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Background: Endpoints that patients and clinicians consider important may differ based on patients' preferences and values. Several methods are available to elicit patient preferences in a succinct and methodologically valid manner., Purpose: We conducted a scoping review of methods used to elicit patient preferences in invasive procedures to provide a framework for researchers and clinicians to incorporate these measures into future efforts., Methods: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials and health and psychological instruments database were searched from inception until September 2020. Articles that examined patient preferences for any invasive procedure were eligible for inclusion. Selection and extraction were completed in duplicate. Preference elicitation methods were identified and summarized., Results: Three hundred ninety-four articles (n = 76,921 patients) were included representing several surgical specialties. Of included studies, 11.7% (n = 46) used both quantitative and qualitative methods, 81.2% (n = 320) used quantitative methods only, and 7.1% (n = 28) used qualitative methods only to elicit preferences. The most frequently employed quantitative method to elicit preferences was simple choice selection, while one-on-one interviews with participants was the most frequently used qualitative method. Preference elicitation was the primary outcome in 74.6% (n = 294) of included studies., Conclusion: There are several methods to elicit patient preferences in surgical research. Qualitative methods are valuable for exploring views and generating consensus statements. Quantitative methods are better suited for assessing relative preferences, establishing preference thresholds, or ascertaining the presence of preferences. The choice of method should align with the specific research objectives., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To describe long-term re-presentations and interventions following non-operative management (NOM) of acute appendicitis (AA)., Summary Background Data: Trial data suggest NOM of AA carries a substantial risk of subsequent appendectomy, although NOM is increasingly offered to patients. Population-based data is required to understand the real-world long-term course of patients undergoing NOM of AA., Methods: This population-based cohort study included all adult patients in Ontario, Canada who presented to any emergency department (ED) with AA between 2004-2019. Patients who did not undergo a procedure on index ED presentation or hospital admission were defined as NOM and followed for five years. The cumulative incidence of composite re-presentation or intervention (ED re-presentation, re-admission, emergency, or scheduled appendicitis procedure) was calculated accounting for competing risk of death., Results: Of 156,362 patients identified with AA, 13,200 underwent NOM. The cumulative incidence of composite re-presentation or intervention was 33% at 1-year (95%CI 32-33%) and 36% at 5-years (95%CI 36-37%). At 5-years, the incidence of appendicitis-specific ED re-presentation or hospital re-admission was 16% (95%CI 15-16%), the incidence of an emergency procedure for AA was 12% (95%CI 12-13%), and the incidence of scheduled surgery was 21% (95%CI 20-21%). In a subgroup of patients with uncomplicated AA, composite re-presentation or intervention was 28% at 1-year (95%CI 27-29%) and 32% at 5-years (95%CI 32-33%)., Conclusions: Real-world estimates of emergency re-presentation with or without urgent surgery following NOM of AA were lower than previously described. Scheduled appendectomy made up an important proportion of long-term interventions following NOM., Competing Interests: Conflicts of Interest and Source of Funding: This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources. There are no conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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.

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Associations between procedure volumes and outcomes can inform minimum volume standards and the regionalization of health services. Robot-assisted surgery continues to expand globally; however, data are limited regarding which hospitals should be using the technology., Study Design: Using administrative health data for all residents of Ontario, Canada, this retrospective cohort study included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using 4 arms (RPL-4) between January 2010 and September 2021. Associations between yearly hospital volumes and 90-day major complications were evaluated using multivariable logistic regression models adjusted for patient characteristics and clustering at the level of the hospital., Results: A total of 10,879 patients were included, with 7567, 1776, 724, and 812 undergoing a RARP, TRH, RAPN, and RPL-4, respectively. Yearly hospital volume was not associated with 90-day complications for any procedure. Doubling of yearly volume was associated with a 17-min decrease in operative time for RARP (95% confidence interval [CI] - 23 to - 10), 8-min decrease for RAPN (95% CI - 14 to - 2), 24-min decrease for RPL-4 (95% CI - 29 to - 19), and no significant change for TRH (- 7 min; 95% CI - 17 to 3)., Conclusion: The risk of 90-day major complications does not appear to be higher in low volume hospitals; however, they may not be as efficient with operating room utilization. Careful case selection may have contributed to the lack of an observed association between volumes and complications., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: Tools for endovascular performance assessment are necessary in competency based education. This study aimed to develop and test a detailed analysis tool to assess steps, errors, and events in peripheral endovascular interventions (PVI)., Methods: A modified Delphi consensus was used to identify steps, errors, and events in iliac-femoral-popliteal endovascular interventions. International experts in vascular surgery, interventional radiology, cardiology, and angiology were identified, based on their scientific track record. In an initial open ended survey round, experts volunteered a comprehensive list of steps, errors, and events. The items were then rated on a five point Likert scale until consensus was reached with a pre-defined threshold (Cronbach's alpha > 0.7) and > 70% expert agreement. An experienced endovascular surgeon applied the finalised frameworks on 10 previously videorecorded elective PVI cases., Results: The expert consensus panel was formed by 28 of 98 invited proceduralists, consisting of three angiologists, seven interventional radiologists, five cardiologists, and 13 vascular surgeons, with 29% from North America and 71% from Europe. The Delphi process was completed after three rounds (Cronbach's alpha; α steps  = 0.79; α errors  = 0.90; α events  = 0.90), with 15, 26, and 18 items included in the final step (73 - 100% agreement), error (73 - 100% agreement), and event (73 - 100% agreement) frameworks, respectively. The median rating time per case was 4.3 hours (interquartile range [IQR] 3.2, 5 hours). A median of 55 steps (IQR 40, 67), 27 errors (IQR 21, 49), and two events (IQR 1, 6) were identified per case., Conclusion: An evaluation tool for the procedural steps, errors, and events in iliac-femoral-popliteal endovascular procedures was developed through a modified Delphi consensus and applied to recorded intra-operative data to identify hazardous steps, common errors, and events. Procedural mastery may be promoted by using the frameworks to provide endovascular proceduralists with detailed technical performance feedback., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

Objective: We assessed trends in socioeconomic inequalities in cardiovascular risk factors prevalence among Swiss adults from 2008 to 2019., Methods: Using data from the Bus Santé study, an annual survey of adults living in Geneva, Switzerland, we calculated the prevalence per period and by demographic and socioeconomic indicators, assessing inequality trends using the relative index of inequality (RII) and the slope index of inequality (SII)., Results: Among 10,739 participants, most CVD risk factors decreased over time, while diabetes, obesity, and smoking prevalence remained steady. In 2017-2019, prevalence of most CVD risk factors was higher in socioeconomically disadvantaged groups. Relative and absolute inequalities decreased over time, but mostly remained, for hypertension [in 2017-2019, education-RII (95 % CI) = 1.27 (1.12-1.46), income-RII = 1.27 (1.10-1.47)], hypercholesterolemia [education-RII = 1.15 (1.00-1.32)], and sedentarity [education-RII = 1.95 (1.52-2.51), income-RII = 1.69 (1.28-2.23)], and appeared to have reversed for hazardous alcohol use [income-RII = 0.75 (0.60-0.93)]. Substantial and persistent relative and absolute inequalities in diabetes prevalence were observed [education-RII = 2.39 (1.75-3.27), income-RII = 3.18 (2.25-4.48), and subsidy-RII = 2.77 (1.89-4.05)]. Inequalities were also marked across all socioeconomic indicators for obesity prevalence [education-RII = 3.32 (2.63-4.19), income-RII = 2.37 (1.85-3.04), subsidy-RII = 1.98 (1.48-2.66)] and for smoking [education-RII = 2.42 (2.06-2.84), income-RII = 2.37 (1.99-2.84), subsidy-RII = 1.91 (1.56-2.35)]., Conclusions: Over 12 years in Geneva, Switzerland, socioeconomic inequalities in hypertension, hypercholesterolemia, hazardous alcohol use, and sedentarity decreased but persist, while substantial inequalities in diabetes, obesity, and smoking remained unchanged., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Introduction: Lower extremity amputation (LEA) is a life altering procedure, with significant negative impacts to patients, care partners, and the overall health system. There are gaps in knowledge with respect to patterns of healthcare utilization following LEA due to dysvascular etiology., Objective: To examine inpatient acute and emergency department (ED) healthcare utilization among an incident cohort of individuals with major dysvascular LEA 1 year post-initial amputation; and to identify factors associated with acute care readmissions and ED visits., Design: Retrospective cohort study using population-level administrative data., Setting: Ontario, Canada., Population: Adults individuals (18 years or older) with a major dysvascular LEA between April 1, 2004 and March 31, 2018., Interventions: Not applicable., Main Outcome Measures: Acute care hospitalizations and ED visits within one year post-initial discharge., Results: A total of 10,905 individuals with major dysvascular LEA were identified (67.7% male). There were 14,363 acute hospitalizations and 19,660 ED visits within one year post-discharge from initial amputation acute stay. The highest common risk factors across all the models included age of 65 years or older (versus less than 65 years), high comorbidity (versus low), and low and moderate continuity of care (versus high). Sex differences were identified for risk factors for hospitalizations, with differences in the types of comorbidities increasing risk and geographical setting., Conclusion: Persons with LEA were generally more at risk for acute hospitalizations and ED visits if higher comorbidity and lower continuity of care. Clinical care efforts might focus on improving transitions from the acute setting such as coordinated and integrated care for sub-populations with LEA who are more at risk., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Guilcher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: The COVID-19 pandemic has resulted in a significant diagnostic, screening, and procedure backlog in Ontario. Engagement of key stakeholders in healthcare leadership positions is urgently needed to inform a comprehensive provincial recovery strategy., Methods: A list of 20 policy recommendations addressing the diagnostic, screening and procedure backlog in Ontario were transformed into a national online survey. Policy recommendations were rated on a 7-point Likert scale (strongly agree to strongly disagree) and organized into those retained (≥75% strongly agree to somewhat agree), discarded (≥80% somewhat disagree to strongly disagree), and no consensus reached. Survey participants included a diverse sample of healthcare leaders with the potential to impact policy reform., Results: Of 56 healthcare leaders invited to participate, there were 34 unique responses (61% response rate). Participants were from diverse clinical backgrounds, including surgical subspecialties, medicine, nursing, and healthcare administration and held institutional or provincial leadership positions. A total of 11 of 20 policy recommendations reached the threshold for consensus agreement with the remaining 9 having no consensus reached., Conclusion: Consensus agreement was reached among Canadian healthcare leaders on 11 policy recommendations to address the diagnostic, screening, and procedure backlog in Ontario. Recommendations included strategies to address patient information needs on expected wait times, expand health and human resource capacity, and streamline efficiencies to increase operating room output. No consensus was reached on the optimal funding strategy within the public system in Ontario or the appropriateness of implementing private funding models.

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.

Objective: To characterize the association between ambulatory cardiology or general internal medicine (GIM) assessment prior to surgery and outcomes following scheduled major vascular surgery., Background: Cardiovascular risk assessment and management prior to high-risk surgery remains an evolving area of care., Methods: This is population-based retrospective cohort study of all adults who underwent scheduled major vascular surgery in Ontario, Canada, April 1, 2004-March 31, 2019. Patients who had an ambulatory cardiology and/or GIM assessment within 6 months prior to surgery were compared to those who did not. The primary outcome was 30-day mortality. Secondary outcomes included: composite of 30-day mortality, myocardial infarction or stroke; 30-day cardiovascular death; 1-year mortality; composite of 1-year mortality, myocardial infarction or stroke; and 1-year cardiovascular death. Cox proportional hazard regression using inverse probability of treatment weighting (IPTW) was used to mitigate confounding by indication., Results: Among 50,228 patients, 20,484 (40.8%) underwent an ambulatory assessment prior to surgery: 11,074 (54.1%) with cardiology, 8,071 (39.4%) with GIM and 1,339 (6.5%) with both. Compared to patients who did not, those who underwent an assessment had a higher Revised Cardiac Risk Index (N with Index over 2= 4,989[24.4%] vs. 4,587[15.4%], P<0.001) and more frequent pre-operative cardiac testing (N=7,772[37.9%] vs. 6,113[20.6%], P<0.001) but, lower 30-day mortality (N=551[2.7%] vs. 970[3.3%], P<0.001). After application of IPTW, cardiology or GIM assessment prior to surgery remained associated with a lower 30-day mortality (weighted Hazard Ratio [95%CI] = 0.73 [0.65-0.82]) and a lower rate of all secondary outcomes., Conclusions: Major vascular surgery patients assessed by a cardiology or GIM physician prior to surgery have better outcomes than those who are not. Further research is needed to better understand potential mechanisms of benefit., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Competing Interests: Disclosures None.

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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

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.

Background: Robot-assisted surgery has been rapidly adopted. It is important to define the learning curve to inform credentialling requirements, training programs, identify fast and slow learners, and protect patients. This study aimed to characterize the hospital learning curve for common robot-assisted procedures., Study Design: This cohort study, using administrative health data for Ontario, Canada, included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using four arms (RPL-4) between 2010 and 2021. The association between cumulative hospital volume of a robot-assisted procedure and major complications was evaluated using multivariable logistic models adjusted for patient characteristics and clustering at the hospital level., Results: A total of 6814 patients were included, with 5230, 543, 465, and 576 patients in the RARP, TRH, RAPN, and RPL-4 cohorts, respectively. There was no association between cumulative hospital volume and major complications. Visual inspection of learning curves demonstrated a transient worsening of outcomes followed by subsequent improvements with experience. Operative time decreased for all procedures with increasing volume and reached plateaus after approximately 300 RARPs, 75 TRHs, and 150 RPL-4s. The odds of a prolonged length of stay decreased with increasing volume for patients undergoing a RARP (OR 0.87; 95% CI 0.82-0.92) or RPL-4 (OR 0.77; 95% CI 0.68-0.87)., Conclusion: Hospitals may adopt robot-assisted surgery without significantly increasing the risk of major complications for patients early in the learning curve and with an expectation of increasing efficiency., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA)., Background: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools., Methods: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021. Input features included 36 preoperative variables. The primary outcome was 30-day major adverse cardiovascular event (composite of myocardial infarction, stroke, or death). Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Subgroup analysis was performed to assess model performance based on age, sex, race, ethnicity, and prior AAA repair., Results: Overall, 16,282 patients were included. The primary outcome of 30-day major adverse cardiovascular event occurred in 390 (2.4%) patients. Our best-performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.95 (0.94-0.96) compared with logistic regression [0.72 [0.70-0.74)]. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.06. Model performance remained robust on all subgroup analyses., Conclusions: Our newer ML models accurately predict 30-day outcomes following EVAR using preoperative data and perform better than logistic regression. Our automated algorithms can guide risk mitigation strategies for patients being considered for EVAR., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: Disclosures None., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Diabetes is increasing in Switzerland, but whether its management has improved is unknown. We aimed to assess diabetes prevalence, diagnosis, treatment, and control in French-speaking Switzerland. Our study used cross-sectional data for years 2005-2019 from a population-based study in Geneva, Switzerland. Overall prevalence (self-reported diagnosis and/or fasting plasma glucose level ≥ 7 mmol/L), diagnosed, treated (among diagnosed participants) and controlled diabetes (defined as a fasting plasma glucose FPG < 6.7 mmol/L among treated participants) were calculated for periods 2005-9, 2010-4 and 2015-9. Data from 12,348 participants (mean age ± standard deviation: 48.6 ± 13.5 years, 51.7% women) was used. Between 2005-9 and 2015-9, overall prevalence and frequency of diagnosed diabetes decreased (from 8.7 to 6.2% and from 7.0 to 5.2%, respectively). Among participants diagnosed with diabetes, treatment and control rates did not change from 44.1 to 51.9%, p = 0.251 and from 30.2 to 34.0%, p = 0.830, respectively. A trend towards higher treatment of participants with diabetes was found after multivariable adjustment, while no changes were found for overall prevalence, diagnosis, nor control. Among antidiabetic drugs, percentage of combinations increased from 12 to 23%; percentage of sulfonylureas and biguanides decreased from 15 to 6% and from 63 to 54%, respectively, while no trend was found for insulin. After multivariable analysis, women with diabetes were less likely to be treated but more likely to be controlled, the opposite association being found for obesity. In conclusion, in Canton Geneva, antidiabetic combination therapy is gaining importance, but only half of participants diagnosed with diabetes are treated, and glycaemic control remains poor., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Background: Screening programs for abdominal aortic aneurysm (AAA) are not available in Canada. We sought to determine the effectiveness and costutility of AAA screening in Ontario., Methods: We compared one-time ultrasonography-based AAA screening for people aged 65 years to no screening using a fully probabilistic Markov model with a lifetime horizon. We estimated life-years, quality-adjusted life-years (QALYs), AAA-related deaths, number needed to screen to prevent 1 AAA-related death and costs (in Canadian dollars) from the perspective of the Ontario Ministry of Health. We retrieved model inputs from literature, Statistics Canada, and the Ontario Case Costing Initiative., Results: Screening reduced AAA-related deaths by 84.9% among males and 81.0% among females. Compared with no screening, screening resulted in 0.04 (18.96 v. 18.92) additional life-years and 0.04 (14.95 v. 14.91) additional QALYs at an incremental cost of $80 per person among males. Among females, screening resulted in 0.02 (21.25 v. 21.23) additional life-years and 0.01 (16.20 v. 16.19) additional QALYs at an incremental cost of $11 per person. At a willingness-to-pay of $50 000 per year, screening was cost-effective in 84% (males) and 90% (females) of model iterations. Screening was increasingly cost-effective with higher AAA prevalence., Interpretation: Screening for AAA among people aged 65 years in Ontario was associated with fewer AAA-related deaths and favourable cost-effectiveness. To maximize QALY gains per dollar spent and AAA-related deaths prevented, AAA screening programs should be designed to ensure that populations with high prevalence of AAA participate., Competing Interests: Competing interests: Dominique Vervoort sits on the medical advisory board with the Global Alliance for Rheumatic and Congenital Hearts. Thomas Lindsay reports honoraria from Artivion and participation on an advisory board for Novartis. Varun Kapila is the provincial lead for vascular at Ontario Health. Charles de Mestral reports consulting fees from the Health Technology Assessment Unit, Ontario Health. No other competing interests were declared., (© 2024 CMA Impact Inc. or its licensors.)

Objective: The management of diabetic foot ulcers (DFUs) is complex, and patient engagement is essential for DFU healing, but it often comes down to the patient's consultation. Therefore, we sought to document patients' engagement in terms of collaboration and partnership for DFUs in 5 levels (direct care, organizational, policy level, research, and education), as well as strategies for patient engagement using an adapted engagement framework., Methods: We conducted a scoping review of the literature from inception to April 2022 using the Joanna Briggs Institute method and a patient-oriented approach. We also consulted DFU stakeholders to obtain feedback on the findings. The data were extracted using PROGRESS+ factors for an equity lens. The effects of engagement were described using Bodenheimer's quadruple aims for value-based care., Results: Of 4,211 potentially eligible records, 15 studies met our eligibility criteria, including 214 patients involved in engagement initiatives. Most studies were recent (9 of 15 since 2020) and involved patient engagement at the direct medical care level (8 of 15). Self-management (7 of 15) was the principal way to clinically engage the patients. None of the studies sought to define the direct influence of patient engagement on health outcomes., Conclusions: Very few studies described patients' characteristics. Engaged patients were typically men from high-income countries, in their 50s, with poorly managed type 2 diabetes. We found little rigorous research of patient engagement at all levels for DFUs. There is an urgent need to improve the reporting of research in this area and to engage a diversity of patients., (Copyright © 2023 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Objective: Open surgical treatment options for aortoiliac occlusive disease carry significant perioperative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following open aortoiliac revascularization., Methods: The National Surgical Quality Improvement Program (NSQIP) targeted vascular database was used to identify patients who underwent open aortoiliac revascularization for atherosclerotic disease between 2011 and 2021. Input features included 38 preoperative 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. The 30-day secondary outcomes were individual components of the primary outcome, major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death), individual components of MACE, wound complication, bleeding, other morbidity, non-home discharge, and unplanned readmission. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. Variable importance scores were calculated to determine the top 10 predictive features. Performance was assessed on subgroups based on age, sex, race, ethnicity, symptom status, procedure type, and urgency., Results: Overall, 9649 patients were included. The primary outcome of 30-day MALE or death occurred in 1021 patients (10.6%). Our best performing prediction model for 30-day MALE or death was XGBoost, achieving an AUROC of 0.95 (95% confidence interval [CI], 0.94-0.96). In comparison, logistic regression had an AUROC of 0.79 (95% CI, 0.77-0.81). For 30-day secondary outcomes, XGBoost achieved AUROCs between 0.87 and 0.97 (untreated loss of patency [0.95], major reintervention [0.88], major amputation [0.96], death [0.97], MACE [0.95], myocardial infarction [0.88], stroke [0.93], wound complication [0.94], bleeding [0.87], other morbidity [0.96], non-home discharge [0.90], and unplanned readmission [0.91]). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. The strongest predictive feature in our algorithm was chronic limb-threatening ischemia. Model performance remained robust on all subgroup analyses of specific demographic/clinical populations., Conclusions: Our ML models accurately predict 30-day outcomes following open aortoiliac revascularization using preoperative data, performing better than logistic regression. They have potential for important utility in guiding risk-mitigation strategies for patients being considered for open aortoiliac revascularization to improve outcomes., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of BJS Society Ltd. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The 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 metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.

This manuscript describes the implementation and initial evaluation of a novel Canadian acute care pathway for people with a diabetic foot ulcer (DFU). A multidisciplinary team developed and implemented an acute care pathway for patients with a DFU who presented to the emergency department (ED) and required hospitalisation at a tertiary care hospital in Canada. Processes of care, length of stay (LOS), and hospitalisation costs were considered through retrospective cohort study of all DFU hospitalizations from pathway launch in December 2018 to December 2020. There were 82 DFU-related hospital admissions through the ED of which 55 required invasive intervention: 28 (34.1%) minor amputations, 16 (19.5%) abscess drainage and debridement, 6 (7.3%) lower extremity revascularisations, 5 (6.1%) major amputations. Mean hospital LOS was 8.8 ± 4.9 days. Mean hospitalisation cost was $20 569 (±14 143): $25 901 (±15 965) when surgical intervention was required and $9279 (±7106) when it was not. LOS and hospitalisation costs compared favourably to historical data. An acute care DFU pathway can support the efficient evaluation and management of patients hospitalised with a DFU. A dedicated multidisciplinary DFU care team is a valuable resource for hospitals in Canada., (© 2023 The Authors. International Wound Journal published by Medicalhelplines.com Inc and John Wiley & Sons Ltd.)

Background: Surgical procedures in Canada were historically funded through global hospital budgets. Activity-based funding models were developed to improve access, equity, timeliness, and value of care for priority areas. COVID-19 upended health priorities and resulted in unprecedented disruptions to surgical care, which created a significant procedure gap. We hypothesized that activity-based funding models influenced the magnitude and trajectory of this procedure gap., Methods: Population-based analysis of procedure rates comparing the pandemic (March 1, 2020-December 31, 2021) to a prepandemic baseline (January 1, 2017-February 29, 2020) in Ontario, Canada. Poisson generalized estimating equation models were used to predict expected rates in the pandemic based on the prepandemic baseline. Analyses were stratified by procedure type (outpatient, inpatient), body region, and funding category (activity-based funding programs vs. global budget)., Results: In all, 281,328 fewer scheduled procedures were performed during the COVID-19 period compared with the prepandemic baseline (Rate Ratio 0.78; 95% CI 0.77-0.80). Inpatient procedures saw a larger reduction (24.8%) in volume compared with outpatient procedures (20.5%). An increase in the proportion of procedures funded through activity-based programs was seen during the pandemic (52%) relative to the prepandemic baseline (50%). Body systems funded predominantly through global hospital budgets (eg, gynecology, otologic surgery) saw the least months at or above baseline volumes, whereas those with multiple activity-based funding options (eg, musculoskeletal, abdominal) saw the most months at or above baseline volumes., Conclusions: Those needing procedures funded through global hospital budgets may have been disproportionately disadvantaged by pandemic-related health care disruptions., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

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., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Importance: The association of inpatient COVID-19 caseloads with outcomes in patients hospitalized with non-COVID-19 conditions is unclear., Objective: To determine whether 30-day mortality and length of stay (LOS) for patients hospitalized with non-COVID-19 medical conditions differed (1) before and during the pandemic and (2) across COVID-19 caseloads., Design, Setting, and Participants: This retrospective cohort study compared patient hospitalizations between April 1, 2018, and September 30, 2019 (prepandemic), vs between April 1, 2020, and September 30, 2021 (during the pandemic), in 235 acute care hospitals in Alberta and Ontario, Canada. All adults hospitalized for heart failure (HF), chronic obstructive pulmonary disease (COPD) or asthma, urinary tract infection or urosepsis, acute coronary syndrome, or stroke were included., Exposure: The monthly surge index for each hospital from April 2020 through September 2021 was used as a measure of COVID-19 caseload relative to baseline bed capacity., Main Outcomes and Measures: The primary study outcome was 30-day all-cause mortality after hospital admission for the 5 selected conditions or COVID-19 as measured by hierarchical multivariable regression models. Length of stay was the secondary outcome., Results: Between April 2018 and September 2019, 132 240 patients (mean [SD] age, 71.8 [14.8] years; 61 493 female [46.5%] and 70 747 male [53.5%]) were hospitalized for the selected medical conditions as their most responsible diagnosis compared with 115 225 (mean [SD] age, 71.9 [14.7] years, 52 058 female [45.2%] and 63 167 male [54.8%]) between April 2020 and September 2021 (114 414 [99.3%] of whom had negative SARS-CoV-2 test results). Patients admitted during the pandemic with any of the selected conditions and concomitant SARS-CoV-2 infection exhibited a much longer LOS (mean [SD], 8.6 [7.1] days or a median of 6 days longer [range, 1-22 days]) and greater mortality (varying across diagnoses, but with a mean [SD] absolute increase at 30 days of 4.7% [3.1%]) than those without coinfection. Patients hospitalized with any of the selected conditions without concomitant SARS-CoV-2 infection had similar LOSs during the pandemic as before the pandemic, and only patients with HF (adjusted odds ratio [AOR], 1.16; 95% CI, 1.09-1.24) and COPD or asthma (AOR, 1.41; 95% CI, 1.30-1.53) had a higher risk-adjusted 30-day mortality during the pandemic. As hospitals experienced COVID-19 surges, LOS and risk-adjusted mortality remained stable for patients with the selected conditions but were higher in patients with COVID-19. Once capacity reached above the 99th percentile, patients' 30-day mortality AOR was 1.80 (95% CI, 1.24-2.61) vs when the surge index was below the 75th percentile., Conclusions and Relevance: This cohort study found that during surges in COVID-19 caseloads, mortality rates were significantly higher only for hospitalized patients with COVID-19. However, most patients hospitalized with non-COVID-19 conditions and negative SARS-CoV-2 test results (except those with HF or with COPD or asthma) exhibited similar risk-adjusted outcomes during the pandemic as before the pandemic, even during COVID-19 caseload surges, suggesting resiliency in the event of regional or hospital-specific occupancy strains.

Aim/hypothesis: To describe the influence of diabetes on temporal changes in rates of lower extremity revascularisation and amputation for peripheral artery disease (PAD) in Ontario, Canada., Methods: In this population-based repeated cross-sectional study, we calculated annual rates of lower extremity revascularisation (open or endovascular) and amputation (toe, foot or leg) related to PAD among Ontario residents aged ≥40 years between 2002 and 2019. Annual rate ratios (relative to 2002) adjusted for changes in diabetes prevalence alone, as well as fully adjusted for changes in demographics, diabetes and other comorbidities, were estimated using generalized estimating equation models to model population-level effects while accounting for correlation within units of observation., Results: Compared with 2002, the Ontario population in 2019 exhibited a significantly higher prevalence of diabetes (18% vs. 10%). Between 2002 and 2019, the crude rate of revascularisation increased from 75.1 to 90.7/100,000 person-years (unadjusted RR = 1.10, 95% CI = 1.07-1.13). However, after adjustment, there was no longer an increase in the rate of revascularisation (diabetes-adjusted RR = 0.98, 95% CI = 0.96-1.01, fully-adjusted RR = 0.94, 95% CI = 0.91-0.96). The crude rate of amputation decreased from 2002 to 2019 from 49.5 to 45.4/100,000 person-years (unadjusted RR = 0.78, 95% CI = 0.75-0.81), but was more pronounced after adjustment (diabetes-adjusted RR = 0.62, 95% CI = 0.60-0.64; fully-adjusted RR = 0.58, 95% CI = 0.56-0.60)., Conclusions/interpretation: Diabetes prevalence rates strongly influenced rates of revascularisation and amputation related to PAD. A decrease in amputations related to PAD over time was attenuated by rising diabetes prevalence rates., (© 2023 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Background: Thrombosis is a common cause of vascular prosthesis failure. Antibody coating of prostheses to capture circulating endothelial progenitor cells to aid endothelialization on the device surface appears a promising solution to prevent thrombus formation. Compared with random antibody immobilization, oriented antibody coating (OAC) increases antibody-antigen binding capacity and reduces antibody immunogenicity in vivo. Currently, few OAC methods have been documented, with none possessing clinical application potential., Results: Dopamine and the linker amino-PEG8-hydrazide-t-boc were successfully deposited on the surface of cobalt chromium (CC) discs, CC stents and expanded polytetrafluoroethylene (ePTFE) grafts under a slightly basic condition. CD34 antibodies were immobilized through the reaction between aldehydes in the Fc region created by oxidation and hydrazides in the linker after t-boc removal. CD34 antibody-coated surfaces were integral and smooth as shown by scanning electron microscopy (SEM), had significantly reduced or no substrate-specific signals as revealed by X-ray photoelectron spectroscopy, were hospitable for HUVEC growth as demonstrated by cell proliferation assay, and specifically bound CD34 + cells as shown by cell binding testing. CD34 antibody coating turned hydrophobic property of ePTFE grafts to hydrophilic. In a porcine carotid artery interposition model, a confluent monolayer of cobblestone-shaped CD31 + endothelial cells on the luminal surface of the CD34 antibody coated ePTFE graft were observed. In contrast, thrombi and fibrin fibers on the bare graft, and sporadic cells on the graft coated by chemicals without antibodies were seen., Conclusion: A universal, OAC method was developed. Our in vitro and in vivo data suggest that the method can be potentially translated into clinical application, e.g., modifying ePTFE grafts to mitigate their thrombotic propensity and possibly provide for improved long-term patency for small-diameter grafts., (© 2023. The Author(s).)

We assessed 1) trends in prevalence, awareness, treatment and control rates of dyslipidaemia and associated factors, 2) the effect of statin generation/potency on control levels and 3) the effect of ESC lipid guidelines, on lipid management. Data from multiple cross-sectional, population-based surveys conducted between 2005 and 2019 in the canton of Geneva, Switzerland, were used. Prevalence, awareness, treatment and control rates of dyslipidaemia were 46.0% and 34.9% (p < 0.001), 67.0% and 77.3% (p = 0.124), 40.0% and 19.9% (p < 0.001), and 68.0% and 84.0% (p = 0.255), in 2005 and 2019, respectively. After multivariable adjustment, only the decrease in treatment rates was significant. Increasing age, higher BMI, history of hypertension or diabetes were positively associated with prevalence, while female sex was negatively associated. Female sex, history of diabetes or CVD were positively associated with awareness, while increasing age was negatively associated. Increasing age, smoking, higher BMI, history of hypertension, diabetes or CVD were positively associated with treatment, while female sex was negatively associated. Female sex was positively associated with control, while increasing age was negatively associated. Highly potent statins increased from 50.0% to 87.5% and third generation statins from 0% to 47.5% in 2009 and 2015, respectively. Increased statin potency was borderline (p = 0.059) associated with dyslipidaemia control. ESC guidelines had no effect regarding the prescription of more potent or higher generation statins. We conclude that in the canton of Geneva, treatment of diagnosed dyslipidaemia is low, but control is adequate. Women are undertreated but better controlled than men. The most potent hypolipidemic drugs are underused., Competing Interests: The authors report no relationships that could be construed as a conflict of interest., (© 2023 The Authors. Published by Elsevier B.V.)

Objective: We aimed to examine factors associated with parental willingness to vaccinate their children against COVID-19., Methods: We surveyed adults included in a digital longitudinal cohort study composed of participants in previous SARS-CoV-2 serosurveys conducted in Geneva, Switzerland. In February 2022, an online questionnaire collected information on COVID-19 vaccination acceptance, parental willingness to vaccinate their children aged ≥5 years and reasons for vaccination preference. We used multivariable logistic regression to assess the demographic, socioeconomic and health-related factors associated with being vaccinated and with parental intention to vaccinate their children., Results: We included 1,383 participants (56.8% women; 69.3% aged 35-49 years). Parental willingness to vaccinate their children increased markedly with the child's age: 84.0%, 60.9% and 21.2%, respectively, for parents of adolescents aged 16-17 years, 12-15 years and 5-12 years. For all child age groups, unvaccinated parents more frequently indicated not intending to vaccinate their children than vaccinated parents. Refusal to vaccine children was associated with having a secondary education (1.73; 1.18-2.47) relative to a tertiary education and with middle (1.75; 1.18-2.60) and low (1.96; 1.20-3.22) household income relative to high income. Refusal to vaccine their children was also associated with only having children aged 12-15 years (3.08; 1.61-5.91), aged 5-11 years (19.77; 10.27-38.05), or in multiple age groups (6.05; 3.22-11.37), relative to only having children aged 16-17 years., Conclusion: Willingness to vaccinate children was high for parents of adolescents aged 16-17 years but decreased significantly with decreasing child age. Unvaccinated, socioeconomically disadvantaged parents and those with younger children were less likely to be willing to vaccinate their children. These results are important for vaccination programs and developing communication strategies to reach vaccine-hesitant groups, both in the context of COVID-19 and in the prevention of other diseases and future pandemics.

Objective: The aim of this study was to quantify the recent and historical extent of regional variation in revascularization and amputation for peripheral artery disease (PAD)., Methods: This was a repeated cross-sectional analysis of all Ontarians aged 40 years or greater between 2002 and 2019. The co-primary outcomes were revascularization (endovascular or open) and major (above-ankle) amputation for PAD. For each of 14 health care administrative regions, rates per 100,000 person-years (PY) were calculated for 6-year time periods from the fiscal years 2002 to 2019. Rates were directly standardized for regional demographics (age, sex, income) and comorbidities (congestive heart failure, diabetes, chronic obstructive pulmonary disease, chronic kidney disease). The extent of regional variation in revascularization and major amputation rates for each time period was quantified by the ratio of 90th over the 10th percentile (PRR)., Results: In 2014 to 2019, there were large differences across regions in demographics (rural living [range, 0%-39.4%], lowest neighborhood income quintile [range, 10.1%-25.5%]) and comorbidities (diabetes [range, 14.2%-22.0%], chronic obstructive pulmonary disease [range, 7.8%-17.9%]), and chronic kidney disease [range, 2.1%-4.0%]. Standardized revascularization rates ranged across regions from 52.6 to 132.6/100,000 PY and standardized major amputation rates ranged from 10.0 to 37.7/100,000 PY. The extent of regional variation was large (PRR ≥2.0) for both revascularization and major amputation. From 2002-2004 to 2017-2019, the extent of regional variation increased from moderate to large for revascularization (standardized PRR, 1.87 to 2.04) and major amputation (standardized PRR, 1.94 to 3.07)., Conclusions: Significant regional differences in revascularization and major amputation rates related to PAD remain after standardizing for regional differences in demographics and comorbidities. These differences have not improved over time., (Copyright © 2022 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Introduction: Robotic surgery has integrated into the healthcare system despite limited evidence demonstrating its clinical benefit. Our objectives were (i) to describe secular trends and (ii) patient- and system-level determinants of the receipt of robotic as compared to open or laparoscopic surgery., Methods: This population-based retrospective cohort study included adult patients who, between 2009 and 2018 in Ontario, Canada, underwent one of four commonly performed robotic procedures: radical prostatectomy, total hysterectomy, thoracic lobectomy, partial nephrectomy. Patients were categorized based on the surgical approach as robotic, open, or laparoscopic for each procedure. Multivariable regression models were used to estimate the temporal trend in robotic surgery use and associations of patient and system characteristics with the surgical approach., Results: The cohort included 24,741 radical prostatectomy, 75,473 total hysterectomy, 18,252 thoracic lobectomy, and 4608 partial nephrectomy patients, of which 6.21% were robotic. After adjusting for patient and system characteristics, the rate of robotic surgery increased by 24% annually (RR 1.24, 95%CI 1.13-1.35): 13% (RR 1.13, 95%CI 1.11-1.16) for robotic radical prostatectomy, 9% (RR 1.09, 95%CI 1.05-1.13) for robotic total hysterectomy, 26% (RR 1.26, 95%CI 1.06-1.50) for thoracic lobectomy and 26% (RR 1.26, 95%CI 1.13-1.40) for partial nephrectomy. Lower comorbidity burden, earlier disease stage (among cancer cases), and early career surgeons with high case volume at a teaching hospital were consistently associated with the receipt of robotic surgery., Conclusion: The use of robotic surgery has increased. The study of the real-world clinical outcomes and associated costs is needed before further expanding use among additional providers and hospitals., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: More than two years into the COVID-19 pandemic, most of the population has developed anti-SARS-CoV-2 antibodies from infection and/or vaccination. However, public health decision-making is hindered by the lack of up-to-date and precise characterization of the immune landscape in the population. Here, we estimated anti-SARS-CoV-2 antibodies seroprevalence and cross-variant neutralization capacity after Omicron became dominant in Geneva, Switzerland., Methods: We conducted a population-based serosurvey between April 29 and June 9, 2022, recruiting children and adults of all ages from age-stratified random samples of the general population of Geneva, Switzerland. We tested for anti-SARS-CoV-2 antibodies using commercial immunoassays targeting either the spike (S) or nucleocapsid (N) protein, and for antibody neutralization capacity against different SARS-CoV-2 variants using a cell-free Spike trimer-ACE2 binding-based surrogate neutralization assay. We estimated seroprevalence and neutralization capacity using a Bayesian modeling framework accounting for the demographics, vaccination, and infection statuses of the Geneva population., Findings: Among the 2521 individuals included in the analysis, the estimated total antibodies seroprevalence was 93.8% (95% CrI 93.1-94.5), including 72.4% (70.0-74.7) for infection-induced antibodies. Estimates of neutralizing antibodies in a representative subsample (N = 1160) ranged from 79.5% (77.1-81.8) against the Alpha variant to 46.7% (43.0-50.4) against the Omicron BA.4/BA.5 subvariants. Despite having high seroprevalence of infection-induced antibodies (76.7% [69.7-83.0] for ages 0-5 years, 90.5% [86.5-94.1] for ages 6-11 years), children aged <12 years had substantially lower neutralizing activity than older participants, particularly against Omicron subvariants. Overall, vaccination was associated with higher neutralizing activity against pre-Omicron variants. Vaccine booster alongside recent infection was associated with higher neutralizing activity against Omicron subvariants., Interpretation: While most of the Geneva population has developed anti-SARS-CoV-2 antibodies through vaccination and/or infection, less than half has neutralizing activity against the currently circulating Omicron BA.5 subvariant. Hybrid immunity obtained through booster vaccination and infection confers the greatest neutralization capacity, including against Omicron., Funding: General Directorate of Health in Geneva canton, Private Foundation of the Geneva University Hospitals, European Commission ("CoVICIS" grant), and a private foundation advised by CARIGEST SA., Competing Interests: DT is a founder and co-chair of the Scientific Advisory Board of Aerium Therapeutics, holds stock in that company, and has two patents pending for monoclonal antibodies against SARS-CoV-2. KMPB is a member of the Advisory Boards for pneumococcal vaccine and varicella vaccine at MSD. All other authors declare that they have no competing interests., (© 2022 The Authors.)

Objective: Competency-based surgical education requires detailed and actionable feedback to ensure adequate and efficient skill development. Comprehensive operative capture systems such as the Operating Room Black Box (ORBB; Surgical Safety Technologies, Inc), which continuously records and synchronizes multiple sources of intraoperative data, have recently been integrated into hybrid rooms to provide targeted feedback to endovascular teams. The objective of this study is to develop step, error, and event frameworks to evaluate technical performance in elective endovascular aortic repair (EVAR) comprehensively captured by the ORBB (Surgical Safety Technologies, Inc; Toronto, Canada)., Methods: This study is based upon a modified Delphi consensus process to create evaluation frameworks for steps, errors, and events in EVAR. International experts from Vascular Surgery and Interventional Radiology were identified, based on their records of publications and invited presentations, or serving on relevant journal editorial boards. In an initial open-ended survey round, experts were asked to volunteer a comprehensive list of steps, errors, and events for a standard EVAR of an infrarenal aorto-iliac aneurysm (AAA). In subsequent survey rounds, the identified items were presented to the expert panel to rate on a 5-point Likert scale. Delphi survey rounds were repeated until the process reached consensus with a predefined agreement threshold (Cronbach α>0.7). The final frameworks were constructed with items achieving an agreement (responses of 4 or 5) from greater than 70% of experts., Results: Of 98 invited proceduralists, 38 formed the expert consensus panel (39%), consisting of 29 vascular surgeons and 9 interventional radiologists, with 34% from North America and 66% from Europe. Consensus criteria were met following the third round of the Delphi consensus process (Cronbach α=0.82-0.93). There were 15, 32, and 25 items in the error, step, and event frameworks, respectively (within-item agreement=74%-100%)., Conclusion: A detailed evaluation tool for the procedural steps, errors, and events in infrarenal EVAR was developed. This tool will be validated on recorded procedures in future work: It may focus skill development on common errors and hazardous steps. This tool might be used to provide high-quality feedback on technical performance of trainees and experienced surgeons alike, thus promoting surgical mastery.

The field of vascular surgery is in constant evolution. Administrative data and registries can provide important contemporary evidence to inform clinical decision making and delivery of health services. This review outlines some important considerations for retrospective studies using administrative health databases and registries. First, these data sources have advantages (e.g., real-world applicability, timely data access, and relatively lower research cost) and disadvantages (e.g., potential missing data, selection bias, and confounding bias) that may be more or less relevant to different administrative databases or registries. Second, a framework to guide data source selection and provide a summary of frequently used data sources in vascular surgery research is discussed. Third, a retrospective study design warrants planned exposure, outcome, and covariate definitions and, when studying an exposure-outcome association, careful consideration of confounders through direct acyclic graphs. Finally, investigators must plan the most appropriate analytic approach, and we distinguish descriptive, explanatory, and predictive analyses., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objectives: To estimate the prevalence of children and adolescents reporting persistent symptoms after SARS-CoV-2 infection., Design: A random sample of children and adolescents participated with their family members to a serological survey including a blood drawing for detecting antibodies targeting the SARS-CoV-2 nucleocapsid (N) protein and a questionnaire on COVID-19-related symptoms experienced since the beginning of the pandemic., Setting: The study took place in the canton of Geneva, Switzerland, between June and July 2021., Participant: 660 children aged between 2 and 17 years old., Primary and Secondary Outcome: The primary outcome was the persistence of symptoms beyond 4 weeks comparing seropositive and seronegative participants. The type of declared symptoms were also studied as well as associated risk factors., Results: Among seropositive children, the sex-adjusted and age-adjusted prevalence of symptoms lasting longer than 2 weeks was 18.3%, compared with 11.1% among seronegatives (adjusted prevalence difference (ΔaPrev)=7.2%, 95% CI: 1.5% to 13.0%). Among adolescents aged 12-17 years, we estimated the prevalence of experiencing symptoms lasting over 4 weeks to be 4.4% (ΔaPrev,95% CI: -3.8% to 13.6%), whereas no seropositive child aged 2-11 reported symptoms of this duration. The most frequently declared symptoms were fatigue, headache and loss of smell., Conclusions: We estimated the prevalence of experiencing persistent symptoms lasting over 4 weeks to be around 4% among adolescents, which represents a large absolute number, and should raise awareness and concern. We did not observe meaningful differences of persistent symptoms between seropositive and seronegative younger children, suggesting that they may be less affected than their older counterparts., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Backgrounds/aims: Biliary colic is a common cause of emergency department (ED) visits; however, the natural history of the disease and thus the indications for urgent or scheduled surgery remain unclear. Limitations of previous attempts to elucidate this natural history at a population level are based on the reliance on the identification of biliary colic via administrative codes in isolation. The purpose of our study was to validate the use of International Statistical Classification of Diseases and Related Health Problems codes, 10th Revision, Canadian modification (ICD-10-CA) from ED visits in adequately differentiating patients with biliary colic from those with other biliary diagnoses such as cholecystitis or common bile duct stones., Methods: We performed a retrospective validation study using administrative data from two large academic hospitals in Toronto. We assessed all the patients presenting to the ED between January 1, 2012 and December 31, 2018, assigned ICD-10-CA codes in keeping with uncomplicated biliary colic. The codes were compared to the individually abstracted charts to assess diagnostic agreement., Results: Among the 991 patient charts abstracted, 26.5% were misclassified, corresponding to a positive predictive value of 73% (95% confidence interval 73%-74%). The most frequent reasons for inaccurate diagnoses were a lack of gallstones (49.8%) and acute cholecystitis (27.8%)., Conclusions: Our findings suggest that the use of ICD-10 codes as the sole means of identifying biliary colic to the exclusion of other biliary pathologies is prone to moderate inaccuracy. Previous investigations of biliary colic utilizing administrative codes for diagnosis may therefore be prone to unforeseen bias.

Background: Robotic surgery is used in several surgical procedures with limited evidence of clinical benefit. In some jurisdictions, the demand for robotic surgery may have been fueled by public perception of this novel technology. Therefore, we sought to investigate the public's perception of robotic surgery., Study Design: We conducted a cross-sectional survey using a series of vignette-associated questions designed to examine the public's perception of robotic surgery. Eligible participants were recruited through Amazon Mechanical Turk's system and randomized to one of two pairs of vignettes: laparoscopic surgery compared to (1) robotic surgery, or (2) "novel surgical technology" (without using the term "robotic"). Outcomes of interest were anticipated postoperative outcomes using the surgical fear questionnaire, procedure preference, perception of error, trust, and competency of the surgeon., Results: The survey included 362 respondents; 64.1% were male with median age of 53 years. There were no differences in the distribution of responses of the questionnaire based on use of the term "robotic" or "novel surgical technology"; therefore, the two cohorts were combined to examine perception of robotic compared to laparoscopic surgery. More respondents feared outcomes of robotic surgery than laparoscopic surgery (78.2% vs 14.9%, p < 0.001). Participants preferred laparoscopic to robotic surgery (64.4% vs 35.6%, p < 0.001)., Conclusion: The public fears recovery after robotic surgery and prefers laparoscopic surgery. The propagation of robotic surgery is unlikely based on public demand and may be more related to institutional or surgeon perceptions. Surgeons who provide robotic surgery should ensure their patients are comfortable with and understand this technology., (© 2022. Crown.)

Background: Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields with increasing utility in health care. We conducted a survey to determine the perceptions of Canadian vascular surgeons toward AI/ML., Methods: An online questionnaire was distributed to 162 members of the Canadian Society for Vascular Surgery. Self-reported knowledge, attitudes, and perceptions with respect to potential applications, limitations, and facilitators of AI/ML were assessed., Results: Overall, 50 of the 162 Canadian vascular surgeons (31%) responded to the survey. Most respondents were aged 30 to 59 years (72%), male (80%), and White (67%) and practiced in academic settings (72%). One half of the participants reported that their knowledge of AI/ML was poor or very poor. Most were excited or very excited about AI/ML (66%) and were interested or very interested in learning more about the field (83.7%). The respondents believed that AI/ML would be useful or very useful for diagnosis (62%), prognosis (72%), patient selection (56%), image analysis (64%), intraoperative guidance (52%), research (88%), and education (80%). The limitations that the participants were most concerned about were errors leading to patient harm (42%), bias based on patient demographics (42%), and lack of clinician knowledge and skills in AI/ML (40%). Most were not concerned or were mildly concerned about job replacement (86%). The factors that were most important to encouraging clinicians to use AI/ML models were improvements in efficiency (88%), accurate predictions (84%), and ease of use (84%). The comments from respondents focused on the pressing need for the implementation of AI/ML in vascular surgery owing to the potential to improve care delivery., Conclusions: Canadian vascular surgeons have positive views on AI/ML and believe this technology can be applied to multiple aspects of the specialty to improve patient care, research, and education. Current self-reported knowledge is poor, although interest was expressed in learning more about the field. The facilitators and barriers to the effective use of AI/ML identified in the present study can guide future development of these tools in vascular surgery., (© 2022 The Author(s).)

Background: Robotic surgery was integrated into some healthcare systems despite there being few well designed, real-world studies on safety or benefit. This study compared the safety of robotic with laparoscopic, thoracoscopic, and open approaches in common robotic procedures., Methods: This was a population-based, retrospective study of all adults who underwent prostatectomy, hysterectomy, pulmonary lobectomy, or partial nephrectomy in Ontario, Canada, between 2008 and 2018. The primary outcome was 90-day total adverse events using propensity score overlap weights, and secondary outcomes were minor or major morbidity/adverse events., Results: Data on 24 741 prostatectomy, 75 473 hysterectomy, 18 252 pulmonary lobectomy, and 6608 partial nephrectomy operations were included. Relative risks for total adverse events in robotic compared with open surgery were 0.80 (95 per cent c.i. 0.74 to 0.87) for radical prostatectomy, 0.44 (0.37 to 0.52) for hysterectomy, 0.53 (0.44 to 0.65) for pulmonary lobectomy, and 0.72 (0.54 to 0.97) for partial nephrectomy. Relative risks for total adverse events in robotic surgery compared with a laparoscopic/thoracoscopic approach were 0.94 (0.77 to 1.15), 1.00 (0.82 to 1.23), 1.01 (0.84 to 1.21), and 1.23 (0.82 to 1.84) respectively., Conclusion: The robotic approach is associated with fewer adverse events than an open approach but similar to a laparoscopic/thoracoscopic approach. The benefit of the robotic approach is related to the minimally-invasive approach rather than the platform itself., (© The Author(s) 2022. Published by Oxford University Press on behalf of BJS Society Ltd. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: Regular foot screening by a knowledgeable health-care provider is the cornerstone of ulcer and amputation prevention in people with diabetes. However, information on foot screening practices among Canadians with diabetes remains sparse. Therefore, we sought to synthesize available data on the frequency and approach to diabetic foot screening across Canada., Methods: We conducted a scoping review by searching MEDLINE and Embase databases, alongside a grey literature search, for both English- and French-language reports. Data on patients' demographics, setting as well as the frequency of and approach to foot screening were abstracted. Title and abstract screening, full-text review and data abstraction were conducted in duplicate, with discrepancies resolved by a third reviewer., Results: The search yielded 21 reports including information on diabetic foot screening practices in Canada. In a consolidated study sample of 13,388 Canadians with diabetes, 7,277 (53%) reported receiving a foot examination by a health-care provider at least once in the past year. The majority of reports did not provide information on the demographics of patients being screened or details on the approach to foot screening. No report mentioned the use of a triage algorithm applied to the results of foot screening., Conclusions: In this work, we identified the limited frequency and uncertain quality of diabetic foot screening across Canada. Further research should focus on better understanding disparities and barriers to regular diabetic foot screening., (Copyright © 2022 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

Objective: Our goal was to describe contemporary management and inhospital mortality associated with blunt thoracic aortic intimal tears (IT) within the American College of Surgeons Trauma Quality Improvement Program., Summary Background Data: The evidence basis for nonoperative expectant management of traumatic iT of the thoracic aorta remains weak., Methods: All adult patients with a thoracic aortic IT following blunt trauma were captured from Level I and II North American Centers enrolled in Trauma Quality Improvement Program from 2010 to 2017. For each patient, we extracted demographics, injury characteristics, the timing and approach of thoracic aortic repair and in-hospital mortality. Mortality attributable to IT was calculated by comparing IT patients to a propensity-score matched control cohort of severely injured blunt trauma patients without aortic injury., Results: There were 2203 IT patients across 315 facilities. Injury most often resulted from motor vehicle collision (75%). A total of 758 patients (34%) underwent operative management, with 93% (N = 708) of repairs performed via an endovascular approach. Median time to surgery was 11 hours (IQR 4- 40). The frequency of operative management was higher in patients without traumatic brain injury (TBI) (35%, N = 674) compared to those with TBI (29%, N = 84) (P = 0.024). Compared to severely injured blunt trauma patients without aortic injury, ITwas not associated with additional in-hospital mortality (10.7% for IT vs 11.7% for no IT, absolute risk difference: -1.0%, 95% CI: -2.9% to 0.8%)., Conclusions: The majority of blunt thoracic IT are managed nonoperatively and IT does not confer additional in-hospital mortality risk. Future studies should focus on the risk of injury progression., Competing Interests: The authors report no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Diabetic foot ulcers (DFUs) are common and disabling, necessitating lengthy hospitalizations. In this study we sought to identify potentially modifiable determinants of high-intensity hospital care use among adults with DFUs., Methods: Three related case-control studies were conducted using Canada-wide cohorts of adults hospitalized with a DFU from 2011 to 2015. In study 1, cases comprised the top 10% with the highest cumulative 1-year acute care hospital costs; controls were randomly selected from those below the top 10%. Study 2 comprised cases/controls within/below the top 10% for cumulative acute care hospital length of stay (LOS). Study 3 included cases/controls within/below the top 10% for cumulative number of acute care hospitalizations. Using generalized linear models, predictor variables were tested between cases and controls, while adjusting for age and sex., Results: In study 1, mean acute care costs among 8,971 cases and 3,174 controls were $71,757 and $13,687, respectively. Sepsis conferred the greatest excess cost (mean, $38,790; 95% confidence interval [CI], $34,597 to $43,508), followed by chronic kidney disease (mean, $30,607; 95% CI, $28,389 to $32,825) and major lower limb amputation (mean, $30,884; 95% CI, $28,613 to $33,155). In study 2, mean LOS was higher among 8,477 cases (69 days) than 3,467 controls (12 days). Lower limb amputation conferred the greatest adjusted excess in mean LOS (mean, 28 days; 95% CI, 27 to 28 days). In study 3, there was a mean of 3 hospitalizations among 10,341 cases and 1 among 5,509 controls. Peripheral artery disease conferred the greatest excess number of hospitalizations (1.3 more hospitalizations; 1.2 to 1.4)., Conclusions: Early aggressive treatment of chronic kidney disease and peripheral artery disease, alongside guideline-based amputation prevention strategies, may reduce high-intensity hospital care use among adults with DFUs., (Copyright © 2021 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

Background: The frequency of readmissions after COVID-19 hospitalizations is uncertain, as is whether current readmission prediction equations are useful for discharge risk stratification of COVID-19 survivors or for comparing among hospitals. We sought to determine the frequency and predictors of death or unplanned readmission after a COVID-19 hospital discharge., Methods: We conducted a retrospective cohort study of all adults (≥ 18 yr) who were discharged alive from hospital after a nonpsychiatric, nonobstetric, acute care admission for COVID-19 between Jan. 1, 2020, and Sept. 30, 2021, in Alberta and Ontario., Results: Of 843 737 individuals who tested positive for SARS-CoV-2 by reverse transcription polymerase chain reaction during the study period, 46 412 (5.5%) were adults admitted to hospital within 14 days of their positive test. Of these, 8496 died in hospital and 34 846 were discharged alive (30 336 discharged after an index admission of ≤ 30 d and 4510 discharged after an admission > 30 d). One in 9 discharged patients died or were readmitted within 30 days after discharge (3173 [10.5%] of those with stay ≤ 30 d and 579 [12.8%] of those with stay > 30 d). The LACE score (length of stay, acuity, Charlson Comorbidity Index and number of emergency visits in previous 6 months) for predicting urgent readmission or death within 30 days had a c-statistic of 0.60 in Alberta and 0.61 in Ontario; inclusion of sex, discharge locale, deprivation index and teaching hospital status in the model improved the c-statistic to 0.73., Interpretation: Death or readmission after discharge from a COVID-19 hospitalization is common and had a similar frequency in Alberta and Ontario. Risk stratification and interinstitutional comparisons of outcomes after hospital admission for COVID-19 should include sex, discharge locale and socioeconomic measures, in addition to the LACE variables., Competing Interests: Competing interests: Kieran Quinn has a grant from the Canadian Institutes of Health Research (CIHR) for a study of long COVID-19, and reports stock in BioNTech and Merck. Amol Verma holds research grants from CIHR, the Canadian Frailty Network, the Digital Research Alliance of Canada, the St. Michael’s Hospital Association, the St. Michael’s Hospital Foundation and the University of Toronto to support research related to COVID-19. He is the provincial clinical lead for quality improvement in general internal medicine with Ontario Health. Jacob Udell reports grants from Amgen, Bayer, Boehringer-Ingelheim, Novartis and Sanofi. Amy Yu holds a new investigator award from the Heart and Stroke Foundation of Canada and an AFP Innovation award, and grants from the CIHR, Health Data Research Network Canada and Academic Health Sciences Centres of Ontario. Fahad Razak receives a salary from Ontario Health, is assistant director of the Ontario COVID-19 Science Advisory Table and has a salary award from the Physicians’ Services Incorporated (PSI) Foundation., (© 2022 CMA Impact Inc. or its licensors.)