Implications of Five Different Risk Models In Primary Prevention Guidelines.

BACKGROUND: A lack of consensus exists across guidelines as to which risk model should be used for the primary prevention of cardiovascular disease (CVD). Our objective was to determine potential improvements in the number needed to treat (NNT) and number of events prevented (NEP) using different risk models in patients eligible for risk stratification. METHODS: A retrospective observational cohort was assembled from primary care patients in Ontario, Canada between January 1st, 2010, to December 31st, 2014 and followed for up to 5 years. Risk estimation was undertaken in patients 40-75 years of age, without CVD, diabetes, or chronic kidney disease using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs), a recalibrated FRS (R-FRS), Systematic Coronary Risk Evaluation 2 (SCORE2), and the low-risk region recalibrated SCORE2 (LR-SCORE2). RESULTS: The cohort consisted of 47,399 patients (59% women, mean age 54). The NNT with statins was lowest for SCORE2 at 40, followed by LR-SCORE2 at 41, R-FRS at 43, PCEs at 55, and FRS at 65. Models that selected for individuals with a lower NNT recommended statins to fewer, but higher risk patients. For instance, SCORE2 recommended statins to 7.9% of patients (5-year CVD incidence 5.92%). The FRS, however, recommended statins to 34.6% of patients (5-year CVD incidence 4.01%). Accordingly, the NEP was highest for the FRS at 406 and lowest for SCORE2 at 156. CONCLUSIONS: Newer models such as SCORE2 may improve statin allocation to higher risk groups with a lower NNT but prevent fewer events at the population level.

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.

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.

Population Trends of New Prescriptions for Antihyperglycemics and Antihypertensives Between 2014 and 2022.

BACKGROUND: In the wake of pandemic-related health decline and health care disruptions, there are concerns that previous gains for cardiovascular risk factors may have stalled or reversed. Population-level excess burden of drug-treated diabetes and hypertension during the pandemic compared with baseline is not well characterized. We evaluated the change in incident prescription claims for antihyperglycemics and antihypertensives before versus during the pandemic. METHODS AND RESULTS: In this retrospective, serial, cross-sectional, population-based study, we used interrupted time series analyses to examine changes in the age- and sex-standardized monthly rate of incident prescriptions for antihyperglycemics and antihypertensives in patients aged ≥66 years in Ontario, Canada, before the pandemic (April 2014 to March 2020) compared with during the pandemic (July 2020 to November 2022). Incident claim was defined as the first prescription filled for any medication in these classes. The characteristics of patients with incident prescriptions of antihyperglycemics (n=151 888) or antihypertensives (n=368 123) before the pandemic were comparable with their pandemic counterparts (antihyperglycemics, n=97 015; antihypertensives, n=146 524). Before the pandemic, monthly rates of incident prescriptions were decreasing (-0.03 per 10 000 individuals [95% CI, -0.04 to -0.01] for antihyperglycemics; -0.14 [95% CI, -0.18 to -0.10] for antihypertensives). After July 2020, monthly rates increased (postinterruption trend 0.31 per 10 000 individuals [95% CI, 0.28-0.34] for antihyperglycemics; 0.19 [95% CI, 0.14-0.23] for antihypertensives). CONCLUSIONS: Population-level increases in new antihyperglycemic and antihypertensive prescriptions during the pandemic reversed prepandemic declines and were sustained for >2 years. Our findings are concerning for current and future cardiovascular health.

Evaluating Readability, Understandability, and Actionability of Online Printable Patient Education Materials for Cholesterol Management: A Systematic Review.

BACKGROUND: Dyslipidemia management is a cornerstone in cardiovascular disease prevention and relies heavily on patient adherence to lifestyle modifications and medications. Numerous cholesterol patient education materials are available online, but it remains unclear whether these resources are suitable for the majority of North American adults given the prevalence of low health literacy. This review aimed to (1) identify printable cholesterol patient education materials through an online search, and (2) evaluate the readability, understandability, and actionability of each resource to determine its utility in practice. METHODS AND RESULTS: We searched the MEDLINE database for peer-reviewed educational materials and the websites of Canadian and American national health organizations for gray literature. Readability was measured using the Flesch-Kincaid Grade Level, and scores between fifth- and sixth-grade reading levels were considered adequate. Understandability and actionability were scored using the Patient Education Materials Assessment Tool and categorized as superior (>80%), adequate (50%-70%), or inadequate (<50%). Our search yielded 91 results that were screened for eligibility. Among the 22 educational materials included in the study, 15 were identified through MEDLINE, and 7 were from websites. The readability across all materials averaged an 11th-grade reading level (Flesch-Kincaid Grade Level=11.9±2.59). The mean±SD understandability and actionability scores were 82.8±6.58% and 40.9±28.60%, respectively. CONCLUSIONS: The readability of online cholesterol patient education materials consistently exceeds the health literacy level of the average North American adult. Many resources also inadequately describe action items for individuals to self-manage their cholesterol, representing an implementation gap in cardiovascular disease prevention.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Association of virtual end-of-life care with healthcare outcomes before and during the COVID-19 pandemic: A population-based study.

The use of virtual care for people at the end-of-life significantly increased during the COVID-19 pandemic, but its association with acute healthcare use and location of death is unknown. The objective of this study was to measure the association between the use of virtual end-of-life care with acute healthcare use and an out-of-hospital death before vs. after the introduction of specialized fee codes that enabled broader delivery of virtual care during the COVID-19 pandemic. This was a population-based cohort study of 323,995 adults in their last 90 days of life between January 25, 2018 and December 31, 2021 using health administrative data in Ontario, Canada. Primary outcomes were acute healthcare use (emergency department, hospitalization) and location of death (in or out-of-hospital). Prior to March 14, 2020, 13,974 (8%) people received at least 1 virtual end-of-life care visit, which was associated with a 16% higher rate of emergency department use (adjusted Rate Ratio [aRR] 1.16, 95%CI 1.12 to 1.20), a 17% higher rate of hospitalization (aRR 1.17, 95%CI 1.15 to 1.20), and a 34% higher risk of an out-of-hospital death (aRR 1.34, 95%CI 1.31 to 1.37) compared to people who did not receive virtual end-of-life care. After March 14, 2020, 104,165 (71%) people received at least 1 virtual end-of-life care visit, which was associated with a 58% higher rate of an emergency department visit (aRR 1.58, 95%CI 1.54 to 1.62), a 45% higher rate of hospitalization (aRR 1.45, 95%CI 1.42 to 1.47), and a 65% higher risk of an out-of-hospital death (aRR 1.65, 95%CI 1.61 to 1.69) compared to people who did not receive virtual end-of-life care. The use of virtual end-of-life care was associated with higher acute healthcare use in the last 90 days of life and a higher likelihood of dying out-of-hospital, and these rates increased during the pandemic.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Predicting long-term survival after de novo cardioverter-defibrillator implantation for primary prevention: A population based study.

Background: Implantable cardioverter-defibrillators (ICDs) reduce the risk of sudden cardiac death in patients with left ventricular dysfunction. While short-term mortality benefit of ICD insertion has been established in landmark randomized controlled trials, little is known about the long-term outcomes of patients with ICDs in clinical practice. In this paper, we describe the long-term survival of patients following de novo ICD implantation for primary prevention in clinical practice and determine the factors which help predict survival after ICD implant. Methods: Retrospective population-based study of all patients receiving a de novo ICD for primary prevention in Ontario, Canada from 2007 to 2011 using the Ontario ICD Database housed within ICES. Simple random selection was used to split the population into a derivation and internal validation cohort in a ratio of 2:1. Cox proportional hazards regression was used to determine predictors of interest and predict 10-year survival, model performance was assessed using calibration and validation. Results: In the derivation cohort (n = 3399), mean age was 65.3 years (standard deviation [SD] = 11.0), 664 patients were female (19.5 %) and 2344 patients (69.0 %) had ischemic cardiomyopathy. Ten year survival was 45.7 % (95 % confidence interval [CI] 44.0 %-47.4 %). The final prediction model included age, sex, disease factors (ischemic vs nonischemic cardiomyopathy, left ventricular ejection fraction) and patient factors (symptoms, comorbidities), and biomarkers at the time of ICD assessment. This model had good discrimination and calibration in derivation (0.79, 95 % CI 0.77, 0.81) and validation samples (0.78, 95 % CI 0.76, 0.79). Conclusions: A combination of demographic and clinical factors determined at baseline can be used to predict 10-year survival in patients with implantable cardioverter-defibrillators with good accuracy. Our findings help to identify individuals at risk of long-term mortality and may be useful in targeting future prevention strategies to enhance longevity in this high-risk population.

Validation of the European SCORE2 models in a Canadian primary care cohort.

AIMS: Systematic Coronary Risk Evaluation Model 2 (SCORE2) was recently developed to predict atherosclerotic cardiovascular disease (ASCVD) in Europe. Whether these models could be used outside of Europe is not known. The objective of this study was to test the validity of SCORE2 in a large Canadian cohort. METHODS AND RESULTS: A primary care cohort of persons with routinely collected electronic medical record data from 1 January 2010 to 31 December 2014, in Ontario, Canada, was used for validation. The SCORE2 models for younger persons (YP) were applied to 57 409 individuals aged 40-69 while the models for older persons (OPs) were applied to 9885 individuals 70-89 years of age. Five-year ASCVD predictions from both the uncalibrated and low-risk region recalibrated SCORE2 models were evaluated. The C-statistic for SCORE2-YP was 0.74 in women and 0.69 in men. The uncalibrated SCORE2-YP overestimated risk by 17% in women and underestimated by 2% in men. In contrast, the low-risk region recalibrated model demonstrated worse calibration, overestimating risk by 100% in women and 36% in men. The C-statistic for SCORE2-OP was 0.64 and 0.62 in older women and men, respectively. The uncalibrated SCORE2-OP overestimated risk by more than 100% in both sexes. The low-risk region recalibrated model demonstrated improved calibration but still overestimated risk by 60% in women and 13% in men. CONCLUSION: The performance of SCORE2 to predict ASCVD risk in Canada varied by age group and depended on whether regional calibration was applied. This underscores the necessity for validation assessment of SCORE2 prior to implementation in new jurisdictions.

In this study, new tools [Systematic Coronary Risk Evaluation Model 2 (SCORE2)] that were developed across Europe to predict heart attack and stroke risk in healthy individuals were tested independently for the first time in a Canadian setting. Key findings are as follows:The accuracy of predictions from SCORE2 in Canadians depends on the age group considered and whether uncalibrated or recalibrated equations are being used.Independent assessment of tools such as SCORE2 remains useful prior to widespread implementation in new jurisdictions.

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.

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.

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.

Association of a Heart Failure Management Incentive in Primary Care With Clinical Outcomes: A Retrospective Cohort Study.

BACKGROUND: We aim to examine the association between primary care physicians' billing of Q050A, a pay-for-performance heart failure (HF) management incentive fee code, and the composite outcome of mortality, hospitalization, and emergency department visits. METHODS AND RESULTS: This population-based cohort study linked administrative health databases in Ontario, Canada, for patients with HF aged >66 years between January 1, 2008, and March 31, 2020. Cases were patients with HF who had a Q050A fee code billed. Cases and controls were matched 1:1 on age, sex, patient status on being rostered to a primary care physician, cardiologist, or internist visit in the 6 months before study enrollment, Johns Hopkins Adjusted Clinical Group resource use bands, days between HF diagnosis and study enrollment (±2 years), and the logit of the propensity score. A Cox proportional hazards model assessed the association of Q050A with the outcome. A total of 59 664 cases had a Q050A billed, whereas 244 883 patients did not. Before matching, patients who had a Q050A billed were more likely to be men (52% versus 49%), were rostered to a primary care physician (100% versus 96%), had a higher Charlson Comorbidity Index, and had higher health care costs. The mean follow-up was 481 days for cases and 530 days for controls. The composite outcome (hazard ratio, 1.11 [95% CI, 1.09-1.12]) was significantly higher for cases than controls. CONCLUSIONS: The Q050A incentive improved financial compensation for primary care physicians managing patients with HF but was not associated with improvements in the outcome. Research on promoting evidence-based HF management is warranted.

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 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 Marginalization With Health Care Use and Clinical Outcomes Following Hospital Discharge in Patients Who Underwent Coronary Catheterization for Acute Myocardial Infarction in a Single-Payer Health Care System.

BACKGROUND: Canadian data suggest that patients of lower socioeconomic status with acute myocardial infarction receive less beneficial therapy and have worse clinical outcomes, raising questions regarding care disparities even in universal health care systems. We assessed the contemporary association of marginalization with clinical outcomes and health services use. METHODS: Using clinical and administrative databases in Ontario, Canada, we conducted a population-based study of patients aged ≥65 years hospitalized for their first acute myocardial infarction between April 1, 2010 and March 1, 2019. Patients receiving cardiac catheterization and surviving 7 days postdischarge were included. Our primary exposure was neighborhood-level marginalization, a multidimensional socioeconomic status metric. Neighborhoods were categorized by quintile from Q1 (least marginalized) to Q5 (most marginalized). Our primary outcome was all-cause mortality. A proportional hazards regression model with a robust variance estimator was used to quantify the association of marginalization with outcomes, adjusting for risk factors, comorbidities, disease severity, and regional cardiologist supply. RESULTS: Among 53 841 patients (median age, 75 years; 39.1% female) from 20 640 neighborhoods, crude 1- and 3-year mortality rates were 7.7% and 17.2%, respectively. Patients in Q5 had no significant difference in 1-year mortality (hazard ratio [HR], 1.08 [95% CI, 0.95-1.22]), but greater mortality over 3 years (HR, 1.13 [95% CI, 1.03-1.22]) compared with Q1. Over 1 year, we observed differences between Q1 and Q5 in visits to primary care physicians (Q1, 96.7%; Q5, 93.7%) and cardiologists (Q1, 82.6%; Q5, 72.6%), as well as diagnostic testing. There were no differences in secondary prevention medications dispensed or medication adherence at 1 year. CONCLUSIONS: In older patients with acute myocardial infarction who survived to hospital discharge, those residing in the most marginalized neighborhoods had a greater long-term risk of mortality, less specialist care, and fewer diagnostic tests. Yet, there were no differences across socioeconomic status in prescription medication use and adherence.

Association of neighbourhood-level material deprivation with adverse outcomes and processes of care among patients with heart failure in a single-payer healthcare system: A population-based cohort study.

AIM: We studied the association between neighbourhood material deprivation, a metric estimating inability to attain basic material needs, with outcomes and processes of care among incident heart failure patients in a universal healthcare system. METHODS AND RESULTS: In a population-based retrospective study (2007-2019), we examined the association of material deprivation with 1-year all-cause mortality, cause-specific hospitalization, and 90-day processes of care. Using cause-specific hazards regression, we quantified the relative rate of events after multiple covariate adjustment, stratifying by age ≤65 or ≥66 years. Among 395 763 patients (median age 76 [interquartile range 66-84] years, 47% women), there was significant interaction between age and deprivation quintile for mortality/hospitalization outcomes (p ≤ 0.001). Younger residents (age ≤65 years) of the most versus least deprived neighbourhoods had higher hazards of all-cause death (hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.10-1.29]) and cardiovascular hospitalization (HR 1.29 [95% CI 1.19-1.39]). Older individuals (≥66 years) in the most deprived neighbourhoods had significantly higher hazard of death (HR 1.11 [95% CI 1.08-1.14]) and cardiovascular hospitalization (HR 1.13 [95% CI 1.09-1.18]) compared to the least deprived. The magnitude of the association between deprivation and outcomes was amplified in the younger compared to the older age group. More deprived individuals in both age groups had a lower hazard of cardiology visits and advanced cardiac imaging (all p < 0.001), while the most deprived of younger ages were less likely to undergo implantable cardioverter-defibrillator/cardiac resynchronization therapy-pacemaker implantation (p = 0.023), compared to the least deprived. CONCLUSION: Patients with newly-diagnosed heart failure residing in the most deprived neighbourhoods had worse outcomes and reduced access to care than those less deprived.

Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.

Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.