Trial of an Intervention to Improve Acute Heart Failure Outcomes.

BACKGROUND: Patients with acute heart failure are frequently or systematically hospitalized, often because the risk of adverse events is uncertain and the options for rapid follow-up are inadequate. Whether the use of a strategy to support clinicians in making decisions about discharging or admitting patients, coupled with rapid follow-up in an outpatient clinic, would affect outcomes remains uncertain. METHODS: In a stepped-wedge, cluster-randomized trial conducted in Ontario, Canada, we randomly assigned 10 hospitals to staggered start dates for one-way crossover from the control phase (usual care) to the intervention phase, which involved the use of a point-of-care algorithm to stratify patients with acute heart failure according to the risk of death. During the intervention phase, low-risk patients were discharged early (in ≤3 days) and received standardized outpatient care, and high-risk patients were admitted to the hospital. The coprimary outcomes were a composite of death from any cause or hospitalization for cardiovascular causes within 30 days after presentation and the composite outcome within 20 months. RESULTS: A total of 5452 patients were enrolled in the trial (2972 during the control phase and 2480 during the intervention phase). Within 30 days, death from any cause or hospitalization for cardiovascular causes occurred in 301 patients (12.1%) who were enrolled during the intervention phase and in 430 patients (14.5%) who were enrolled during the control phase (adjusted hazard ratio, 0.88; 95% confidence interval [CI], 0.78 to 0.99; P = 0.04). Within 20 months, the cumulative incidence of primary-outcome events was 54.4% (95% CI, 48.6 to 59.9) among patients who were enrolled during the intervention phase and 56.2% (95% CI, 54.2 to 58.1) among patients who were enrolled during the control phase (adjusted hazard ratio, 0.95; 95% CI, 0.92 to 0.99). Fewer than six deaths or hospitalizations for any cause occurred in low- or intermediate-risk patients before the first outpatient visit within 30 days after discharge. CONCLUSIONS: Among patients with acute heart failure who were seeking emergency care, the use of a hospital-based strategy to support clinical decision making and rapid follow-up led to a lower risk of the composite of death from any cause or hospitalization for cardiovascular causes within 30 days than usual care. (Funded by the Ontario SPOR Support Unit and others; COACH ClinicalTrials.gov number, NCT02674438.).

Stroke risk in women with atrial fibrillation.

BACKGROUND AND AIMS: Female sex is associated with higher rates of stroke in atrial fibrillation (AF) after adjustment for other CHA2DS2-VASc factors. This study aimed to describe sex differences in age and cardiovascular care to examine their relationship with stroke hazard in AF. METHODS: Population-based cohort study using administrative datasets of people aged ≥66 years diagnosed with AF in Ontario between 2007 and 2019. Cause-specific hazard regression was used to estimate the adjusted hazard ratio (HR) for stroke associated with female sex over a 2-year follow-up. Model 1 included CHA2DS2-VASc factors, with age modelled as 66-74 vs. ≥ 75 years. Model 2 treated age as a continuous variable and included an age-sex interaction term. Model 3 further accounted for multimorbidity and markers of cardiovascular care. RESULTS: The cohort consisted of 354 254 individuals with AF (median age 78 years, 49.2% female). Females were more likely to be diagnosed in emergency departments and less likely to receive cardiologist assessments, statins, or LDL-C testing, with higher LDL-C levels among females than males. In Model 1, the adjusted HR for stroke associated with female sex was 1.27 (95% confidence interval 1.21-1.32). Model 2 revealed a significant age-sex interaction, such that female sex was only associated with increased stroke hazard at age >70 years. Adjusting for markers of cardiovascular care and multimorbidity further decreased the HR, so that female sex was not associated with increased stroke hazard at age ≤80 years. CONCLUSION: Older age and inequities in cardiovascular care may partly explain higher stroke rates in females with AF.

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.

Ambulatory Cardiology or General Internal Medicine Assessment Prior to Scheduled Major Vascular Surgery is Associated with Improved Outcomes.

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.

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.

Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization.

INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. OBJECTIVES: To compare models developed using ML with those developed using CSM to predict 30-day readmission for cardiovascular and noncardiovascular causes in HF patients. METHODS: We retrospectively enrolled 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests for competing risks (RSF-CR). Models were evaluated in the validation set using both discrimination and calibration metrics. RESULTS: In the validation sample of 3602 patients, RSF-CR (c-statistic=0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic=0.621) for 30-day cardiovascular readmission. In contrast, for 30-day noncardiovascular readmission, the Fine-Gray model (c-statistic=0.641) slightly outperformed the RSF-CR model (c-statistic=0.632). For both outcomes, The Fine-Gray model displayed better calibration than RSF-CR using calibration plots of observed vs predicted risks across the deciles of predicted risk. CONCLUSIONS: Fine-Gray models had similar discrimination but superior calibration to the RSF-CR model, highlighting the importance of reporting calibration metrics for ML-based prediction models. The discrimination was modest in all readmission prediction models regardless of the methods used.

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.

Days alive and out of hospital for adult female and male cardiac surgery patients: a population-based cohort study.

BACKGROUND: Research shows women experience higher mortality than men after cardiac surgery but information on sex-differences during postoperative recovery is limited. Days alive and out of hospital (DAH) combines death, readmission and length of stay, and may better quantify sex-differences during recovery. This main objective is to evaluate (i) how DAH at 30-days varies between sex and surgical procedure, (ii) DAH responsiveness to patient and surgical complexity, and (iii) longer-term prognostic value of DAH. METHODS: We evaluated 111,430 patients (26% female) who underwent one of three types of cardiac surgery (isolated coronary artery bypass [CABG], isolated non-CABG, combination procedures) between 2009 - 2019. Primary outcome was DAH at 30 days (DAH30), secondary outcomes were DAH at 90 days (DAH90) and 180 days (DAH180). Data were stratified by sex and surgical group. Unadjusted and risk-adjusted analyses were conducted to determine the association of DAH with patient-, surgery-, and hospital-level characteristics. Patients were divided into two groups (below and above the 10th percentile) based on the number of days at DAH30. Proportion of patients below the 10th percentile at DAH30 that remained in this group at DAH90 and DAH180 were determined. RESULTS: DAH30 were lower for women compared to men (22 vs. 23 days), and seen across all surgical groups (isolated CABG 23 vs. 24, isolated non-CABG 22 vs. 23, combined surgeries 19 vs. 21 days). Clinical risk factors including multimorbidity, socioeconomic status and surgical complexity were associated with lower DAH30 values, but women showed lower values of DAH30 compared to men for many factors. Among patients in the lowest 10th percentile at DAH30, 80% of both females and males remained in the lowest 10th percentile at 90 days, while 72% of females and 76% males remained in that percentile at 180 days. CONCLUSION: DAH is a responsive outcome to differences in patient and surgical risk factors. Further research is needed to identify new care pathways to reduce disparities in outcomes between male and female patients.

Development and Validation of the CANHEART Population-Based Laboratory Prediction Models for Atherosclerotic Cardiovascular Disease.

BACKGROUND: Prediction of atherosclerotic cardiovascular disease (ASCVD) in primary prevention assessments exclusively with laboratory results may facilitate automated risk reporting and improve uptake of preventive therapies. OBJECTIVE: To develop and validate sex-specific prediction models for ASCVD using age and routine laboratory tests and compare their performance with that of the pooled cohort equations (PCEs). DESIGN: Derivation and validation of the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) Lab Models. SETTING: Population-based cohort study in Ontario, Canada. PARTICIPANTS: A derivation and internal validation cohort of adults aged 40 to 75 years without cardiovascular disease from April 2009 to December 2015; an external validation cohort of primary care patients from January 2010 to December 2014. MEASUREMENTS: Age and laboratory predictors measured in the outpatient setting included serum total cholesterol, high-density lipoprotein cholesterol, triglycerides, hemoglobin, mean corpuscular volume, platelets, leukocytes, estimated glomerular filtration rate, and glucose. The ASCVD outcomes were defined as myocardial infarction, stroke, and death from ischemic heart or cerebrovascular disease within 5 years. RESULTS: Sex-specific models were developed and internally validated in 2 160 497 women and 1 833 147 men. They were well calibrated, with relative differences less than 1% between mean predicted and observed risk for both sexes. The c-statistic was 0.77 in women and 0.71 in men. External validation in 31 697 primary care patients showed a relative difference less than 14% and an absolute difference less than 0.3 percentage points in mean predicted and observed risks for both sexes. The c-statistics for the laboratory models were 0.72 for both sexes and were not statistically significantly different from those for the PCEs in women (change in c-statistic, -0.01 [95% CI, -0.03 to 0.01]) or men (change in c-statistic, -0.01 [CI, -0.04 to 0.02]). LIMITATION: Medication use was not available at the population level. CONCLUSION: The CANHEART Lab Models predict ASCVD with similar accuracy to more complex models, such as the PCEs. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.

Association of Neighborhood-Level Material Deprivation With Atrial Fibrillation Care in a Single-Payer Health Care System: A Population-Based Cohort Study.

BACKGROUND: There are limited data on the association of material deprivation with clinical care and outcomes after atrial fibrillation (AF) diagnosis in jurisdictions with universal health care. METHODS: This was a population-based cohort study of individuals ≥66 years of age with first diagnosis of AF between April 1, 2007, and March 31, 2019, in the Canadian province of Ontario, which provides public funding and prohibits private payment for medically necessary physician and hospital services. Prescription medications are subsidized for residents >65 years of age. The primary exposure was neighborhood material deprivation, a metric derived from Canadian census data to estimate inability to attain basic material needs. Neighborhoods were categorized by quintile from Q1 (least deprived) to Q5 (most deprived). Cause-specific hazards regression was used to study the association of material deprivation quintile with time to AF-related adverse events (death or hospitalization for stroke, heart failure, or bleeding), clinical services (physician visits, cardiac diagnostics), and interventions (anticoagulation, cardioversion, ablation) while adjusting for individual characteristics and regional cardiologist supply. RESULTS: Among 347 632 individuals with AF (median age 79 years, 48.9% female), individuals in the most deprived neighborhoods (Q5) had higher prevalence of cardiovascular disease, risk factors, and noncardiovascular comorbidity relative to residents of the least deprived neighborhoods (Q1). After adjustment, Q5 residents had higher hazards of death (hazard ratio [HR], 1.16 [95% CI, 1.13-1.20]) and hospitalization for stroke (HR, 1.16 [95% CI, 1.07-1.27]), heart failure (HR, 1.14 [95% CI, 1.11-1.18]), or bleeding (HR, 1.16 [95% CI, 1.07-1.25]) relative to Q1. There were small differences across quintiles in primary care physician visits (HR, Q5 versus Q1, 0.91 [95% CI, 0.89-0.92]), echocardiography (HR, Q5 versus Q1, 0.97 [95% CI, 0.96-0.99]), and dispensation of anticoagulation (HR, Q5 versus Q1, 0.97 [95% CI, 0.95-0.98]). There were more prominent disparities for Q5 versus Q1 in cardiologist visits (HR, 0.84 [95% CI, 0.82-0.86]), cardioversion (HR, 0.80 [95% CI, 0.76-0.84]), and ablation (HR, 0.45 [95% CI, 0.30-0.67]). CONCLUSIONS: Despite universal health care and prescription medication coverage, residents of more deprived neighborhoods were less likely to visit cardiologists or receive rhythm control interventions after AF diagnosis, even though they exhibited higher cardiovascular disease burden and higher risk of adverse outcomes.

Quantifying COPD as a risk factor for cardiac disease in a primary prevention cohort.

BACKGROUND: Despite COPD being a risk factor for cardiovascular disease (CVD) and knowing that risk stratification for CVD primary prevention is important, little is known about the real-world risk of CVD among people with COPD with no history of CVD. This knowledge would inform CVD management for people with COPD. The current study aimed to examine the risk of major adverse cardiovascular events (MACE) (including acute myocardial infarction, stroke or cardiovascular death) in a large, complete real-world population with COPD without previous CVD. METHODS: We conducted a retrospective population cohort study using health administrative, medication, laboratory, electronic medical record and other data from Ontario, Canada. People without a history of CVD with and without physician-diagnosed COPD were followed between 2008 and 2016, and cardiac risk factors and comorbidities compared. Sequential cause-specific hazard models adjusting for these factors determined the risk of MACE in people with COPD. RESULTS: Among ∼5.8 million individuals in Ontario aged ≥40 years without CVD, 152 125 had COPD. After adjustment for cardiovascular risk factors, comorbidities and other variables, the rate of MACE was 25% higher in persons with COPD compared with those without COPD (hazard ratio 1.25, 95% CI 1.23-1.27). CONCLUSIONS: In a large real-world population without CVD, people with physician-diagnosed COPD were 25% more likely to have a major CVD event, after adjustment for CVD risk and other factors. This rate is comparable to the rate in people with diabetes and calls for more aggressive CVD primary prevention in the COPD population.

Association of prior outpatient diabetes screening with cardiovascular events and mortality among people with incident diabetes: a population-based cohort study.

BACKGROUND: Outcomes of diabetes screening in contemporary, multi-ethnic populations are unknown. We examined the association of prior outpatient diabetes screening with the risks of cardiovascular events and mortality in Ontario, Canada. METHODS: We conducted a population-based cohort study using administrative databases among adults aged ≥ 20 years with incident diabetes diagnosed during 2014-2016. The exposure was outpatient diabetes screening performed within 3 years prior to diabetes diagnosis. The co-primary outcomes were (1) a composite of all-cause mortality and hospitalization for myocardial infarction, stroke, coronary revascularization, and (2) all-cause mortality (followed up until 2018). We calculated standardized rates of each outcome and conducted cause-specific hazard modelling to determine the adjusted hazard ratio (HR) of the outcomes, adjusting for prespecified confounders and accounting for the competing risk of death. RESULTS: We included 178,753 Ontarians with incident diabetes (70.2% previously screened). Individuals receiving prior screening were older (58.3 versus 53.4 years) and more likely to be women (49.6% versus 40.0%) than previously unscreened individuals. Individuals receiving prior screening had relatively lower standardized event rates than those without prior screening across all outcomes (composite: 12.8 versus 18.1, mortality: 8.2 versus 11.1 per 1000 patient-years). After multivariable adjustment, prior screening was associated with 34% and 32% lower risks of the composite (HR 0.66, 0.63-0.69) and mortality (0.68, 0.64-0.72) outcomes. Among those receiving prior screening, a result in the prediabetes range was associated with lower risks of the composite (0.82, 0.77-0.88) and mortality (0.71, 0.66-0.78) outcomes than a result in the normoglycemic range. CONCLUSIONS: Previously screened individuals with diabetes had lower risks of cardiovascular events and mortality versus previously unscreened individuals. Better risk assessment tools are needed to support wider and more appropriate uptake of diabetes screening, especially among young adults.

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 open abdominal aortic aneurysm repair.

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

Using machine learning to predict outcomes following carotid endarterectomy.

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

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.

Morbidity and healthcare use among mothers of children with cancer: A population-based study.

BACKGROUND: The impact of a child's cancer diagnosis on subsequent maternal physical health is unclear. METHODS: We identified all Ontario children diagnosed less than 18 years with cancer between 1992 and 2017. Linkage to administrative databases identified mothers who were matched to population controls. We identified physical health conditions, acute healthcare use, and preventive healthcare use through validated algorithms using healthcare data, and compared them between exposed (child with cancer) and unexposed mothers. Predictors of health outcomes were assessed among exposed mothers. RESULTS: We identified 5311 exposed mothers and 19,516 matched unexposed mothers. For exposed mothers, median age at last follow-up was 48 years, (interquartile range: 42-53). Exposed mothers had an increased risk of cancer (hazard ratio [HR] 1.2, 95% confidence interval [95% CI]: 1.0-1.5, p = .03), but not of any other adverse physical outcomes or of increased acute healthcare use. Exposed mothers were more likely to receive influenza vaccinations (odds ratio 1.4, 95% CI: 1.3-1.5, p < .0001), and underwent cancer screening at the same rate as unexposed mothers. Among exposed mothers, bereavement was associated with a subsequent increased risk of cancer (HR 1.7, 95% CI: 1.2-2.5, p = .004) and death (HR 2.2, 95% CI: 1.2-4.1, p = .01). CONCLUSION: Mothers of children with cancer are at increased risk of developing cancer, but not of other adverse physical health outcomes, and were equally or more likely to be adherent to preventive healthcare practices. Bereaved mothers were at increased risk of subsequent cancer and death. Interventions targeting specific subpopulations of mothers of children with cancer or focused on screening for specific cancers may be warranted.

Comorbidity-stratified estimates of 30-day mortality risk by age for unvaccinated men and women with COVID-19: a population-based cohort study.

BACKGROUND: The mortality risk following COVID-19 diagnosis in men and women with common comorbidities at different ages has been difficult to communicate to the general public. The purpose of this study was to determine the age at which unvaccinated men and women with common comorbidities have a mortality risk which exceeds that of 75- and 65-year-old individuals in the general population (Phases 1b/1c thresholds of the Centre for Disease Control Vaccine Rollout Recommendations) following COVID-19 infection during the first wave. METHODS: We conducted a population-based retrospective cohort study using linked administrative datasets in Ontario, Canada. We identified all community-dwelling adults diagnosed with COVID-19 between January 1 and October 31st, 2020. Exposures of interest were age (modelled using restricted cubic splines) and the following conditions: major cardiovascular disease (recent myocardial infarction or lifetime history of heart failure); 2) diabetes; 3) hypertension; 4) recent cancer; 5) chronic obstructive pulmonary disease; 6) Stages 4/5 chronic kidney disease (CKD); 7) frailty. Logistic regression in the full cohort was used to estimate the risk of 30-day mortality for 75- and 65-year-old individuals. Analyses were repeated after stratifying by sex and medical condition to determine the age at which 30-day morality risk in strata exceed that of the general population at ages 65 and 75 years. RESULTS: We studied 52,429 individuals (median age 42 years; 52.5% women) of whom 417 (0.8%) died within 30 days. The 30-day mortality risk increased with age, male sex, and comorbidities. The 65- and 75-year-old mortality risks in the general population were exceeded at the youngest age by people with CKD, cancer, and frailty. Conversely, women aged < 65 years who had diabetes or hypertension did not have higher mortality than 65-year-olds in the general population. Most people with medical conditions (except for Stage 4-5 CKD) aged < 45 years had lower predicted mortality than the general population at age 65 years. CONCLUSION: The mortality risk in COVID-19 increases with age and comorbidity but the prognostic implications varied by sex and condition. These observations can support communication efforts and inform vaccine rollout in jurisdictions with limited vaccine supplies.