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.

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.

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.

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.

Using machine learning to predict outcomes following open abdominal aortic aneurysm repair.

OBJECTIVE: Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair. METHODS: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023. Input features included 52 preoperative demographic/clinical variables. All available preoperative variables from VQI were used to maximize predictive performance. The primary outcome was in-hospital major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). Secondary outcomes were individual components of the primary outcome, other in-hospital complications, and 1-year mortality and any reintervention. We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, six ML models were trained 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). Model robustness was evaluated with calibration plot and Brier score. The top 10 predictive features in our final model were determined based on variable importance scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median area deprivation index, proximal clamp site, prior aortic surgery, and concomitant procedures. RESULTS: Overall, 12,027 patients were included. The primary outcome of in-hospital MACE occurred in 630 patients (5.2%). Compared with patients without a primary outcome, those who developed in-hospital MACE were older with more comorbidities, demonstrated poorer functional status, had more complex aneurysms, and were more likely to require concomitant procedures. Our best performing prediction model for in-hospital MACE was XGBoost, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). Comparatively, logistic regression had an AUROC of 0.71 (95% confidence interval, 0.70-0.73). For secondary outcomes, XGBoost achieved AUROCs between 0.84 and 0.94. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. These findings highlight the excellent predictive performance of the XGBoost model. The top three predictive features in our algorithm for in-hospital MACE following open AAA repair were: (1) coronary artery disease; (2) American Society of Anesthesiologists classification; and (3) proximal clamp site. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Open AAA repair outcomes can be accurately predicted using preoperative data with our ML models, which perform better than logistic regression. Our automated algorithms can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.

Using machine learning to predict outcomes following carotid endarterectomy.

OBJECTIVE: Prediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA. METHODS: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022. We identified 71 potential predictor variables (features) from the index hospitalization (43 preoperative [demographic/clinical], 21 intraoperative [procedural], and 7 postoperative [in-hospital complications]). The primary outcome was stroke or death at 1 year following CEA. 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). After selecting the best performing algorithm, additional models were built 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, insurance status, symptom status, and urgency of surgery. RESULTS: Overall, 166,369 patients underwent CEA during the study period. In total, 7749 patients (4.7%) had the primary outcome of stroke or death at 1 year. Patients with an outcome were older with more comorbidities, had poorer functional status, and demonstrated higher risk anatomic features. They were also more likely to undergo intraoperative surgical re-exploration and have in-hospital complications. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.90 (95% confidence interval [CI], 0.89-0.91). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67), and existing tools in the literature demonstrate AUROCs ranging from 0.58 to 0.74. Our XGBoost models maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.90 (95% CI, 0.89-0.91) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.15 (preoperative), 0.14 (intraoperative), and 0.11 (postoperative). Of the top 10 predictors, eight were preoperative features, including comorbidities, functional status, and previous procedures. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following CEA. Our algorithms perform better than logistic regression and existing tools, and therefore, have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes.

The Impact of Heparin on Mortality Following Open Ruptured Abdominal Aortic Aneurysm Repair.

BACKGROUND: Systemic administration of heparin is widely used in patients undergoing open elective abdominal aortic aneurysm (AAA) repair. However, no clear consensus exists in the use of intraoperative heparin during open ruptured AAA (rAAA) repair. In this study, we assessed the safety of intravenous heparin administration in patients undergoing open rAAA repair. METHODS: A retrospective cohort study comparing patients who received and did not receive heparin during open rAAA repair in the Vascular Quality Initiative database between 2003 and 2020 was conducted. The primary outcomes were 30-day and 10-year mortality. The secondary outcomes included estimated blood loss, number of packed red blood cells transfused, early postoperative transfusions, and postsurgical complications. Propensity score matching was used to adjust for potentially confounding variables. The outcomes were compared between the 2 groups using relative risk for binary outcomes and paired t-test and the Wilcoxon rank-sum test for normally and non-normally distributed continuous variables, respectively. Survival was examined using Kaplan-Meier curves and compared using a Cox proportional hazards model. RESULTS: A total of 2,410 patients who underwent open rAAA repair between 2003 and 2020 were studied. Of the 2,410 patients, 1,853 patients received intraoperative heparin and 557 did not. Propensity score matching on 25 variables yielded 519 pairs for the heparin to no heparin comparison. Thirty-day mortality was lower in the heparin group (risk ratio: 0.74; 95% confidence interval [CI]: 0.66-0.84) and in-hospital was also lower in the heparin group (risk ratio: 0.68; 95% CI: 0.60-0.77). Furthermore, estimated blood loss was 910 mL (95% CI: 230 mL to 1,590 mL) lower in the heparin group and the mean number of packed red blood cells transfused intraoperatively and postoperatively were 17 units lower in the heparin group (95% CI: 8-42). Ten-year survival was higher for patients who received heparin, and their rate of survival was approximately 40% higher than those who did not receive heparin (hazard ratio: 0.62; 95% CI, 0.53-0.72; P < 0.0001). CONCLUSIONS: In patients who received systemic heparin administration at the time of open rAAA repair, there were significant short-term and long-term survival benefits within 30 days and at 10 years. Heparin administration may have afforded a mortality benefit or been a surrogate for healthier and less moribund patients at the time of the procedure.

The impact of race on outcomes following ruptured abdominal aortic aneurysm repair.

OBJECTIVES: Racial differences in elective abdominal aortic aneurysm (AAA) repair outcomes have been previously reported; however, data on racial differences in ruptured AAA (rAAA) repair outcomes remain limited. This study assessed in-hospital and long-term mortality after rAAA repair in Black versus White patients. METHODS: The Vascular Quality Initiative database was queried to identify all Black and White patients who underwent open or endovascular rAAA repair between 2003 and 2019. Baseline demographic and clinical characteristics were recorded, and independent t test and χ2 test were performed to assess differences between groups. In-hospital and 8-year mortality rates were the primary outcomes. Univariate and multivariate logistic regression and Cox proportional hazards analyses were conducted to analyze associations between race and outcomes. RESULTS: Overall, 310 Black patients and 4679 White patients underwent rAAA repair. A greater proportion of Black patients underwent endovascular repair (73.2% vs 56.1%). Black patients had a lower mean age and were more likely to be female, with a greater proportion being Medicaid insured (9.7% vs 2.1%) or uninsured (4.8% vs 3.3%). Although Black patients were more likely to be current smokers and have hypertension, diabetes, and congestive heart failure, they were not more likely to receive risk reduction medications. The time from symptom onset to incision or access was higher for Black patients (median, 12.0 hours vs 7.0 hours). Similarly, the time from hospital admission to intervention was higher for Black patients (median, 2.8 hours vs 1.3 hours). In-hospital mortality was lower in Black patients (20.0% vs 28.6%; odds ratio [OR], 0.63; 95% confidence interval [CI], 0.47-0.83); however, this did not persist after adjusting for baseline characteristics (adjusted OR, 0.58; 95% CI, 0.30-1.07; P = .09). Furthermore, the 8-year survival was not different between groups (50.4% vs 46.6%; hazard ratio, 0.85; 95% CI, 0.57-1.26; P = .42), even when stratified by repair type. CONCLUSIONS: This study identified racial differences in demographic, clinical, and procedural characteristics for patients undergoing rAAA repair. In particular, the door-to-intervention time for Black patients of 2.8 hours does not meet the Society for Vascular Surgery recommendation of 90 minutes. Despite these differences, the 8-year mortality is similar for Black and White patients. These differences should be investigated further, and there are opportunities to improve rAAA care for Black patients.

Vascular surgical management of profunda femoris artery aneurysms: A single center experience.

OBJECTIVE: Profunda femoris artery aneurysms (PFAAs), which comprise true profunda femoris artery aneurysms (TPFAAs) and profunda femoris artery pseudoaneuryms (PFA PSAs), are rare but clinically significant diseases of the peripheral arterial vasculature. Our aim is to describe our institution's 15-year experience with PFAAs (TPFAAs and PFA PSAs) to provide insight into patient characteristics, diagnostic imaging modalities, and surgical interventions that contribute to clinically important outcomes in patients with PFAAs. METHODS: We conducted a retrospective study at our institution using our radiology database. RESULTS: We identified six patients with PFA PSAs and four patients with TPFAAs. The clinical presentation of PFA PSAs included a triad of thigh pain, bleeding, and unexplained anemia. There was variety in the aetiologies of PFA PSAs, arising from catheterizations, upper thigh fractures, anastomotic complications, or unknown causes. Most patients with PFA PSAs had hypertension and coronary artery disease, and half of our cohort had peripheral vascular disease. All patients were imaged with duplex ultrasonography (DUS) or computed tomography (CT), the latter being more accurate. All patients with PFA PSAs underwent endovascular treatment, including glue, thrombin, or coil embolization as well as stent-graft insertions. All TPFAAs presented to our center were small and incidentally discovered, explaining the conservative management of our TPFAAs. Two of the four TPFAAs were idiopathic in nature, while one was attributed to post-stenotic dilatation, and another was found in a patient with Ehlers Danlos Syndrome. There was an association between TPFAAs and multiple synchronous or asynchronous aneurysms. CONCLUSION: Pseudoaneurysms of the PFA are mostly iatrogenic in nature and can present with the triad of thigh swelling, bleeding, and unexplained anemia. If the clinical picture is suggestive of a PFA PSA but DUS does not detect a pseudoaneurysm, CT may be added as a more accurate imaging modality. Endovascular embolization is used in smaller pseudoaneurysms and in poor surgical candidates. Multiple glue, coil, or thrombin injections may be required to fully thrombose the pseudoaneurysm sac. True aneurysms of the PFA are associated with synchronous/asynchronous aneurysms and small TPFAAs should be carefully monitored, as there is a risk of enlargement and rupture.

The Impact of Sex on Outcomes following Carotid Endarterectomy.

BACKGROUND: Previous studies have demonstrated significant sex differences in vascular surgery outcomes. We assessed stroke or death rates following carotid endarterectomy (CEA) in women versus men. METHODS: The Vascular Quality Initiative was used to identify all patients who underwent CEA between 2010 and 2019. Demographic, clinical, and procedural characteristics were recorded and differences between women and men were assessed using independent t-test and chi-squared test. The primary outcomes were 30-day and 1-year stroke or death. Associations between sex and outcomes were assessed using univariate/multivariate logistic regression and Cox proportional hazards analysis. RESULTS: Overall, 52,137 women and 79,974 men underwent CEA in Vascular Quality Initiative sites during the study period. Women were younger (70.3 vs. 70.5 years, P < 0.001) and more likely to have hypertension (89.2% vs. 88.9%, P < 0.05) and diabetes (36.2% vs. 35.8%, P < 0.001) but less likely to be diagnosed with coronary artery disease (23.2% vs. 31.0%, P < 0.001). A greater proportion of men were receiving cardiovascular risk reduction medications and had symptomatic carotid stenosis (28.5% vs. 26.7%, P < 0.001). Women had shorter procedure times (113 vs. 122 min, P < 0.001) and were less likely to receive electroencephalography neuromonitoring (27.9% vs. 28.8%, P < 0.001), drain (35.9% vs. 37.3%, P < 0.001), and protamine (67.4% vs. 68.0%, P < 0.01). Stroke or death at 30 days (1.9% vs. 1.8%, P = 0.60) and 1 year (hazard ratio [HR] 0.98, 95% confidence interval [CI] 0.94-1.01, P = 0.20) were similar between groups, which persisted in asymptomatic patients (HR 0.97, 95% CI 0.93-1.01, P = 0.17) and symptomatic patients (HR 0.99, 95% CI 0.93-1.05, P = 0.71). The similarities in 1-year stroke or death rates existed in both the United States (HR 0.96, 95% CI 0.92-1.01, P = 0.09) and Canada (HR 1.21, 95% CI 0.47-3.11, P = 0.70). CONCLUSIONS: Despite sex differences in clinical and procedural characteristics, women and men have similar 30-day and 1-year outcomes following CEA.

Vascular Complications With Intra-aortic Balloon Pump (IABP): Experience From a Large Canadian Metropolitan Centre.

Background: Intra-aortic balloon pump (IABP) insertion in critically ill patients has been associated with both vascular and nonvascular complications, which have restricted its use. The primary objective for this study was to determine the frequency and predictors of vascular complication in our centre. Methods: We conducted a retrospective cohort study of consecutive patients treated with an IABP between January 2014 and June 2018. Baseline clinical characteristics, cannulation details, duration of treatment and management, overall mortality, and complications were extracted from electronic and paper medical records. Results: A total of 187 patients required an IABP; of these, 146 were male (78.1%), the average age was 65.2 ± 11.5 years, and body mass index was 26.8 ± 6.2 kg/m2. A majority of the patients had an IABP inserted in either the cardiac catheterization laboratory (54.5%) or an outside hospital (26.7%). The main indications for insertion were acute decompensated heart failure-cardiogenic shock (58.3%), followed by acute myocardial infarction and cardiogenic shock (26.2%). From the documented cannulation site, the right femoral artery was cannulated in 61.6% of patients, with a median size of 7.5 Fr (range: 5 -12 Fr). Mortality for in-hospital, 30-day, and 1-year mortality was calculated at 37.4%, 40.6%, and 41.7%, respectively. Limb ischemia (3.2%), bleeding (1.6%), mesenteric ischemia (0.5%), compartment syndrome (0.5%), and fasciotomy (0.5%), were rare occurrences. No records indicated amputation, aortoiliac dissection, thrombectomy, or infection at the site of insertion. Conclusions: This single-centre retrospective study demonstrated that more than one third of this patient population died secondary to their primary diagnosis. The incidence of vascular complications secondary to IABP insertion remained low, with less than 3% developing an ischemic limb.

Contexte: L'insertion d'un ballon de contrepulsion intra-aortique (BCPIA) chez les patients dont l'état est critique est associée à des complications à la fois vasculaires et non vasculaires, ce qui limite son utilisation. L'objectif principal de cette étude était de déterminer la fréquence des complications vasculaires dans notre centre ainsi que les facteurs prédictifs de ces complications. Méthodologie: Nous avons mené une étude de cohorte rétrospective auprès de patients traités consécutivement par BCPIA entre janvier 2014 et juin 2018. Les caractéristiques cliniques initiales, les détails sur la canulation, la durée du traitement et de la prise en charge, la mortalité globale et les complications ont été extraits des dossiers médicaux électroniques et en format papier. Résultats: Au total, un BCPIA a été nécessaire chez 187 patients; 146 d'entre eux étaient des hommes (78,1 %), l'âge moyen était de 65,2 ± 11,5 ans, et l'indice de masse corporelle moyen était de 26,8 ± 6,2 kg/m2. La majorité des insertions de BCPIA s'étaient déroulées soit dans le laboratoire de cathétérisme (54,5 %) ou dans un hôpital externe (26,7 %). Les principales indications pour lesquelles ces insertions ont été effectuées étaient l'insuffisance cardiaque aiguë décompensée avec choc cardiogénique (58,3 %), suivie de l'infarctus du myocarde aigu avec choc cardiogénique (26,2 %). Selon les sites de canulation documentés, l'artère fémorale droite avait été canulée chez 61,6 % des patients, avec un calibre médian de 7,5 Fr (de 5 à 12 Fr). Les valeurs de mortalité à l'hôpital, à 30 jours et à un an, ont été établies à 37,4 %, 40,6 % et 41,7 %, respectivement. L'ischémie d'un membre (3,2 %), l'hémorragie (1,6 %), l'ischémie mésentérique (0,5 %), le syndrome des loges (0,5 %) et la fasciotomie (0,5 %) ont été constatés dans quelques rares cas. Aucun dossier n'indiquait d'amputation, de dissection aorto-iliaque, de thrombectomie ou d'infection au point d'insertion. Conclusions: Cette étude de cohorte rétrospective unicentrique a permis de démontrer que plus d'un tiers des patients de la population à l'étude sont décédés des suites de leur diagnostic primaire. L'incidence de complications vasculaires secondaires à l'insertion d'un BCPIA est demeurée faible, avec moins de 3 % des patients présentant une ischémie d'un membre.

Outcomes of hybrid procedures for peripheral arterial disease: 5-year single center experience.

OBJECTIVE: The objective of the study was to present patients with peripheral vascular disease (PVD) who underwent hybrid procedures at our institution, the results of these interventions for a 5-year period and determine patency, mortality, failure, and amputation rates compared to the literature. MATERIAL AND METHODS: Observational, single center, retrospective, and cross-sectional study which analyzed data gathered from the vascular quality initiative from patients who had hybrid revascularization procedures from January 2010 to December 2015. RESULTS: 87 patients were identified: 51 (58%) male, 36 (41%) female, 9 (10%) had critical limb ischemia (CLI), and 78 (90%) claudication. We analyzed results of hybrid interventions in their variations. Technical success rate was 100%, patency at 2 years 88.5% (primary 65%, primary-assisted 18.3%, and secondary 4.5%) and 11.49% failure rate (lost patency < 1 year, conversion to open or/and amputation). Predictors of failure were: Female, previous chronic heart failure, longer length of stay, and previously transferred from another hospital. Amputation rate was 12.6% (10.3% major and 2.2% minor amputation), the only significant predictor was age (p = 0.035, odds ratio = 0.89) (0.806-99). CONCLUSIONS: Hybrid procedures are effective to treat patients with either CLI or claudication. Our study had outcomes comparable to the literature, with similar patency, amputation, and complication rates. We conclude it is a safe and effective option for PVD with multi-level disease.

OBJETIVO: Presentar pacientes con EAP que requirieron procedimientos híbridos en nuestra institución, resultados en 1 periodo de 5 años y determinar permeabilidad, mortalidad, falla y rangos de amputación comparado con la literatura. MATERIAL Y MÉTODOS: Estudio observacional un céntrico, retrospectivo y transversal que analizó datos obtenidos del VQI de pacientes post-revascularización híbrida de Enero 2010 a Diciembre 2015. RESULTADOS: Se identificaron 87 pacientes: 51 masculinos (58%) y 34 femeninos (41%). 9 (10%) presentaron isquemia crítica, 78 (90%) claudicación. Se analizaron resultados de dichas intervenciones en sus variaciones, con éxito técnico 100%, permeabilidad a 2 años 88.5% (primaria 65%, primaria asistida 18.3%, secundaria 4.5%) y 11.49% de falla (pérdida de permeabilidad < 1 año, conversión a cirugía abierta y/o amputación). Predictores de falla: femenino, IC, larga EIH, traslado de hospital previo). El rango de amputación fue 12.6% (10.3% mayor, 2-2% amputación menor) y el único predictor significativo fue edad (p = 0.035, OR = 0.89) (0.806-99). CONCLUSIONES: Los procedimientos híbridos son efectivos para tratar pacientes con isquemia crítica o claudicación. Nuestro estudio tuvo resultados similares a la literatura,permeabilidad, riesgo de amputación y complicaciones comparables con lo descrito. Concluimos que es una opción segura y efectiva para tratar pacientes con EAP multinivel.