Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning.

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.

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

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data.

OBJECTIVE: Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD-related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group. METHODS: We performed a prognostic study using a prospectively recruited cohort of patients with PAD (n = 569). Demographic/clinical data were recorded including sex, age, comorbidities, previous procedures, and medications. Plasma concentrations of three biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP], fatty acid binding protein 3 [FABP3], and FABP4) were measured at baseline. The cohort was followed for 3 years. MALE was the primary outcome (composite of open/endovascular vascular intervention or major amputation). We trained three machine learning models with 10-fold cross-validation using demographic, clinical, and biomarker data (random forest, decision trees, and Extreme Gradient Boosting [XGBoost]) to predict 3-year MALE in patients. Area under the receiver operating characteristic curve (AUROC) was the primary model evaluation metric. RESULTS: Three-year MALE was observed in 162 patients (29%). XGBoost was the top-performing predictive model for 3-year MALE, achieving the following performance metrics: AUROC = 0.88 (95% confidence interval [CI], 0.84-0.94); sensitivity, 88%; specificity, 84%; positive predictive value, 83%; and negative predictive value, 91% on test set data. On an independent validation cohort of patients with PAD, XGBoost attained an AUROC of 0.87 (95% CI, 0.82-0.90). The 10 most important predictors of 3-year MALE consisted of: (1) FABP3; (2) FABP4; (3) age; (4) NT-proBNP; (5) active smoking; (6) diabetes; (7) hypertension; (8) dyslipidemia; (9) coronary artery disease; and (10) sex. CONCLUSIONS: We built robust machine learning algorithms that accurately predict 3-year MALE in patients with PAD using demographic, clinical, and novel biomarker data. Our algorithms can support risk stratification of patients with PAD for additional vascular evaluation and early aggressive medical management, thereby improving outcomes. Further validation of our models for clinical implementation is warranted.

Temporal trends in hemodialysis access creation during the fistula first era.

OBJECTIVE: Although forearm arteriovenous fistulas (AVFs) are the preferred initial vascular access for hemodialysis based on national guidelines, there are no population-level studies evaluating trends in creation of forearm vs upper arm AVFs and arteriovenous grafts (AVGs). The purpose of this study was to report temporal trends in first-time permanent hemodialysis access type, and to assess the effect of national initiatives on rates of AVF placement. METHODS: Retrospective cross-sectional study (2012-2022) utilizing the Vascular Quality Initiative database. All patients older than 18 years with creation of first-time upper extremity surgical hemodialysis access were included. Anatomic location of the AVF or AVG (forearm vs upper arm) was defined based on inflow artery, outflow vein, and presumed cannulation zone. Primary analysis examined temporal trends in rates of forearm vs upper arm AVFs and AVGs using time series analyses (modified Mann-Kendall test). Subgroup analyses examined rates of access configuration stratified by age, sex, race, dialysis, and socioeconomic status. Interrupted time series analysis was performed to assess the effect of the 2015 Fistula First Catheter Last initiative on rates of AVFs. RESULTS: Of the 52,170 accesses, 57.9% were upper arm AVFs, 25.2% were forearm AVFs, 15.4% were upper arm AVGs, and 1.5% were forearm AVGs. From 2012 to 2022, there was no significant change in overall rates of forearm or upper arm AVFs. There was a numerical increase in upper arm AVGs (13.9 to 18.2 per 100; P = .09), whereas forearm AVGs significantly declined (1.8 to 0.7 per 100; P = .02). In subgroup analyses, we observed a decrease in forearm AVFs among men (33.1 to 28.7 per 100; P = .04) and disadvantaged (Area Deprivation Index percentile ≥50) patients (29.0 to 20.7 per 100; P = .04), whereas female (17.2 to 23.1 per 100; P = .03), Black (15.6 to 24.5 per 100; P < .01), elderly (age ≥80 years) (18.7 to 32.5 per 100; P < .01), and disadvantaged (13.6 to 20.5 per 100; P < .01) patients had a significant increase in upper arm AVGs. The Fistula First Catheter Last initiative had no effect on the rate of AVF placement (83.2 to 83.7 per 100; P=.37). CONCLUSIONS: Despite national initiatives to promote autogenous vascular access, the rates of first-time AVFs have remained relatively constant, with forearm AVFs only representing one-quarter of all permanent surgical accesses. Furthermore, elderly, Black, female, and disadvantaged patients saw an increase in upper arm AVGs. Further efforts to elucidate factors associated with forearm AVF placement, as well as potential physician, center, and regional variation is warranted.

Using machine learning to predict outcomes following suprainguinal bypass.

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

Opioid-related deaths between 2019 and 2021 across 9 Canadian provinces and territories.

BACKGROUND: The drug toxicity crisis continues to accelerate across Canada, with rapid increases in opioid-related harms following the onset of the COVID-19 pandemic. We sought to describe trends in the burden of opioid-related deaths across Canada throughout the pandemic, comparing these trends by province or territory, age, and sex. METHODS: We conducted a repeated cross-sectional analysis of accidental opioid-related deaths between Jan. 1, 2019, and Dec. 31, 2021, across 9 Canadian provinces and territories using aggregated national data. Our primary measure was the burden of premature opioid-related death, measured by potential years of life lost. Our secondary measure was the proportion of all deaths attributable to opioids; we used the Cochrane-Armitage test for trend to compare proportions. RESULTS: Between 2019 and 2021, the annual number of opioid-related deaths increased from 3007 to 6222 and years of life lost increased from 126 115 to 256 336 (from 3.5 to 7.0 yr of life lost per 1000 population). In 2021, the highest number of years of life lost was among males (181 525 yr) and people aged 30-39 years (87 045 yr). In 2019, we found that 1.7% of all deaths among those younger than 85 years were related to opioids, rising to 3.2% in 2021. Significant increases in the proportion of deaths related to opioids were observed across all age groups (p < 0.001), representing 29.3% and 29.0% of deaths among people aged 20-29 and 30-39 years in 2021, respectively. INTERPRETATION: Across Canada, the burden of premature opioid-related deaths doubled between 2019 and 2021, representing more than one-quarter of deaths among younger adults. The disproportionate loss of life in this demographic group highlights the critical need for targeted prevention efforts.

Impact of COVID-19 pandemic on prescription stimulant use among children and youth: a population-based study.

COVID-19 associated public health measures and school closures exacerbated symptoms in some children and youth with attention-deficit hyperactivity disorder (ADHD). Less well understood is how the pandemic influenced patterns of prescription stimulant use. We conducted a population-based study of stimulant dispensing to children and youth ≤ 24 years old between January 1, 2013, and June 30, 2022. We used structural break analyses to identify the pandemic month(s) when changes in the dispensing of stimulants occurred. We used interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected stimulant use. Our main outcome was the change in the monthly rate of stimulant use per 100,000 children and youth. Following an initial immediate decline of 60.1 individuals per 100,000 (95% confidence interval [CI] - 99.0 to - 21.2), the monthly rate of stimulant dispensing increased by 11.8 individuals per 100,000 (95% CI 10.0-13.6), with the greatest increases in trend observed among females, individuals in the highest income neighbourhoods, and those aged 20 to 24. Observed rates were between 3.9% (95% CI 1.7-6.2%) and 36.9% (95% CI 34.3-39.5%) higher than predicted among females from June 2020 onward and between 7.1% (95% CI 4.2-10.0%) and 50.7% (95% CI 47.0-54.4%) higher than expected among individuals aged 20-24 from May 2020 onward. Additional research is needed to ascertain the appropriateness of stimulant use and to develop strategies supporting children and youth with ADHD during future periods of long-term stressors.

Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion-symptom mapping.

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion-symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof-of-concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single-outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well-defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single-outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single-outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single-outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.

Regional variation in lower extremity revascularization and amputation for peripheral artery disease.

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.

Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning.

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.

Using machine learning to predict outcomes following carotid endarterectomy.

OBJECTIVE: Prediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA. METHODS: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022. We identified 71 potential predictor variables (features) from the index hospitalization (43 preoperative [demographic/clinical], 21 intraoperative [procedural], and 7 postoperative [in-hospital complications]). The primary outcome was stroke or death at 1 year following CEA. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, insurance status, symptom status, and urgency of surgery. RESULTS: Overall, 166,369 patients underwent CEA during the study period. In total, 7749 patients (4.7%) had the primary outcome of stroke or death at 1 year. Patients with an outcome were older with more comorbidities, had poorer functional status, and demonstrated higher risk anatomic features. They were also more likely to undergo intraoperative surgical re-exploration and have in-hospital complications. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.90 (95% confidence interval [CI], 0.89-0.91). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67), and existing tools in the literature demonstrate AUROCs ranging from 0.58 to 0.74. Our XGBoost models maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.90 (95% CI, 0.89-0.91) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.15 (preoperative), 0.14 (intraoperative), and 0.11 (postoperative). Of the top 10 predictors, eight were preoperative features, including comorbidities, functional status, and previous procedures. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following CEA. Our algorithms perform better than logistic regression and existing tools, and therefore, have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes.

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

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

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

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

The influence of diabetes on temporal trends in lower extremity revascularisation and amputation for peripheral artery disease: A population-based repeated cross-sectional analysis.

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.

Prescription of oxycodone versus codeine after childbirth and risk of persistent opioid use: a population-based cohort study.

BACKGROUND: Oxycodone is increasingly prescribed for postpartum analgesia in lieu of codeine owing to concerns regarding the neonatal safety of codeine during lactation. We examined whether initiation of oxycodone after delivery was associated with an increased risk of persistent opioid use relative to initiation of codeine. METHODS: We conducted a population-based cohort study of people who filled a prescription for either codeine or oxycodone within 7 days of discharge from hospital after delivery between Sept. 1, 2012, and June 30, 2020. The primary outcome was persistent opioid use, defined as 1 or more additional prescriptions for an opioid within 90 days of the first postpartum prescription and 1 or more additional prescriptions in the 91 to 365 days thereafter. We used inverse probability of treatment weighting to assess the risk of persistent postpartum opioid use, comparing people who initiated oxycodone with those who initiated codeine. RESULTS: Over the 8-year study period, we identified 70 607 people who filled an opioid prescription within 7 days of discharge from hospital: 21 308 (30.2%) received codeine and 49 299 (69.8%) oxycodone. Compared with people who filled a prescription for codeine, receipt of oxycodone was not associated with persistent opioid use (relative risk [RR] 1.04, 95% confidence interval [CI] 0.91-1.20). We found an association between a prescription for oxycodone and persistent use after vaginal delivery (RR 1.63, 95% CI 1.31-2.03), but not after cesarean delivery (RR 0.85, 95% CI 0.73-1.00). INTERPRETATION: Initiation of oxycodone (v. codeine) was not associated with an increased risk of persistent opioid use, except after vaginal delivery.

Impact of a Publicly-Funded Pharmacare Program on Prescription Stimulant use Among Children and Youth: A Population-Based Observational Natural Experiment.

OBJECTIVE: Stimulants are first-line pharmacotherapy for individuals with attention-deficit hyperactivity disorder. However, disparities in drug coverage may contribute to inequitable treatment access. In January 2018, the government of Ontario, Canada, implemented a publicly-funded program (OHIP+) providing universal access to medications at no cost to children and youth between the ages of 0 and 24. In April 2019, the program was amended to cover only children and youth without private insurance. We studied whether these policy changes were associated with changes in prescription stimulant dispensing to Ontario children and youth. METHODS: We conducted a population-based observational natural experiment study of stimulant dispensing to children and youth in Ontario between January 2013 and March 2020. We used interventional autoregressive integrated moving average models to estimate the association between OHIP+ and its subsequent modification with stimulant dispensing trends. RESULTS: The implementation of OHIP+ was associated with a significant immediate increase in the monthly rate of stimulant dispensing of 53.6 individuals per 100,000 population (95% confidence interval [CI], 36.8 to 70.5 per 100,000) and a 14.2% (95% CI, 12.8% to 15.6%) relative percent increase in stimulant dispensing rates between December 2017 and March 2019 (1198.6 vs. 1368.7 per 100,000 population). The April 2019 OHIP+ program amendment was associated with an increase in monthly stimulant dispensing trends of 10.2 individuals per 100,000 population (95% CI, 5.0 to 15.5), with rates increasing 7.5% (95% CI, 6.2% to 8.7%) between March 2019 and March 2020 (1368.7 vs. 1470.8 per 100,000 population). These associations were most pronounced among males, children and youth living in the highest income neighbourhoods and individuals aged 20 to 24. CONCLUSION: A publicly-funded pharmacare program was associated with more children and youth being dispensed stimulants.

Geographic variation and sociodemographic correlates of prescription psychotropic drug use among children and youth in Ontario, Canada: a population-based study.

BACKGROUND: Population-based research examining geographic variability in psychotropic medication dispensing to children and youth and the sociodemographic correlates of such variation is lacking. Variation in psychotropic use could reflect disparities in access to non-pharmacologic interventions and identify potentially concerning use patterns. METHODS: We conducted a population-based study of all Ontario residents aged 0 to 24 years who were dispensed a benzodiazepine, stimulant, antipsychotic or antidepressant between January 1, 2018, and December 31, 2018. We conducted small-area variation analyses and identified determinants of dispensing using negative binomial generalized estimating equation models. RESULTS: The age- and sex-standardized rate of psychotropic dispensing to children and youth was 76.8 (range 41.7 to 144.4) prescriptions per 1000 population, with large variation in psychotropic dispensing across Ontario's census divisions. Males had higher antipsychotic [rate ratio (RR) 1.40; 95% confidence interval (CI) 1.36 to 1.44) and stimulant (RR 1.75; 95% CI 1.70 to 1.80) dispensing rates relative to females, with less use of benzodiazepines (RR 0.85; 95% CI 0.83 to 0.88) and antidepressants (RR 0.81; 95% CI 0.80 to 0.82). Lower antipsychotic dispensing was observed in the highest income neighbourhoods (RR 0.72; 95% CI 0.70 to 0.75) relative to the lowest. Benzodiazepine (RR 1.12; 95% CI 1.01 to 1.24) and stimulant (RR 1.11; 95% CI 1.01 to 1.23) dispensing increased with the density of mental health services in census divisions, whereas antipsychotic use decreased (RR 0.82; 95% CI 0.73 to 0.91). The regional density of child and adolescent psychiatrists and developmental pediatricians (RR 1.00; 95% CI 0.99 to 1.01) was not associated with psychotropic dispensing. CONCLUSION: We found significant variation in psychotropic dispensing among young Ontarians. Targeted investment in regions with long wait times for publicly-funded non-pharmacological interventions and novel collaborative service models may minimize variability and promote best practices in using psychotropics among children and youth.

Impact of a publicly-funded pharmacare program policy on benzodiazepine dispensing among children and youth: a population-based natural experiment.

BACKGROUND: In January 2018, the Government of Ontario, Canada, initiated a universal pharmacare program (OHIP+) for all individuals aged 24 years and younger. In April 2019, the program was amended to cover only children and youth without private insurance. Because benzodiazepines are commonly prescribed to children and youth despite their potential hazards, we examined whether changes in publicly-funded drug coverage influenced benzodiazepine dispensing trends in this demographic. METHODS: We conducted a population-based natural experiment study of benzodiazepine dispensing to children and youth in Ontario between January 2013 and March 2020. We used interventional autoregressive integrated moving average models to estimate the impact of OHIP + and its subsequent modification on these trends. RESULTS: The implementation of OHIP + was associated with an immediate increase in the monthly rate of benzodiazepine dispensing of 12.9 individuals per 100,000 population (95% confidence interval [CI]; 7.5 to 18.3 per 100,000). Benzodiazepine dispensing rates rose from 214.2 to 241.5 per 100,000 from December 2017 to March 2019, a 12.8% (95% CI 9.6-16.0%) increase. In stratified analyses, increases were most pronounced among females, children and youth living in the lowest income neighbourhoods and individuals aged 20 to 24. The April 2019 modification to OHIP + was not associated with changes in monthly benzodiazepine dispensing trends (0.39 individuals per 100,000; 95% CI -1.3 to 2.1 per 100,000). However, rates remained elevated relative to the period preceding OHIP + implementation. CONCLUSIONS: Implementation of a publicly-funded pharmacare program resulted in more children and youth being prescribed benzodiazepines.

SLEAP SMART (Sleep Apnea Screening Using Mobile Ambulatory Recorders After TIA/Stroke): A Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Poststroke/transient ischemic attack obstructive sleep apnea (OSA) is prevalent, linked with numerous unfavorable health consequences, but remains underdiagnosed. Reasons include patient inconvenience and costs associated with use of in-laboratory polysomnography (iPSG), the current standard tool. Fortunately, home sleep apnea testing (HSAT) can accurately diagnose OSA and is potentially more convenient and cost-effective compared with iPSG. Our objective was to assess whether screening for OSA in patients with stroke/transient ischemic attack using HSAT, compared with standard of care using iPSG, increased diagnosis and treatment of OSA, improved clinical outcomes and patient experiences with sleep testing, and was a cost-effective approach. METHODS: We consecutively recruited 250 patients who had sustained a stroke/transient ischemic attack within the past 6 months. Patients were randomized (1:1) to use of (1) HSAT versus (2) iPSG. Patients completed assessments and questionnaires at baseline and 6-month follow-up appointments. Patients diagnosed with OSA were offered continuous positive airway pressure. The primary outcome was compared between study arms via an intention-to-treat analysis. RESULTS: At 6 months, 94 patients completed HSAT and 71 patients completed iPSG. A significantly greater proportion of patients in the HSAT arm were diagnosed with OSA (48.8% versus 35.2%, P=0.04) compared with the iPSG arm. Furthermore, patients assigned to HSAT, compared with iPSG, were more likely to be prescribed continuous positive airway pressure (40.0% versus 27.2%), report significantly reduced sleepiness, and a greater ability to perform daily activities. Moreover, a significantly greater proportion of patients reported a positive experience with sleep testing in the HSAT arm compared with the iPSG arm (89.4% versus 31.1%). Finally, a cost-effectiveness analysis revealed that HSAT was economically attractive for the detection of OSA compared with iPSG. CONCLUSIONS: In patients with stroke/transient ischemic attack, use of HSAT compared with iPSG increases the rate of OSA diagnosis and treatment, reduces daytime sleepiness, improves functional outcomes and experiences with sleep testing, and could be an economically attractive approach. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02454023.

Factors Associated with Re-Resection in T1 Bladder Cancer: Identifying Patients Who Do Not Receive Guideline-Concordant Care at the Population Level.

PURPOSE: Prior research has shown that concordance with the guideline-endorsed recommendation to re-resect patients diagnosed with primary T1 bladder cancer (BC) is suboptimal. Therefore, the aim of this population-based study was to identify factors associated with re-resection in T1 BC. MATERIALS AND METHODS: We linked province-wide BC pathology reports (January 2001 to December 2015) with health administrative data sources to derive an incident cohort of patients diagnosed with T1 BC in the province of Ontario, Canada. Re-resection was ascertained by a billing claim for transurethral resection within 2 to 8 weeks after the initial resection, accounting for system-related wait times. Multivariable logistic regression analysis accounting for the clustered nature of the data was used to identify various patient-level and surgeon-level factors associated with re-resection. P values <0.05 were considered statistically significant (2-sided). RESULTS: We identified 7,373 patients who fulfilled the inclusion criteria. Overall, 1,678 patients (23%) underwent re-resection. Patients with a more aggressive tumor profile and individuals without sufficiently sampled muscularis propria as well as younger, healthier and socioeconomically advantaged patients were more likely to receive re-resection (all p <0.05). In addition, more senior, lower volume and male surgeons were less likely to perform re-resection for their patients (all p <0.05). CONCLUSIONS: Only a minority of all patients received re-resection within 2 to 8 weeks after initial resection. To improve the access to care for potentially underserved patients, we suggest specific knowledge translation/exchange interventions that also include equity aspects besides further promotion of evidence-based instead of eminence-based medicine.