Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS. METHODS AND RESULTS: The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). CONCLUSIONS: Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Management of renal artery aneurysms: A retrospective study.

BACKGROUND: Although renal artery aneurysms (RAAs) are rare and often asymptomatic with slow growth, their natural progression and optimal management are not well understood. Treatment recommendations for RAAs do exist; however, they are supported by limited data. METHODS: A retrospective cohort study was conducted to explore the management of patients diagnosed with an RAA at our institution from January 1st, 2013, to December 31st, 2020. Patients were identified through a search of our radiological database, followed by a comprehensive chart review for further assessment. Data collection encompassed patient and aneurysm characteristics, the rationale for initial imaging, treatment, surveillance, and all-cause mortality. RESULTS: One hundred eighty-five patients were diagnosed with or treated for RAAs at our center during this timeframe, with most aneurysms having been discovered incidentally. Average aneurysm size was 1.40 cm (±0.05). Of those treated, the mean size was 2.38 cm (±0.24). Among aneurysms larger than 3 cm in size, comprising 3.24% of the total cases, 83.3% underwent treatment procedures. Only 20% of women of childbearing age received treatment for their aneurysms. There was one instance of aneurysm rupture, with no associated mortality or significant morbidity. CONCLUSIONS: Our institution's management of RAAs over the period of the study generally aligned with guidelines. One potential area of improvement is more proactive intervention for women of childbearing age.

Predicting inferior vena cava filter complications using machine learning.

OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement. RESULTS: Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups. CONCLUSIONS: We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes.

Following the COMPASS: Antithrombotic Prescription Trends in Patients with Symptomatic Peripheral Arterial Disease following Revascularization.

BACKGROUND: The Vascular Outcomes Study of aspirin (ASA) Along with Rivaroxaban in Endovascular or Surgical Limb Revascularization for peripheral artery disease (PAD) trial demonstrated the superiority of ASA and low-dose rivaroxaban (Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial dosing) compared with ASA alone in reducing major adverse cardiovascular events and major adverse limb events. We studied the COMPASS discharge prescription patterns in patients with symptomatic PAD who have undergone revascularization in our institution, since the time of publication of the Vascular Outcomes Study of ASA Along with Rivaroxaban in Endovascular or Surgical Limb Revascularization for PAD trial. METHODS: All patients included in this study had documented lower-extremity atherosclerotic PAD and were eligible for COMPASS dosing. Revascularization strategies included endovascular (n = 299), suprainguinal bypass (n = 18), and infrainguinal bypass (n = 36). RESULTS: COMPASS prescription patterns for the composite of endovascular and surgical strategies demonstrated a consistently low rate over time, without a trend toward increasing use. COMPASS dosing was prescribed as often as antiplatelet monotherapy (33.4% COMPASS vs. 34.6% antiplatelet monotherapy). This low COMPASS prescription rate was driven by significantly lower COMPASS prescriptions following endovascular therapy compared to surgical bypass (28.8% endovascular vs. 59.3% surgical bypass). COMPASS prescriptions following surgical bypass showed better trends; half of suprainguinal bypass patients (50.0%) and two-thirds of infrainguinal bypass patients (63.9%) were discharged on COMPASS. Despite patients with chronic limb-threatening ischemia (CLTI) representing a high-risk limb presentation, COMPASS prescriptions were low (29.8%), as opposed to patients without CLTI, and did not show a trend toward increasing use. In patients who underwent reinterventions throughout the observation period, there was a low conversion rate from ASA alone to COMPASS (3/26, 11.5%). CONCLUSIONS: In this observational study, one-third of patients were undertreated by prescription of antiplatelet monotherapy, indicating that there is significant room for medical optimization. This is especially true of patients undergoing endovascular treatment, including the high-risk subgroup of patients with CLTI. We highlight the importance of dual pathway antithrombotic therapy in patients eligible for COMPASS dosing to optimize best current evidence medical therapy.

Age is not a sole predictor of outcomes in octogenarians undergoing complex endovascular aortic repair.

OBJECTIVE: To examine the perioperative, postoperative, and long-term outcomes of fenestrated/branched endovascular aneurysm repair (F/BEVAR) in octogenarians compared with nonoctogenarians. METHODS: A multicenter, retrospective cohort study was conducted using the Vascular Quality Improvement database, which prospectively captures information on patients who undergo vascular surgery across 1021 academic and community hospitals in North America. All patients who underwent F/BEVAR endovascular aortic repair from 2012 to 2022 were included. Patients were stratified into two groups: those aged <80 years and those aged ≥80 years at the time of the procedure. The preoperative, intraoperative, and postoperative factors were compared between the two groups. The primary outcome was long-term all-cause mortality; secondary outcomes included aortic-specific mortality and aortic-specific reintervention. RESULTS: A total of 6007 patients (aged <80 years, n = 4860; aged ≥80 years, n = 1147) who had undergone F/BEVAR procedures were included. No significant difference was found in technical success, postoperative length of stay, length of intensive care unit stay, postoperative bowel ischemia, and spinal cord ischemia. After adjustment for baseline covariates, octogenarians were more likely to suffer from a postoperative complication (odds ratio [OR]: 1.16; [95% confidence interval (CI): 0.98-1.37], P < .001) and be discharged to a rehabilitation center (OR: 1.60; [95% CI: 1.27-2.00], P < .001) or nursing home (OR: 2.23; [95% CI: 1.64-3.01], P < .001). Five-year survival was lower in octogenarians (83% vs 71%, hazard ratio [HR]: 1.70; [95% CI: 1.46-2.0], P < .0001). Multivariate Cox proportional hazard analysis demonstrated that age was associated with increased all-cause mortality (HR: 1.72, [95% CI: 1.39-2.12], P < .001) and aortic-specific mortality (HR: 1.92, [95% CI: 1.04-3.68], P = .038). Crawford extent II aortic disease was associated with an increase in all-cause mortality (HR 1.49; [95% CI: 1.01-2.19], P < .001), aortic-specific mortality (HR: 5.05; [95% CI: 1.35-18.9], P = .016), and aortic-specific reintervention (HR: 1.91; [95% CI: 1.24-2.93], P = .003). Functional dependence was associated with increased all-cause mortality (HR: 2.90; [95% CI: 1.87-4.51], P < .001) and aortic-specific mortality (HR: 4.93; [95% CI: 1.69-14.4], P = .004). CONCLUSIONS: Our findings suggest that octogenarians do have a mildly increased mortality rate and rate of adverse events after F/BEVAR procedures. Despite this, when adjusted for other risk factors, age is on par with other medical comorbidities and therefore should not be a strict exclusion criterion for F/BEVAR procedures, rather considered in the global context of patient's aortic anatomy, health, and functional status.

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.

Using machine learning to predict outcomes following suprainguinal bypass.

OBJECTIVE: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS: Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.

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.

Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair.

BACKGROUND: Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023. Input features included 47 preoperative demographic/clinical variables. The primary outcome was 1-year all-cause mortality. Data were split into training (70 per cent) and test (30 per cent) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features with logistic regression as the baseline comparator. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. RESULTS: Some 63 655 patients were included. One-year mortality occurred in 3122 (4.9 per cent) patients. The best performing prediction model for 1-year mortality was XGBoost, achieving an AUROC (95 per cent c.i.) of 0.96 (0.95-0.97). Comparatively, logistic regression had an AUROC (95 per cent c.i.) of 0.69 (0.68-0.71). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.04. The top 3 predictive features in the algorithm were 1) unfit for open AAA repair, 2) functional status, and 3) preoperative dialysis. CONCLUSIONS: In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.