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.

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.

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.

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.

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.

The scientific basis of 'net zero emissions' and its diverging sociopolitical representation

The Net Zero Emissions (NZE) concept has created momentum for climate commitment made by national governments, cities, industries and individual companies. However, evidence of tangible decarbonisation is limited. Here we identify precarious differences between the scientific origin of NZE and its social representation in the wider public and explore the consequences of the resulting science-action gap for achieving global climate goals. A particular focus is given to 'offsetting', which is closely connected to the practical delivery of NZE but typically ignores that different types or carbon credits have different environmental efficacy. Revisiting the science related to the global carbon cycle demonstrates that a heavy reliance on any carbon offsetting that is not a permanent removal presents a real risk. Moreover, competition over scarce 'removal credits' distracts from the real tasks at hand, namely to rapidly decrease fossil fuel emissions, actively remove carbon through restoration, and protect existing terrestrial carbon sinks. Establishing separate targets for these distinct actions is an essential step towards disentangling current confusion. Whilst a 'race to net zero' may trigger innovation in the decarbonisation space, the restoration and protection of carbon sinks demands a collective approach where actors should focus on how to make real and verifiable contributions rather than claiming individual net zero scores.

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 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 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 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.

Perioperative outcomes of laparoscopic low anterior resection using ArtiSential® versus robotic approach in patients with rectal cancer: a propensity score matching analysis

BACKGROUND

Total mesorectal excision using conventional straight fixed devices may be technically difficult because of the narrow and concave pelvis. Several laparoscopic articulating tools have been introduced as an alternative to robotic systems. The aim of this study was to compare perioperative outcomes between laparoscopic low anterior resection using ArtiSential® and robot-assisted surgery for rectal cancer.

METHODS

This retrospective study included 682 patients who underwent laparoscopic or robotic low anterior resection  for rectal cancer from September 2018 to December 2021. Among them, 82 underwent laparoscopic surgery using ArtiSential® (group A) and 201 underwent robotic surgery (group B). A total of 73 [group A; 66.37 ± 11.62; group B 65.79 ± 11.34] patients were selected for each group using a propensity score matching analysis.

RESULTS

There was no significant difference in the baseline characteristics between group A and B. Mean operative time was longer in group B than A (163.5 ± 61.9 vs 250.1 ± 77.6 min, p < 0.001). Mean length of hospital stay was not significantly different between the two groups (6.2 ± 4.7 vs 6.7 ± 6.1 days, p = 0.617). Postoperative complications, reoperation, and readmission within 30 days after surgery were similar between the two groups. Pathological findings revealed that the circumferential resection margins were above 10 mm in both groups (11.00 ± 7.47 vs 10.17 ± 6.25 mm, p = 0.960). At least 12 lymph nodes were sufficiently harvested, with no significant difference in the number harvested between the groups (20.5 ± 9.9 vs 19.7 ± 7.3, p = 0.753).

CONCLUSIONS

Laparoscopic low anterior resection using ArtiSential® can achieve acceptable clinical and oncologic outcomes. ArtiSential®, a multi-joint and articulating device, may serve a feasible alternative approach to robotic surgery in rectal cancer.

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.

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.

Factors Affecting Healthcare Provider Referral to Heart Function Clinics: A Mixed-Methods Study

BACKGROUND

Heart failure (HF) care providers are gatekeepers for patients to appropriately access lifesaving HF clinics.

OBJECTIVE

The aim of this study was to investigate referring providers' perceptions regarding referral to HF clinics, including the impact of provider specialty and the coronavirus disease pandemic.

METHODS

An exploratory, sequential design was used in this mixed-methods study. For the qualitative stage, semistructured interviews were performed with a purposive sample of HF providers eligible to refer (ie, nurse practitioners, cardiologists, internists, primary care and emergency medicine physicians) in Ontario. Interviews were conducted via Microsoft Teams. Transcripts were analyzed concurrently by 2 researchers independently using NVivo, using a deductive-thematic approach. Then, a cross-sectional survey of similar providers across Canada was undertaken via REDCap (Research Electronic Data Capture), using an adapted version of the Provider Attitudes toward Cardiac Rehabilitation and Referral scale.

RESULTS

Saturation was achieved upon interviewing 7 providers. Four themes arose: knowledge about clinics and their characteristics, providers' clinical expertise, communication and relationship with their patients, and clinic referral process and care continuity. Seventy-three providers completed the survey. The major negative factors affecting referral were skepticism regarding clinic benefit (4.1 ± 0.9/5), a bad patient experience and believing they are better equipped to manage the patient (both 3.9). Cardiologists more strongly endorsed clarity of referral criteria, referral as normative and within-practice referral supports as supporting appropriate referral versus other professionals ( P s < .02), among other differences. One-third (n = 13) reported the pandemic impacted their referral practices (eg, limits to in-person care, patient concerns).

CONCLUSION

Although there are some legitimate barriers to appropriate clinic referral, greater provider education and support could facilitate optimal patient access.

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.

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.

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.

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.

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.

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.

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.

Association between Gut Microbial Change and Acute Gastrointestinal Toxicity in Patients with Prostate Cancer Receiving Definitive Radiation Therapy: A Prospective Pilot Analysis

PURPOSE/OBJECTIVE(S)

The gut microbiome is an emerging biomarker that is known to have a pivotal role in the development of diverse human diseases. This prospective cohort study aimed to investigate the association between gut microbial changes and acute gastrointestinal (GI) toxicities in prostate cancer patients receiving definitive radiation therapy (RT).

MATERIALS/METHODS

Seventy-nine fecal samples from 16 prostate cancer patients were analyzed. Stool samples were collected at the following timepoints: pre-RT (prRT), 2 weeks after the start of RT (RT-2w), 5 weeks after the start of RT (RT-5w), 1 month after completion of RT (poRT-1m), and 3 months after completion of RT (poRT-3m). Total RT doses were 69.6‒74.4 Gy at 2.4 Gy per fraction in the high-dose area and 45‒50.4 Gy at 1.8 Gy per fraction in the low-dose area. Alpha- and beta-diversity were estimated. We computed the microbial community polarization index (MCPI) as an indicator of RT-induced dysbiosis. A linear mixed effect model was adopted to evaluate time effects after RT. Metabolic pathway abundances were inferred using bioinformatics tools.

RESULTS

Seven patients experienced ≥ grade 1 acute GI toxicities. Patients experiencing toxicity had lower alpha diversity, especially at RT-2w (P = 0.037) and RT-5w (P = 0.003), with the microbiota enriched in Fusobacteria, Fusobacterium, and Bacteroides fragilis. Patients receiving a large RT field had a trend of lower alpha diversity, particularly at poRT-1m (P = 0.027), with the microbiota enriched in Propionibacteriaceae, Cutibacterium, and Prevotella stercorea. Compared with the MCPI at prRT, the MCPI observed at poRT-1m in patients experiencing toxicities was significantly elevated (P = 0.007). In terms of predicted metabolic pathways, we found linearly decreasing pathways, including carbon fixation pathways in prokaryotes (P = 0.035) and the bacterial secretion system (P = 0.005), in patients who experienced toxicities. Regarding the RT field, no linear trend of functional pathways was found across timepoints.

CONCLUSION

We showed RT-induced dysbiosis in the gut microbiome among patients with prostate cancer who experienced toxicities or received a large RT field. Reduced diversity and elevated RT-related MCPI could be helpful for developing individualized RT approaches. Longitudinal analysis revealed dynamic changes in several microbes and metabolic pathways, which should be validated in a whole metagenome sequencing study.

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.

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.

Access to Heart Failure Services in Canada: Findings of the Heart and Stroke National Heart Failure Resources and Services Inventory

BACKGROUND

The rising incidence of heart failure (HF) in Canada necessitates commensurate resources dedicated to its management. Several health system partners launched an HF Action Plan to understand the current state of HF care in Canada and address inequities in access and resources.

METHODS

A national Heart Failure Resources and Services Inventory (HF-RaSI) was conducted from 2020 to 2021 of all 629 acute care hospitals and 20 urgent care centres in Canada. The HF-RaSI consisted of 44 questions on available resources, service,s and processes across acute care hospitals and related ambulatory settings.

RESULTS

HF-RaSIs were completed by 501 acute care hospitals and urgent care centres, representing 94.7% of all HF hospitalisations across Canada. Only 12.2% of HF care was provided by hospitals with HF expertise and resources, and 50.9% of HF admissions were in centres with minimal outpatient or inpatient HF capabilities. Across all Canadian hospitals, 28.7% did not have access to B-type natriuretic peptide testing, and only 48.1% had access to on-site echocardiography. Designated HF medical directors were present at 21.6% of sites (108), and 16.2% sites (81) had dedicated inpatient interdisciplinary HF teams. Among all of the sites, 28.1% (141) were HF clinics, and of those, 40.4% (57) had average wait times from referral to first appointment of more than 2 weeks.

CONCLUSIONS

Significant gaps and geographic variation in delivery and access to HF services exist in Canada. This study highlights the need for provincial and national health systems changes and quality improvement initiatives to ensure equitable access to the appropriate evidence-based HF care.

Multiple arterial coronary bypass grafting is associated with better survival compared with second-generation drug-eluting stents in patients with stable multivessel coronary artery disease

OBJECTIVE

We sought to compare the long-term outcomes of multiarterial graft (MAG) coronary artery bypass grafting (CABG) versus percutaneous coronary intervention (PCI) with second-generation drug-eluting stents (DES) to treat stable multivessel coronary artery disease.

METHODS

This study was a multicenter population-based retrospective analysis of all residents of Ontario, Canada, from January 1, 2011, to December 31, 2019. We identified 3600 cases of elective primary isolated CABG with MAG and 2187 cases of PCI with second-generation DES.

RESULTS

After the application of propensity score-weighting using overlap weights, MAG was associated with better survival over 5 years compared with DES (96.8% vs 94.5%; hazard ratio [HR], 0.56; 95% CI, 0.37-0.85). MAG was also associated with better secondary outcomes including a composite of death, myocardial infarction, and stroke (94.3% vs 88.5%; HR, 0.49; 95% CI, 0.36-0.65). The rate of death, stroke, myocardial infarction, and repeat revascularization (91.2% vs 70.7%; HR, 0.24; 95% CI, 0.20-0.30), and the individual end points of myocardial infarction (1.4% vs 6.9%; HR, 0.22; 95% CI, 0.13-0.35), and repeat revascularization (4.1% vs 24.2%; HR, 0.14; 95% CI, 0.10-0.18) were lower with MAG. PCI with second-generation DES was associated with a lower rate of stroke up to 5 years (0.6% vs 1.8%; HR, 3.97; 95% CI, 1.45-10.88).

CONCLUSIONS

CABG with MAG was associated with better survival and fewer major cardiac adverse events compared with second-generation DES and might be considered the treatment of choice for patients with stable multivessel coronary artery disease. Further randomized controlled trials are needed to confirm this hypothesis.

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.

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.

Global Comparison of Readmission Rates for Patients With Heart Failure

BACKGROUND

Heart failure (HF) readmission rates are low in some jurisdictions. However, international comparisons are lacking and could serve as a foundation for identifying regional patient management strategies that could be shared to improve outcomes.

OBJECTIVES

This study sought to summarize 30-day and 1-year all-cause readmission and mortality rates of hospitalized HF patients across countries and to explore potential differences in rates globally.

METHODS

We performed a systematic review and meta-analysis using MEDLINE, Embase, and CENTRAL for observational reports on hospitalized adult HF patients at risk for readmission or mortality published between January 2010 and March 2021. We conducted a meta-analysis of proportions using a random-effects model, and sources of heterogeneity were evaluated with meta-regression.

RESULTS

In total, 24 papers reporting on 30-day and 23 papers on 1-year readmission were included. Of the 1.5 million individuals at risk, 13.2% (95% CI: 10.5%-16.1%) were readmitted within 30 days and 35.7% (95% CI: 27.1%-44.9%) within 1 year. A total of 33 papers reported on 30-day and 45 papers on 1-year mortality. Of the 1.5 million individuals hospitalized for HF, 7.6% (95% CI: 6.1%-9.3%) died within 30 days and 23.3% (95% CI: 20.8%-25.9%) died within 1 year. Substantial variation in risk across countries was unexplained by countries' gross domestic product, proportion of gross domestic product spent on health care, and Gini coefficient.

CONCLUSIONS

Globally, hospitalized HF patients exhibit high rates of readmission and mortality, and the variability in readmission rates was not explained by health care expenditure, risk of mortality, or comorbidities.

Outcomes Among Patients Hospitalized With Non-COVID-19 Conditions Before and During the COVID-19 Pandemic in Alberta and Ontario, Canada

Importance

The association of inpatient COVID-19 caseloads with outcomes in patients hospitalized with non-COVID-19 conditions is unclear.

Objective

To determine whether 30-day mortality and length of stay (LOS) for patients hospitalized with non-COVID-19 medical conditions differed (1) before and during the pandemic and (2) across COVID-19 caseloads.

Design, Setting, and Participants

This retrospective cohort study compared patient hospitalizations between April 1, 2018, and September 30, 2019 (prepandemic), vs between April 1, 2020, and September 30, 2021 (during the pandemic), in 235 acute care hospitals in Alberta and Ontario, Canada. All adults hospitalized for heart failure (HF), chronic obstructive pulmonary disease (COPD) or asthma, urinary tract infection or urosepsis, acute coronary syndrome, or stroke were included.

Exposure

The monthly surge index for each hospital from April 2020 through September 2021 was used as a measure of COVID-19 caseload relative to baseline bed capacity.

Main Outcomes and Measures

The primary study outcome was 30-day all-cause mortality after hospital admission for the 5 selected conditions or COVID-19 as measured by hierarchical multivariable regression models. Length of stay was the secondary outcome.

Results

Between April 2018 and September 2019, 132 240 patients (mean [SD] age, 71.8 [14.8] years; 61 493 female [46.5%] and 70 747 male [53.5%]) were hospitalized for the selected medical conditions as their most responsible diagnosis compared with 115 225 (mean [SD] age, 71.9 [14.7] years, 52 058 female [45.2%] and 63 167 male [54.8%]) between April 2020 and September 2021 (114 414 [99.3%] of whom had negative SARS-CoV-2 test results). Patients admitted during the pandemic with any of the selected conditions and concomitant SARS-CoV-2 infection exhibited a much longer LOS (mean [SD], 8.6 [7.1] days or a median of 6 days longer [range, 1-22 days]) and greater mortality (varying across diagnoses, but with a mean [SD] absolute increase at 30 days of 4.7% [3.1%]) than those without coinfection. Patients hospitalized with any of the selected conditions without concomitant SARS-CoV-2 infection had similar LOSs during the pandemic as before the pandemic, and only patients with HF (adjusted odds ratio [AOR], 1.16; 95% CI, 1.09-1.24) and COPD or asthma (AOR, 1.41; 95% CI, 1.30-1.53) had a higher risk-adjusted 30-day mortality during the pandemic. As hospitals experienced COVID-19 surges, LOS and risk-adjusted mortality remained stable for patients with the selected conditions but were higher in patients with COVID-19. Once capacity reached above the 99th percentile, patients' 30-day mortality AOR was 1.80 (95% CI, 1.24-2.61) vs when the surge index was below the 75th percentile.

Conclusions and Relevance

This cohort study found that during surges in COVID-19 caseloads, mortality rates were significantly higher only for hospitalized patients with COVID-19. However, most patients hospitalized with non-COVID-19 conditions and negative SARS-CoV-2 test results (except those with HF or with COPD or asthma) exhibited similar risk-adjusted outcomes during the pandemic as before the pandemic, even during COVID-19 caseload surges, suggesting resiliency in the event of regional or hospital-specific occupancy strains.

Statin Use and Stroke Rate in Older Adults With Atrial Fibrillation: A Population-Based Cohort Study

Background Atherosclerotic disease is an important contributor to adverse outcomes in patients with atrial fibrillation (AF). There is limited recognition of the association between statin use and stroke rates in AF. We aimed to quantify the association between statin use and stroke rate in AF. Methods and Results Using linked administrative databases in Ontario, Canada, we conducted a population-based retrospective cohort study of patients, aged ≥66 years, diagnosed with AF between 2009 and 2019. We used cause-specific hazard regression to determine the association of statin use with stroke rate. We developed a second model to further adjust for lipid levels in the subset of patients with available measurements in the year before AF diagnosis. Both models adjusted for age, sex, heart failure, hypertension, diabetes, stroke/transient ischemic attack, vascular disease, and P2Y12 inhibitors at baseline, plus anticoagulation as a time-varying covariate. We studied 261 659 qualifying patients (median age, 78 years; 49% women). Statins were used in 142 834 (54.6%) patients, and 145 673 (55.7%) had lipid measurement(s) in the preceding year. Statin use was associated with lower stroke rates, with adjusted hazard ratios of 0.83 (95% CI, 0.77-0.88; P<0.001) in the full cohort and 0.87 (95% CI, 0.78-0.97; P=0.01) when adjusting for lipid data. Stroke rates increased in a near-linear manner as low-density lipoprotein values increased >1.5 mmol/L. Conclusions Statins were associated with lower stroke rates in patients with AF, whereas higher low-density lipoprotein levels were associated with higher stroke rates, highlighting the importance of vascular risk factor treatment in AF.

Short- and Long-Term Outcomes in Patients With Thrombophilia Undergoing Transcatheter Closure of Patent Foramen Ovale

BACKGROUND

Patients with thrombophilia are underrepresented in studies evaluating outcomes after closure of patent foramen ovale (PFO). Real-world data on long term outcomes in this population are very limited.

OBJECTIVES

This study compared outcomes in patients with and without thrombophilia undergoing PFO closure, using data from a large, clinical database linked to population-based databases.

METHODS

This retrospective cohort study included consecutive patients who had a transcatheter PFO closure and had preprocedural thrombophilia screening. Data from a retrospective, clinical registry were linked to population-based administrative databases in Ontario Canada to evaluate outcomes. Outcomes were reported as rates per 100 person-years and compared using Poisson regression.

RESULTS

We included 669 patients, with a mean age of 56.4 years, 97.9% of whom underwent PFO closure for a cryptogenic stroke. Thrombophilia was diagnosed among 174 (26.0%), of which 86% had inherited mutations. In-hospital, procedural complications were observed in 3.1% of patients with no difference by thrombophilia status. Similarly, no differences were observed in 30-day emergency department visits and readmissions. Over the median follow-up of 11.6 years, the most common adverse outcome was new-onset atrial fibrillation (1.0 per 100 person-years; 95% CI: 0.8-1.2), followed by recurrent cerebrovascular events (0.8 per 100 person-years; 95% CI: 0.6-1.1) with no differences between the groups (P > 0.05).

CONCLUSIONS

After PFO closure, no differences were observed in long-term adverse outcomes between patients with and without thrombophilia. Though these patients have been excluded from randomized clinical trials of PFO closure in the past, real-world evidence supports their eligibility for the procedure.

New Hypertension After Pregnancy in Patients With Heart Disease

Background After pregnancy, patients with preexisting heart disease are at high risk for cardiovascular complications. The primary objective was to compare the incidence of new hypertension after pregnancy in patients with and without heart disease. Methods and Results This was a retrospective matched-cohort study comparing the incidence of new hypertension after pregnancy in 832 patients who are pregnant with congenital or acquired heart disease to a comparison group of 1664 patients who are pregnant without heart disease; matching was by demographics and baseline risk for hypertension at the time of the index pregnancy. We also examined whether new hypertension was associated with subsequent death or cardiovascular events. The 20-year cumulative incidence of hypertension was 24% in patients with heart disease, compared with 14% in patients without heart disease (hazard ratio [HR], 1.81 [95% CI, 1.44-2.27]). The median follow-up time at hypertension diagnosis in the heart disease group was 8.1 years (interquartile range, 4.2-11.9 years). The elevated rate of new hypertension was observed not only in patients with ischemic heart disease, but also in those with left-sided valve disease, cardiomyopathy, and congenital heart disease. Pregnancy risk prediction methods can further stratify risk of new hypertension. New hypertension was associated with an increased rate of subsequent death or cardiovascular events (HR, 1.54 [95% CI, 1.05-2.25]). Conclusions Patients with heart disease are at higher risk for developing hypertension in the decades after pregnancy when compared with those without heart disease. New hypertension in this young cohort is associated with adverse cardiovascular events highlighting the importance of systematic and lifelong surveillance.

Two-dimensional material-based complementary ambipolar field-effect transistors with ohmic-like contacts

Ambipolar field-effect transistors (FETs) possessing both electron and hole carriers enable implementation of novel reconfigurable transistors, artificial synaptic transistors, and output polarity controllable (OPC) amplifiers. Here, we fabricated a two-dimensional (2D) material-based complementary ambipolar FET and investigated its electrical characteristics. Properties of ohmic-like contacts at source/drain sides were verified from output characteristics and temperature-dependent measurements. The symmetry of electron and hole currents can be easily achieved by optimization of the MoS2 or WSe2 channels, different from the conventional ambipolar FET with fundamental issues related to Schottky barriers. In addition, we demonstrated successful operation of a complementary inverter and OPC amplifier, using the fabricated complementary ambipolar FET based on 2D materials.&#xD.

Sex-Based Differences in Severe Outcomes, Including Cardiovascular Hospitalization, in Adults With COVID-19 in Ontario, Canada

Background

While men have experienced higher risks of SARS-CoV-2 infection compared to women, an analysis of sex differences by age in severe outcomes during the acute phase of infection is lacking.

Objectives

The purpose of this study was to assess heterogeneity in severe outcome risks by age and sex by conducting a retrospective cohort study of community-dwelling adults in Ontario who tested positive for SARS-CoV-2 infection during the first 3 waves.

Methods

Adjusted odds ratios were estimated using multilevel multivariable logistic regression models including an interaction term for age and sex. The primary outcome was a composite of severe outcomes (hospitalization for a cardiovascular (CV) event, intensive care unit admission, mechanical ventilation, or death) within 30 days.

Results

Among 30,736, 199,132, and 186,131 adults who tested positive during the first 3 waves, 1,908 (6.2%), 5,437 (2.7%), and 5,653 (3.0%) experienced a severe outcome within 30 days. For all outcomes, the sex-specific risk depended on age (all P for interaction <0.05). Men with SARS-CoV-2 infection experienced a higher risk of outcomes than infected women of the same age, except for the risk of all-cause hospitalization being higher for young women than men (ages 18-45 years) during waves 2 and 3. The sex disparity in CV hospitalization across all ages either persisted or increased with each subsequent wave.

Conclusions

To mitigate risks in subsequent waves, it is helpful to further understand the factors that contribute to the generally higher risks faced by men across all ages, and the persistent or increasing sex disparity in the risk of CV hospitalization.

Derivation and validation of predictive indices for cardiac readmission after coronary and valvular surgery - A multicenter study

Objective

To derive and validate models to predict the risk of a cardiac readmission within one year after specific cardiac surgeries using information that is commonly available from hospital electronic medical records.

Methods

In this retrospective cohort study, we derived and externally validated clinical models to predict the likelihood of cardiac readmissions within one-year of isolated CABG, AVR, and combined CABG+AVR in Ontario, Canada, using multiple clinical registries and routinely collected administrative databases. For all adult patients who underwent these procedures, multiple Fine and Gray subdistribution hazard models were derived within a competing-risk framework using the cohort from April 2015 to March 2018 and validated in an independent cohort (April 2018 to March 2020).

Results

For the model that predicted post-CABG cardiac readmission, the c-statistic was 0.73 in the derivation cohort and 0.70 in the validation cohort at one-year. For the model that predicted post-AVR cardiac readmission, the c-statistic was 0.74 in the derivation and 0.73 in the validation cohort at one-year. For the model that predicted cardiac readmission following CABG+AVR, the c-statistic was 0.70 in the derivation and 0.66 in the validation cohort at one-year.

Conclusions

Prediction of one-year cardiac readmission for isolated CABG, AVR, and combined CABG+AVR can be achieved parsimoniously using multidimensional data sources. Model discrimination was better than existing models derived from single and multicenter registries.

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.

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.

Real-World Examination of Revascularization Strategies for Left Main Coronary Disease in Ontario, Canada

BACKGROUND

Randomized trials have compared percutaneous coronary intervention and coronary artery bypass grafting (CABG) in patients with left main coronary artery disease undergoing nonemergent revascularization. However, there is a paucity of real-world contemporary observational studies comparing percutaneous coronary intervention (PCI) and CABG.

OBJECTIVES

The purpose of this study was to compare the long-term clinical outcomes of CABG versus PCI in patients with left main coronary disease.

METHODS

Clinical and administrative databases for Ontario, Canada, were linked to obtain records of all patients with angiographic evidence of left main coronary artery disease (≥50% stenosis) treated with either isolated CABG or PCI from 2008 to 2020. Emergent, cardiogenic shock, and ST-segment elevation myocardial infarction patients were excluded. Baseline characteristics of patients were compared and 1:1 propensity score matching was performed. Late mortality and major adverse cardiac and cerebrovascular events were compared between the matched groups using a Cox proportional hazard model.

RESULTS

After exclusions, 1,299 and 21,287 patients underwent PCI and CABG, respectively. Prior to matching, PCI patients were older (age 75.2 vs 68.0 years) and more likely to be women (34.6% vs 20.1%), although they had less CAD burden. Propensity score matching on 25 baseline covariates yielded 1,128 well-matched pairs. There was no difference in early mortality between PCI and CABG (5.5% vs 3.9%; P = 0.075). Over 7-year follow-up, all-cause mortality (53.6% vs 35.2%; HR: 1.63; 95% CI: 1.42-1.87; P < 0.001) and major adverse cardiac and cerebrovascular events (66.8% vs 48.6%; HR: 1.77; 95% CI: 1.57-2.00) were significantly higher with PCI than CABG.

CONCLUSIONS

CABG was the most common revascularization strategy in this real-world registry. Patients undergoing PCI were much older and of higher risk at baseline. After matching, there was no difference in early mortality but improved late survival and freedom from major adverse cardiac and cerebrovascular events with CABG.

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.

Real world long-term outcomes among adults undergoing transcatheter patent foramen closure with amplatzer PFO occluder

BACKGROUND

Patent foramen ovale (PFO) is a congenital heart defect associated with an increased risk of cryptogenic stroke. We aimed to evaluate real-world outcomes of adult patients undergoing transcatheter PFO closure with the Amplatzer PFO Occluder.

METHODS

In this single centre, retrospective cohort study, we linked a detailed clinical registry with provincial administrative databases to obtain short and long-term outcomes. Validated algorithms were used to established baseline comorbidities and adverse outcomes.

RESULTS

Between 1999 and 2017, 479 patients had PFO closure with an Amplatzer PFO Occluder. The average age of the patients was 47.3 years (standard deviation (SD) = 12.4), and 54.7% were males. The procedural success was 100%, and 96% of patients were discharged on the same day. Any in-hospital complication was observed in 2.5% (n = 12) of patients. At 30 days post-discharge, 18% of patients had an ED visit and 5% a hospitalization. Over a mean follow-up of 9.1 (SD = 3.8) years, 4% experienced TIA, 1.5% stroke, and 7.6% atrial fibrillation. The composite outcome of stroke/TIA/death was observed in 10.9% of patients (1.22 events per 100 person-years). Patients >60 years old experienced higher rates of adverse events than younger patients.

CONCLUSIONS

In this large real-world cohort of patients with cryptogenic stroke, we observed excellent safety and effectiveness outcomes for PFO closure conducted with Amplatzer PFO Occluder, similar to randomized controlled trials or other long-term cohort studies. New onset atrial fibrillation was one of the most commonly adverse events. Future studies should investigate early post-discharge management of patients to prevent readmissions.

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.).

Derivation and validation of pragmatic clinical models to predict hospital length of stay after cardiac surgery in Ontario, Canada: a population-based cohort study

BACKGROUND

Cardiac surgery is resource intensive and often requires multidisciplinary involvement to facilitate discharge. To facilitate evidence-based resource planning, we derived and validated clinical models to predict postoperative hospital length of stay (LOS).

METHODS

We used linked, population-level databases with information on all Ontario residents and included patients aged 18 years or older who underwent coronary artery bypass grafting, valvular or thoracic aorta surgeries between October 2008 and September 2019. The primary outcome was hospital LOS. The models were derived by using patients who had surgery before Sept. 30, 2016, and validated after that date. To address the rightward skew in LOS data and to identify top-tier resource users, we used logistic regression to derive a model to predict the likelihood of LOS being more than the 98th percentile (> 30 d), and γ regression in the remainder to predict continuous LOS in days. We used backward stepwise variable selection for both models.

RESULTS

Among 105 193 patients, 2422 (2.3%) had an LOS of more than 30 days. Factors predicting prolonged LOS included age, female sex, procedure type and urgency, comorbidities including frailty, high-risk acute coronary syndrome, heart failure, reduced left ventricular ejection fraction and psychiatric and pulmonary circulatory disease. The C statistic was 0.92 for the prolonged LOS model and the mean absolute error was 2.4 days for the continuous LOS model.

INTERPRETATION

We derived and validated clinical models to identify top-tier resource users and predict continuous LOS with excellent accuracy. Our models could be used to benchmark clinical performance based on expected LOS, rationally allocate resources and support patient-centred operative decision-making.

Health Care Implications of the COVID-19 Pandemic for the Cardiovascular Practitioner

There has been substantial excess morbidity and mortality during the COVID-19 pandemic, not all of which was directly attributable to SARS-CoV-2 infection, and many non-COVID-19 deaths were cardiovascular. The indirect effects of the pandemic have been profound, resulting in a substantial increase in the burden of cardiovascular disease and cardiovascular risk factors, both in individuals who survived SARS-CoV-2 infection and in people never infected. In this report, we review the direct effect of SARS-CoV-2 infection on cardiovascular and cardiometabolic disease burden in COVID-19 survivors as well as the indirect effects of the COVID-19 pandemic on the cardiovascular health of people who were never infected with SARS-CoV-2. We also examine the pandemic effects on health care systems and particularly the care deficits caused (or exacerbated) by health care delayed or foregone during the COVID-19 pandemic. We review the consequences of: (1) deferred/delayed acute care for urgent conditions; (2) the shift to virtual provision of outpatient care; (3) shortages of drugs and devices, and reduced access to: (4) diagnostic testing, (5) cardiac rehabilitation, and (6) homecare services. We discuss the broader implications of the COVID-19 pandemic for cardiovascular health and cardiovascular practitioners as we move forward into the next phase of the pandemic.

Population study of sex-based outcomes after surgical aortic valve replacement

Background

Surgical aortic valve replacement (SAVR) is a key strategy for the treatment of aortic valve disease. However, studies have involved primarily male patients, and whether the benefits of this approach can be extrapolated to female patients is unclear.

Methods

Clinical and administrative datasets for 12,207 patients undergoing isolated SAVR in Ontario from 2008 to 2019 were linked. Male and female patients were balanced using inverse probability treatment weighting. Mortality, endocarditis, and major hemorrhagic and thrombotic events, as well as 2 composite outcomes-major adverse cerebral and cardiovascular events (MACCE) and patient-derived adverse cardiovascular and noncardiovascular events (PACE)-and their component events, were compared in the weighted groups with a stratified log-rank test.

Results

A total of 7485 male patients and 4722 female patients were included in the study. Median follow-up was 5.2 years in both sexes. All-cause mortality did not differ between sexes (hazard ratio [HR] 0.949 [95% confidence interval {CI} 0.851-1.059]). Male sex was associated with an increased risk of new-onset dialysis (HR 0.689 [95% CI 0.488-0.974]). Female sex was associated with a significantly increased risk of both new-onset heart failure (HR 1.211 [95% CI 1.051-1.394], P = 0.0081) and heart failure hospitalization (HR 1.200 [95% CI 1.036-1.390], P = 0.015). No statistically significant differences were seen in any of the other secondary outcomes between sexes.

Conclusions

This population health study demonstrated that survival did not differ between male and female patients undergoing SAVR. Significant sex-related differences were found in the risk of heart failure and new-onset dialysis, but these findings should be considered exploratory and require further study.