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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. [ABSTRACT FROM AUTHOR]

To compare 1-year health care costs between endovascular and open thoracoabdominal aortic aneurysm (TAAA). Population-based administrative health databases were used to capture TAAA repairs performed in Ontario, Canada, between January 2006 and February 2017. All health care costs incurred by the Ministry of Health from a single-payer universal health care system were included. Costs of the aortic endografts and ancillary devices for the index procedure were estimated as C$44,000 per endovascular case vs C$1000 for open cases, based on previous reports. Costs (2017 Canadian dollars) were calculated in phases (1, 1-3, 3-6, and 6-12 months from surgery) with censoring for death. For each phase, propensity score matching of endovascular and open cases based on preoperative patient and hospital characteristics was used. The association between preoperative characteristics (including repair approach) and the first month postprocedure cost was characterized through multivariable analysis. Overall 664 TAAA repairs were identified (open, n = 361 [54.5%] and endovascular, n = 303 [45.6%]). At 1 month, the median cost was higher for endovascular TAAA repair in the prematching cohort (C$64,892 vs C$36,647; P <.01). Similarly, in 241 well-balanced endovascular/open patient pairs after propensity score matching, the median health care costs were higher in endovascular TAAA cases during the first month (C$62,802 vs C$33,605; P <.01). The 1- to 3-month median cost was not statistically different between endovascular and open TAAA cases either before matching (C$2781 vs C$2618; P =.71) or after matching (C$2762 vs C$2092; P =.58). Likewise, in the 3- to 6-month and 6- to 12-month postprocedure intervals, there were no significant differences in the median health care costs between groups. On multivariable analysis, older age (5-year increments) (relative change [RC] in mean cost, 1.05; 95% confidence interval [CI], 1.04-1.06; P =.01), urgent procedures (RC, 1.29; 95% CI, 1.10-1.52; P <.01), and history of stroke (RC, 1.34; 95% CI, 1.00-1.78; P =.05) were associated with higher costs in the first postoperative month, whereas open relative to endovascular TAAA repair was associated with a decreased 1-month cost (RC, 0.65; 95% CI, 0.56-0.74; P <.01). TAAA repair is expensive regardless of technique. Compared with open TAAA repair, endovascular repair was associated with a higher early cost, owing to the upfront cost of the endograft and aortic ancillary devices. There was no difference in cost from 1 to 12 months after repair. A decrease in the cost of endovascular devices might allow equivalent costs between endovascular and open TAAA repair. [ABSTRACT FROM AUTHOR]

In the present study, we compared the outcomes of elective abdominal aortic aneurysm (AAA) repair in patients with and without rheumatoid arthritis (RA) stratified by the type of surgery. A retrospective population-based cohort study was conducted from 2003 to 2016. Linked administrative health data from Ontario, Canada were used to identify all patients aged ≥65 years who had undergone elective open or endovascular AAA repair during the study period. Patients were identified using validated procedure and billing codes and matching using propensity scores. The primary outcome was survival. The secondary outcomes were major adverse cardiovascular events (MACE)-free survival (defined as freedom from death, myocardial infarction, and stroke), reintervention, and secondary rupture. Of 14,816 patients undergoing elective AAA repair, a diagnosis of RA was present for 309 (2.0%). The propensity-matched cohort included 234 pairs of RA and control patients. The matched cohort was followed up for a mean ± standard deviation of 4.93 ± 3.35 years, and the median survival was 6.76 and 7.31 years for the RA and control groups, respectively. Cox regression analysis demonstrated no statistically significant differences in the hazards for death, MACE, reintervention, or secondary rupture. Analysis of the differences in outcomes stratified by repair approach also showed no statistically significant differences in the hazards for death, MACE, reintervention, or secondary rupture. We found no statistically significant differences in survival, MACE, reintervention, or secondary rupture among patients with RA undergoing elective AAA repair compared with controls. Further studies are required to evaluate the impact of comorbidities and antirheumatic medications on the outcomes of elective AAA repair. [ABSTRACT FROM AUTHOR]

Existing data regarding endovascular aortic repair (EVAR) of ruptured abdominal aortic aneurysm (rAAA) are conflicting in their findings. The purpose of this paper was to determine the long-term outcomes of EVAR vs open surgical repair (OSR) for treatment of rAAA. A population-based retrospective cohort study of all patients 40 years or more that underwent OSR or EVAR of rAAA in Ontario, Canada, from 2003 to 2016 was conducted. Administrative data from the province of Ontario was used as the data source. The propensity for repair approach was calculated using a logistic regression model including all covariates and used for inverse probability of treatment weighting. Cox proportional hazards regression was conducted using the weighted cohort to determine the survival and major adverse cardiovascular event (MACE)-free survival of EVAR relative to OSR for rAAA up to 10 years after repair. A total of 2692 rAAA (261 EVAR [10%] and 2431 OSR [90%]) repairs were recorded from April 1, 2003, to March 31, 2016. Mean follow-up for the entire cohort was 3.4 years (standard deviation [SD], 3.9 years), with a maximum follow-up of 14.0 years. OSR patients were followed for a mean of 3.5 years (SD, 4.0 years) and maximum of 14.0 years, and EVAR patients were followed for a mean of 2.7 years (SD, 2.7 years) and a maximum of 11.4 years. Median survival was 2.7 years overall, and 2.5 and 3.7 years for OSR and EVAR patients, respectively. There were no significant baseline differences between EVAR and OSR patients after inverse probability of treatment weighting. EVAR patients were at lower hazard for all-cause mortality (hazard ratio, 0.49; 95% confidence interval, 0.37-0.65; P <.01), and MACE (hazard ratio, 0.51, 95% confidence interval, 0.40-0.66; P <.01) within 30 days of repair. There were no statistically significant differences between EVAR and OSR in the hazard for all-cause mortality or MACE from 30 days to 5 years, and 5 to 10 years. Despite this, the upfront mortality and MACE benefits of EVAR persisted for more than 4.5 years after repair. This population-based cohort study using administrative data from Ontario, Canada, demonstrated lower hazards for all-cause mortality and MACE within 30 days of operation in favor of EVAR, but no differences in the mid- or longer-term results. More work is needed to understand and improve the long-term outcomes of ruptured endovascular aortic aneurysm repair and ruptured open surgical repair. [ABSTRACT FROM AUTHOR]

The objective was to characterize the growing body of literature regarding nonoperative management of blunt thoracic aortic injury (BTAI). A systematic search of MedLine, Embase, and Cochrane Central was completed to identify original articles reporting injury characteristics and outcomes in patients with BTAI managed nonoperatively during their index hospitalization. Article title and abstract screening, full-text review, and data abstraction were performed in duplicate, with discrepancies resolved by a third reviewer. The quality of each study was evaluated using the Oxford Centre for Evidence-Based Levels of Evidence. Of 2162 identified studies, 74 were included and reported on 8606 patients with BTAI who were managed nonoperatively between 1970 and 2016. Only one study was prospective. The median nonoperative sample size per study was 11 patients. The characterization of aortic injury grade differed across studies. Follow-up varied widely from 1 day to 118 months. Injury healing or improvement on follow-up imaging occurred in 34% (226 of 673 patients; reported in 37 studies), most often in the context of grade I intimal injury. Injury progression or requirement for a thoracic endovascular aneurysm repair for injury progression was 7.6% (66 of 873 patients; reported in 46 studies). A total of 37 studies reported aortic-related death, with an overall rate of 4.5% (37 of 827 patients) and a rate of 1% in grade I and II injuries (1 of 153 patients) and 18% in grade III and IV (9 of 50 patients). An increasing number of reports support nonoperative management of grade I intimal injury, consistent with Society for Vascular Surgery guidelines. However, a retrospective interpretation of the determinants of management, heterogeneous injury characterization, and variable follow-up remain major limitations to the informed use of nonoperative management across all BTAI grades. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study synthesized the literature comparing the long-term (5-9 years) and very long-term (≥10 years) all-cause mortality, reintervention, and secondary rupture rates between endovascular aneurysm repair (EVAR) and open surgical repair (OSR) of abdominal aortic aneurysm (AAA). MEDLINE, Embase, and CENTRAL databases were searched from inception to May 2018 for studies comparing EVAR to OSR with a minimum follow-up period of 5 years. Study selection, data abstraction, and quality assessment were conducted by two independent reviewers, with a third author resolving discrepancies. Study quality was assessed using the Cochrane and Newcastle-Ottawa scales. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using random-effects models. Heterogeneity was quantified using the I 2 statistic, and publication bias was assessed using funnel plots. Our search yielded 3431 unique articles. Three randomized controlled trials and 68 observational studies comparing 151,092 EVAR to 148,692 OSR patients were included. Inter-rater agreement was excellent at the screening (κ = 0.78) and full-text review (κ = 0.89) stages. Overall, the risk of bias was low to moderate. For long-term outcomes, 54 studies reported all-cause mortality (n = 203,246), 23 reported reintervention (n = 157,151), and 4 reported secondary rupture (n = 150,135). EVAR was associated with higher long-term all-cause mortality (OR, 1.19; 95% CI, 1.06-1.33; P =.003, I 2 = 91%), reintervention (OR, 2.12; 95% CI, 1.67-2.69; P <.00001, I 2 = 96%), and secondary rupture rates (OR, 4.84; 95% CI, 2.63-8.89; P <.00001, I 2 = 92%). For very long-term outcomes, 15 studies reported all-cause mortality (n = 48,721), 9 reported reintervention (n = 7511), and 1 reported secondary rupture (n = 1116). There was no mortality difference between groups, but EVAR was associated with higher reintervention (OR, 2.47; 95% CI, 1.71-3.57; P <.00001, I 2 = 84%) and secondary rupture rates (OR, 8.10; 95% CI, 1.01-64.99; P =.05). Subanalysis of more recent studies, with last year of patient recruitment 2010 or after, demonstrated no long-term mortality differences between EVAR and OSR. EVAR is associated with higher long-term all-cause mortality, reintervention, and secondary rupture rates compared with OSR. In the very long-term, EVAR is also associated with higher reintervention and secondary rupture rates. Notably, EVAR mortality has improved over time. Vigilant long-term surveillance of EVAR patients is recommended. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To estimate the positive predictive value (PPV) of Canadian ICD-10 diagnostic coding for the identification of hospitalization related to a diabetic foot ulcer (DFU).Methods: Hospitalizations related to a neuropathic and/or ischemic DFU were identified from the Discharge Abstract Database (DAD) records of a single Canadian tertiary care hospital between April 1, 2002 and March 31, 2019. The first coding approach required a most responsible diagnosis (MRDx) code for diabetes-specific foot ulceration or gangrene (DSFUG group). Three alternative coding approaches were also considered: MRDx code for lower-limb osteomyelitis (osteomyelitis group); lower-limb ulceration (LLU group); or lower-limb atherosclerotic gangrene (atherosclerosis group)-each in conjunction with a non-MRDx DSFUG code on the same DAD record. From all eligible DAD records, random samples were drawn for each coding group. DAD records were independently compared by a masked reviewer who manually abstracted data from the entire hospital record (reference standard). The PPV and 95% CI were generated.Results: Out of 1,460 hospitalizations, a total of 300, 50, 33 and seven records were included from the DSFUG, osteomyelitis, LLU and atherosclerosis samples, respectively. Compared to the reference standard, the PPV for all 390 records was 88.5% (95% CI 84.9 to 91.5). The DSFUG group had the highest PPV (90.0%, 95% CI 86.0 to 93.2), followed by the atherosclerosis (85.7%, 95% CI 42.1 to 99.6), LLU (84.9%, 95% CI 68.1 to 94.9) and osteomyelitis (82.0%, 95% CI 68.6 to 91.4) groups.Conclusion: Based on data from a Canadian tertiary care hospital, the specified coding algorithms can be used to identify and study the management and outcomes of people hospitalized with a DFU in Ontario. [ABSTRACT FROM AUTHOR]

Background: Reduced use of the emergency department during the COVID-19 pandemic may result in increased disease acuity when patients do seek health care services. We sought to evaluate emergency department visits for common abdominal and gynecologic conditions before and at the beginning of the pandemic to determine whether changes in emergency department attendance had serious consequences for patients.Methods: We conducted a population-based analysis using administrative data to evaluate the weekly rate of emergency department visits pre-COVID-19 (Jan. 1-Mar. 10, 2020) and during the beginning of the COVID-19 pandemic (Mar. 11-June 30, 2020), compared with a historical control period (Jan. 1-July 1, 2019). All residents of Ontario, Canada, presenting to the emergency department with appendicitis, cholecystitis, ectopic pregnancy or miscarriage were included. We evaluated weekly incidence rate ratios (IRRs) of emergency department visits, management strategies and clinical outcomes.Results: Across all study periods, 39 691 emergency department visits met inclusion criteria (40.2 % appendicitis, 32.1% miscarriage, 21.3% cholecystitis, 6.4% ectopic pregnancy). Baseline characteristics of patients presenting to the emergency department did not vary across study periods. After an initial reduction in emergency department visits, presentations for cholecystitis and ectopic pregnancy quickly returned to expected levels. However, presentations for appendicitis and miscarriage showed sustained reductions (IRR 0.61-0.80), with 1087 and 984 fewer visits, respectively, after the start of the pandemic, relative to 2019. Management strategies, complications and mortality rates were similar across study periods for all conditions.Interpretation: Although our study showed evidence of emergency department avoidance in Ontario during the first wave of the COVID-19 pandemic, no adverse consequences were evident. Emergency care and outcomes for patients were similar before and during the pandemic. [ABSTRACT FROM AUTHOR]

Background: Use of neuraxial anaesthesia for open abdominal aortic aneurysm repair is postulated to reduce mortality and morbidity. This study aimed to determine the 90-day outcomes after elective open abdominal aortic aneurysm repair in patients receiving combined general and neuraxial anaesthesia vs general anaesthesia alone.Methods: A retrospective population-based cohort study was conducted from 2003 to 2016. All patients ≥40 yr old undergoing open abdominal aortic aneurysm repair were included. The propensity score was used to construct inverse probability of treatment weighted regression models to assess differences in 90-day outcomes.Results: A total of 10 447 elective open abdominal aortic aneurysm repairs were identified; 9003 (86%) patients received combined general and neuraxial anaesthesia and 1444 (14%) received general anaesthesia alone. Combined anaesthesia was associated with significantly lower hazards for all-cause mortality (hazard ratio [HR]=0.47; 95% confidence interval [CI], 0.37-0.61) and major adverse cardiovascular events (HR=0.72; 95% CI, 0.60-0.86). Combined patients were at lower odds for acute kidney injury (odds ratio [OR]=0.66; 95% CI, 0.49-0.89), respiratory failure (OR=0.41; 95% CI, 0.36-0.47), and limb complications (OR=0.30; 95% CI, 0.25-0.37), with higher odds of being discharged home (OR=1.32; 95% CI, 1.15-1.51). Combined anaesthesia was also associated with significant mechanical ventilation and ICU and hospital length of stay benefits.Conclusions: Combined general and neuraxial anaesthesia in elective open abdominal aortic aneurysm repair is associated with reduced 90-day mortality and morbidity. Neuraxial anaesthesia should be considered as a routine adjunct to general anaesthesia for elective open abdominal aortic aneurysm repair. [ABSTRACT FROM AUTHOR]

Background: Patients who undergo vascular surgery are burdened by high early readmission rates. We examined the frequency and cause of early readmissions after elective and emergent admission to the vascular surgery service at our institution to identify modifiable targets for quality improvement.Methods: Over a 5-year period, all patients admitted and readmitted to the vascular surgery service were identified. Medical records were then individually reviewed to identify baseline characteristics from the index admission and the most responsible diagnosis for readmission within 28 days of discharge.Results: Of a total of 3324 patients, 421 (12.7%) were readmitted to our institution within 28 days of discharge. Forty-seven were found to have more than 1 readmission following their index admission. The readmission rate ranged from 11.8% to 14.1% over the 5-year study period, resulting in an average readmission rate of 12.7%. There were similar rates for men (12.9%) and women (12.3%). Of the readmitted cases, 236 (63.1%) were unplanned readmissions. The most common diagnoses for unplanned readmissions were worsening of peripheral arterial disease status including complications related to peripheral bypass graft (30.9%), surgical site infections (15.3%) and nonsurgical infections (14.8%).Conclusion: To reduce readmission rates effectively, institutions must identify highrisk patients. In our study cohort, the most frequent pathology resulting in readmission was peripheral arterial disease. The most frequent preventable reason for readmission was surgical site infection. Interventions focused on early assessment of clinical status and wounds in addition to avoidance of infectious complications could help reduce readmission rates. Preventive resources can be efficiently targeted by focusing on subgroups at risk for readmission. [ABSTRACT FROM AUTHOR]

Background: The evolving clinical burden of limb loss secondary to diabetes and peripheral artery disease remains poorly characterized. We sought to examine secular trends in the rate of lower-extremity amputations related to diabetes, peripheral artery disease or both.Methods: We included all individuals aged 40 years and older who underwent lower-extremity amputations related to diabetes or peripheral artery disease in Ontario, Canada (2005-2016). We identified patients and amputations through deterministic linkage of administrative health databases. Quarterly rates (per 100 000 individuals aged ≥ 40 yr) of any (major or minor) amputation and of major amputations alone were calculated. We used time-series analyses with exponential smoothing models to characterize secular trends and forecast 2 years forward in time.Results: A total of 20 062 patients underwent any lower-extremity amputation, of which 12 786 (63.7%) underwent a major (above ankle) amputation. Diabetes was present in 81.8%, peripheral artery disease in 93.8%, and both diabetes and peripheral artery disease in 75.6%. The rate of any amputation initially declined from 9.88 to 8.62 per 100 000 between Q2 of 2005 and Q4 of 2010, but increased again by Q1 of 2016 to 10.0 per 100 000 (p = 0.003). We observed a significant increase in the rate of any amputation among patients with diabetes, peripheral artery disease, and both diabetes and peripheral artery disease. Major amputations did not significantly change among patients with diabetes, peripheral artery disease or both.Interpretation: Lower-extremity amputations related to diabetes, peripheral artery disease or both have increased over the last decade. These data support renewed efforts to prevent and decrease the burden of limb loss. [ABSTRACT FROM AUTHOR]

Purpose: To determine the positive predictive values (PPV) of Ontario administrative data codes for the identification of open (OSR) and endovascular (EVAR) repairs of elective (eAAA) and ruptured (rAAA) abdominal aortic aneurysms. Methods: We randomly identified 319 eAAA and rAAA repairs at two Toronto hospitals between April 2003 and March 2015, using administrative health data in Ontario, Canada. International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes I71.3 and I71.4, were used to identify rAAA and eAAA patients, respectively. A blinded retrospective chart review was conducted and served as the gold standard comparator. Re-abstracted records were compared to Canadian Classification of Health Interventions (CCI) and Ontario Health Insurance Plan (OHIP) codes in the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) and OHIP databases. We calculated the PPV and 95% confidence intervals (95% CI) of individual and combined procedure and billing codes for elective and ruptured OSR and EVAR (eOSR, eEVAR, rOSR, and rEVAR). Results: Permutation of codes allowed identification of eOSR with 95% PPV (95% CI 88, 98), eEVAR with 96% PPV (95% CI 90, 99), rOSR with 87% PPV (95% CI 79, 93) and rEVAR with 91% PPV (95% CI 59, 100). Conclusions: Diagnostic, procedure and billing code combinations allow identification of eOSR, eEVAR, rOSR and rEVAR patients in Ontario administrative data with a high degree of certainty. [ABSTRACT FROM AUTHOR]

Background: Rates of hospital readmission following surgery can serve as a marker for quality of care. The aim of this study was to establish the rates and causes of readmission and emergency department visits after vascular surgery and to understand how these patients are managed.Methods: We conducted a prospective observational cohort study including all inpatients who underwent major vascular surgery between September 2015 and June 2016 at a tertiary vascular care centre in Toronto. Patients were followed at 30 days after discharge via telephone interview.Results: We enrolled 133 patients (94 men [70.7%] and 39 women [29.3%] with a mean age of 65.3 years). The most common index admission diagnosis was peripheral artery disease (67 patients [50.4%]). At 30 days, 19 patients (14.8%) had been readmitted or had visited the emergency department, most commonly after lower extremity revascularization (19.4%). Ten patients were readmitted a mean of 16.8 days following discharge; surgical site infection was the most common cause for readmission (3 patients). The most common treatment was antimicrobial therapy (4 patients). The mean hospital length of stay was 14.4 days. Nine patients presented to the emergency department a mean of 10.6 days after discharge; 6 reported a wound issue, and most (6 of 9) were managed with oral antibiotic treatment.Conclusion: Early readmission/emergency department visits after lower extremity revascularization surgery in patients with peripheral artery disease are common and are often due to surgical site infection or wound-related issues. Follow-up within 7-10 days and a specialized wound care team may help reduce the occurrence of these events. [ABSTRACT FROM AUTHOR]

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.)

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.)

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: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data., Methods: The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement., Results: Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups., Conclusions: We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes., Competing Interests: Disclosures None., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.)

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.

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.)

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: 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.)

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.)

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.)

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.)

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.)

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: 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.)

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.)

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.)