validation cohort. In the development cohort, a multivariate risk prediction model was developed to predict MALE using random forests for variable selection and multivariable Cox regression analysis. The model is represented by a visualized nomogram and a web-based calculator. The model performance was tested with the validation cohort and assessed using the C-statistic and calibration plots.

RESULTS

A total of 1179 patients were prospectively enrolled from February 1 to December 20, 2021. Among 816 patients with PAD who were included in the analysis, the median follow-up period for this study was 9 ± 4.07 months, the mean age was 74.64 ± 9.43 years, and 249 (30.5%) were women. Within 1 year, 222 patients (27.2%) developed MALE. Target lesion revascularizations were performed in 99 patients (12.1%), and amputations were performed in 131 patients (16.1%). The mortality rate within the whole cohort was 108 patients (13.2%). After controlling for competing risk events (death), the cumulative risk of developing MALE was not statistically different. Prealbumin (hazard ratio [HR], 0.6; 95% confidence interval [CI], 0.41-0.89; P = .010), percutaneous coronary intervention (HR, 2.31; 95% CI, 1.26-4.21; P = .006), Rutherford classification (HR, 1.77; 95% CI, 1.36-2.31; P < .001), white blood cell (HR, 1.85; 95% CI, 1.20-2.87; P = .005), high altitude area (HR, 3.1; 95% CI, 1.43-6.75; P = .004), endovascular treatment (HR, 10.2; 95% CI, 1.44-72.5; P = .020), and length of stay (HR, 1.01; 95% CI, 1.00-1.03; P = .012) were risk factors for MALE. The MALE prediction model had a C-statistic of 0.76 (95% CI, 0.70-0.79). The C-statistic was 0.68 for internal validation and 0.66 for external validation for the MALE prediction model. The MALE prediction model for PAD presented an interactive nomogram and a web-based network calculator.

CONCLUSIONS

In this study, the MALE prediction model has a discriminative ability to predict MALE among patients with PAD in frailty. The MALE model can optimize clinical decision-making for patients in PAD with frailty.

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

Importance

Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited.

Objective

To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD.

Design, Setting, and Participants

This 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.

Exposures

A 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 Measures

Using 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.

Results

Overall, 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 Relevance

In 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.

Predicting outcomes following lower extremity open revascularization using machine learning

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Peak Aortic Valve Jet Velocity is an Independent Predictor of Mortality of Dialysis Patients Undergoing Open Surgery for Chronic Limb Threatening Ischemia

BACKGROUND

To investigate the impact of peak aortic jet velocity (Vmax) on the prognosis of patients undergoing open surgery for chronic limb threatening ischemia (CLTI).

METHODS

Between April 2015 and March 2022, 352 patients underwent infrainguinal open surgery for CLTI. Patients who met the following exclusion criteria were excluded: subsequent infrainguinal surgeries in the registered period, no record of Vmax, history of aortic valve intervention, and Vmax ≥3.0 m/s (moderate or severe aortic valve stenosis). The remaining patients were dichotomized into 2 groups based on their Vmax values. The Youden index calculated from the receiver operating characteristic curve (ROC) was set as the cutoff value. The 2-year overall survival (OS), calculated using the Kaplan-Meier's method, was compared between the 2 groups. A Cox proportional hazards regression analysis was performed using perioperative factors including Vmax to identify independent predictors separately for dialysis and nondialysis patients and the quantitative relationship between Vmax and OS.

RESULTS

One hundred and ninety-one patients, including 100 dialysis and 91 nondialysis patients, were included in the analysis. The Youden index was 1.7 m/s. The 2-year OS rates of the group with Vmax >1.7 m/s and with Vmax ≤1.7 m/s were 49% and 76% (P = 0.007), respectively, in the dialysis cohort, while they were 71% and 78% (P = 0.680) in the nondialysis cohort, respectively. Multivariate analysis identified Vmax and ejection fraction as independent predictors in the dialysis cohort and the Barthel Index at admission in the nondialysis cohort. There was a stepwise increase in the risk of death in patients with Vmax of ≥1.5 m/s and a significantly higher risk of death in dialysis patients with Vmax >2.5 m/s.

CONCLUSIONS

Vmax was a significant independent predictor of all-cause death within 2 years after open surgery for CLTI in dialysis patients but not in patients managed without dialysis.

Adherence to vascular care guidelines for emergency revascularization of chronic limb-threatening ischemia

Objective

In 2022, the National Health Service Commissioning for Quality and Innovation (CQUIN) indicator for vascular surgery, with its pay-for-performance incentive for timely (5-day) revascularization of chronic limb-threatening ischemia (CLTI), was introduced. We sought to assess its effects in terms of (1) changes in the care pathway process measures relating to timing and patient outcomes; and (2) adherence to the Peripheral Arterial Disease Quality Improvement Framework (PAD-QIF) guidelines for patients admitted with CLTI.

Methods

A retrospective before-and-after cohort study was performed from January to June 2022 of nonelective admissions for CLTI who underwent revascularization (open, endovascular, or hybrid) at Cambridge University Hospitals National Health Service Foundation Trust, a regional vascular "hub." The diagnostic and treatment pathway timing-related process measures recommended in the PAD-QIF were compared between two 3-month cohorts-before vs after introduction of the CQUIN.

Results

For the two cohorts (before vs after CQUIN), 17 of 223 and 17 of 219 total admissions met the inclusion criteria, respectively. After introduction of financial incentives, the percentage of patients meeting the 5-day targets for revascularization increased from 41.2% to 58.8% (P = .049). Improvements were also realized in the attainment of PAD-QIF targets for a referral-to-admission time of ≤2 days (from 82.4% to 88.8%; P = .525) and admission-to-specialist-review time of ≤14 hours (from 58.8% to 76.5%; P = .139). An increase also occurred in the percentage of patients receiving imaging studies within 2 days of referral (from 58.8% to 70.6%; P = .324). The reasons for delay included operating list pressures and unsuitability for intervention (eg, active COVID-19 [coronavirus disease 2019] infection). No statistically significant changes to patient outcomes were observed between the two cohorts in terms of complications (pre-CQUIN, 23.5%; post-CQUIN, 41.2%; P = .086), length of stay (pre-QUIN, 12.0 ± 12.0 days; post-QUIN, 15.0 ± 21.0 days; P = .178), and in-hospital mortality (pre-QUIN, 0%; post-QUIN, 5.9%). Other PAD-QIF targets relating to delivery of care were poorly documented for both cohorts. These included documented staging of limb threat severity with the WIfI (wound, ischemia, foot infection) score (2.9% of patients; target >80%), documented shared decision-making (47.1%; target >80%), documented issuance of written information to patient (5.9%; target 100%), and geriatric assessment (6.3%; target >80%).

Conclusions

The pay-for-performance incentive CQUIN indicators appear to have raised the profile for the need for early revascularization to treat CLTI, engaging senior hospital management, and reducing the time to revascularization in our cohort. Further data collection is required to detect any resultant changes in patient outcomes. Documentation of guideline targets for delivery of care was often poor and should be improved.

Revascularisation of Chronic Limb Threatening Ischaemia in Patients with no Pedal Arteries Leads to Lower Midterm Limb Salvage

OBJECTIVE

Chronic limb threatening ischaemia (CLTI) involving the infragenicular arteries is treated by distal angioplasty or pedal bypass; however, this is not always possible, due to chronically occluded pedal arteries (no patent pedal artery, N-PPA). This pattern represents a hurdle to successful revascularisation, which must be limited to the proximal arteries. The aim of the study was to analyse the outcome of patients with CLTI and N-PPA after a proximal revascularisation.

METHODS

All patients with CLTI submitted to revascularisation in a single centre (2019 - 2020) were analysed. All angiograms were reviewed to identify N-PPA, defined as total obstruction of all pedal arteries. Revascularisation was performed with proximal surgical, endovascular, and hybrid procedures. Early and midterm survival, wound healing, limb salvage, and patency rates were compared between N-PPA and patients with one or more patent pedal artery (PPA).

RESULTS

Two hundred and eighteen procedures were performed. One hundred and forty of 218 (64.2%) patients were male, mean age 73.2 ± 10.6 years. The procedure was surgical in 64/218 (29.4%) cases, endovascular in 138/218 (63.3%), and hybrid in 16/218 (7.3%). N-PPA was present in 60/218 (27.5%) cases. Eleven of 60 (18.3%) cases were treated surgically, 43/60 (71.7%) by endovascular and 6/60 (10%) by hybrid procedures. Technical success was similar in the two groups (N-PPA 85% vs. PPA 82.3%, p = .42). At a mean follow up of 24.5 ± 10.2 months, survival (N-PPA 93.7 ± 3.5% vs. PPA 95.3 ± 2.1%, p = .22) and primary patency (N-PPA 53.1 ± 8.1% vs. PPA 55.2 ± 5%, p = .56) were similar. Limb salvage was significantly lower in N-PPA patients (N-PPA 71.4 ± 6.6% vs. PPA 81.5 ± 3.4%, p = .042); N-PPA was an independent predictor of major amputation (hazard ratio [HR] 2.02, 1.07 - 3.82, p = .038) together with age > 73 years (HR 2.32, 1.17 - 4.57, p = .012) and haemodialysis (2.84, 1.48 - 5.43, p = .002).

CONCLUSION

N-PPA is not uncommon in patients with CLTI. This condition does not hamper technical success, primary patency, and midterm survival; however, midterm limb salvage is significantly lower than in patients with PPA. This should be considered in the decision making process.

Prediction Models for Two Year Overall Survival and Amputation Free Survival After Revascularisation for Chronic Limb Threatening Ischaemia

OBJECTIVE

The aim of this study was to create prediction models for two year overall survival (OS) and amputation free survival (AFS) after revascularisation in patients with chronic limb threatening ischaemia (CLTI).

METHODS

This was a retrospective analysis of prospectively collected multicentre registry data (JAPAN Critical Limb Ischaemia Database; JCLIMB). Data from 3 505 unique patients with CLTI who had undergone revascularisation from 2013 to 2017 were extracted from the JCLIMB for the analysis. The cohort was randomly divided into development (2 861 patients) and validation cohorts (644 patients). In the development cohort, multivariable risk models were constructed to predict two year OS and AFS using Cox proportional hazard regression analysis. These models were applied to the validation cohort and their performances were evaluated using Harrell's C index and calibration plots.

RESULTS

Kaplan-Meier estimates of two year OS and AFS post-revascularisation in the whole cohort were 69% and 62%, respectively. Strong predictors for OS consisted of age, activity, malignant neoplasm, chronic kidney disease (CKD), congestive heart failure (CHF), geriatric nutritional risk index (GNRI), and sex. Strong predictors for AFS included age, activity, malignant neoplasm, CKD, CHF, GNRI, body temperature, white blood cells, urgent revascularisation procedure, and sex. Prediction models for two year OS and AFS showed good discrimination with Harrell's C indexes of 0.73 (95% confidence interval [CI] 0.69 - 0.77) and 0.72 (95% CI 0.68 - 0.76), respectively CONCLUSION: Prediction models for two year OS and AFS post-revascularisation in patients with CLTI were created. They can assist in determining treatment strategies and serve as risk adjustment modalities for quality benchmarking for revascularisation in patients with CLTI at each facility.

2019 JAPAN Critical Limb Ischemia Database (JCLIMB) Annual Report

Since 2013, the Japanese Society for Vascular Surgery has started the project of nationwide registration and tracking database for patients with critical limb ischemia (CLI) who are treated by vascular surgeons. The objective of this project is to elucidate the current status of the medical practice for CLI patients to contribute to the improvement of the quality of medical care. This database, called JAPAN Critical Limb Ischemia Database (JCLIMB), is created on the National Clinical Database (NCD) and collects data of patients’ background, therapeutic measures, early results, and long-term prognosis as long as 5 years after the initial treatment. The limbs managed conservatively are also registered in JCLIMB, together with those treated with surgery and/or endovascular treatment (EVT). In 2019, 1070 CLI limbs (male: 725 limbs, 68%) were registered by 83 facilities. Arteriosclerosis obliterans (ASO) accounted for 98% of the pathogenesis of these limbs. In this manuscript, the background data and the early prognosis of the registered limbs are reported. Although the registration format for the simultaneous surgery of bilateral limbs in NCD was changed to one patient and two limbs, JCLIMB still counted two patients and two limbs to eliminate discrepancy with the past annual reports. (This is a translation of Jpn J Vasc Surg 2022; 31: 157–185.)

Congestive Heart Failure Is Associated With Worse Outcomes in Patients With Ischemic Colitis: A Nationwide Study

INTRODUCTION

Ischemic colitis (IC) results from compromised blood flow to the colon. Risk factors include atrial fibrillation (A.Fib), peripheral artery disease (PAD), coronary artery disease (CAD), and congestive heart failure (CHF). However, few studies compared the mortality rate and colectomy between patients with IC with CHF and IC alone.

OBJECTIVE

We aim to investigate the possibility of worse outcomes in patients with IC and CHF compared to IC alone.

METHODOLOGY

Using the National Inpatient Sample database from 2016 to 2019, we obtained baseline demographic data, total hospital charge, rate of colectomy, length of hospital stay (LOS), and in-hospital mortality. Data were compared using a t-test and chi-squared. Odds ratios for comorbidities including A.Fib, CAD, PAD, end-stage renal disease, chronic obstructive pulmonary disease, hyperlipidemia, hypertension, diabetes, and cirrhosis were calculated.

RESULTS

106,705 patients with IC were identified, among which 15,220 patients also had CHF. IC patients with CHF had a longer LOS (6.6 days vs 4.4 days; P<0.0001), higher total hospital charge ($71,359 vs $45,176; P<0.0001), higher mortality rate (8.5% vs 2.9%; P<0.0001), and higher colectomy rate (9.2% vs 5.9%; P<0.0001).

CONCLUSION

CHF is associated with poor outcomes in patients with IC. Our study showed an increased risk of mortality and colectomy compared to patients with IC alone. The findings suggest it may be warranted to have a heightened clinical suspicion of IC in patients with CHF who present with bleeding per rectum.

MagicTouch PTA Sirolimus Coated Balloon for Femoropopliteal and Below the Knee Disease: Results From XTOSI Pilot Study Up To 12 Months

INTRODUCTION

Sirolimus coated balloon (SCB) is a promising treatment option to prevent restenosis for peripheral arterial occlusive disease (PAOD). This is a pilot first-in-human study of MagicTouch percutaneous transluminal angioplasty (PTA) SCB for treatment of PAOD for both femoropopliteal and below the knee arteries (BTK).

MATERIAL AND METHODS

Xtreme Touch-Neo [MagicTouch PTA] Sirolimus Coated Balloon (XTOSI) pilot study is a prospective, single-arm, open-label, single-center trial evaluating MagicTouch PTA SCB for symptomatic PAOD. Primary endpoint was defined as primary patency at 6 months (duplex ultrasound peak systolic velocity ratio ≤2.4). Secondary endpoints included clinically driven target lesion revascularization (CD-TLR), amputation free survival (AFS), all-cause mortality, and limb salvage success.

RESULTS

Fifty patients were recruited. The mean age was 67 (n=31 [62%] males). SCB was applied to femoropopliteal in 20 patients (40%) and BTK in 30 patients (60%). Majority of treatments (94%) were performed for limb salvage indications (Rutherford scores 5 or 6). This was a high risk cohort, in which 90% had diabetes, 36% had coronary artery disease, 20% had end stage renal failure, and American Society of Anaesthesiologists (ASA) score was 3 or more in 80%. Mean lesion length treated was 227±81 mm, of which 36% were total occlusions. Technical and device success were both 100%. At 30 days, mortality was 2% and major limb amputation was also 2%. Six-month primary patency was 80% (88.2% for femoropopliteal; 74% for BTK). At 12 months, freedom from CD-TLR was 89.7% (94.1% for femoropopliteal; 86.3% for BTK), AFS was 81.6% (90.0% for femoropopliteal; 75.9% for BTK), all-cause mortality was 14.3% (10.0% for femoropopliteal; 17.2% for BTK), and limb salvage success was 92.9% (94.4% for femoropopliteal; 91.7% for BTK). There was a statistically significant increase between baseline and 6-month toe pressures for both femoropopliteal (57.3±23.3 mm Hg vs 82.5±37.8 mm Hg; p<.001) and BTK lesions (52.8±19.2 mm Hg vs 70.7±37 mm Hg; p<.037). At 12 months, wound healing rate was 33/39 (84.6%).

CONCLUSIONS

MagicTouch PTA SCB in the XTOSI study showed promising 6-month primary patency and encouraging 12-month freedom from CD-TLR, AFS, and limb salvage rates. No early safety concerns were raised. Randomized trials are needed to investigate the safety and efficacy of SCB for treatment of PAOD.