Kevin R. Bainey

Background: Coronary artery bypass grafting (CABG) is often preferred over percutaneous coronary intervention (PCI) in patients with diabetes mellitus (DM) and multivessel coronary artery disease (MVD), but whether it confers a long-term survival advantage remains unclear. Moreover, whether anatomic complexity modulates this relationship is unknown. Methods: We included 5,615 patients with diabetes and MVD who underwent revascularization with contemporary drug-eluting stents (PCI-DES; n=3,410) or CABG (n=2,205) in Alberta, Canada, between 2009 and 2019. The primary outcome was 5-year all-cause mortality. Secondary outcomes were 5-year major adverse cardiovascular events (MACE) and 10-year mortality and MACE in an earlier-era subcohort of 2,103 patients. Inverse probability of treatment weighting with Cox proportional hazards models was used. Interactions between revascularization, Duke Jeopardy Score (DJS), and clinical presentation (stable angina, unstable angina/NSTEMI, or STEMI) were examined. Results: Five-year all-cause mortality did not differ between PCI-DES and CABG (HR, 1.14; [95% CI,0.99-1.31]; P=0.08). However, PCI-DES was associated with a higher 5-year MACE (HR, 1.23; [95% CI,1.10-1.38]; P=0.0004), but lower 10-year all-cause mortality compared to CABG (HR,0.85; [95% CI,0.72-0.99]; P=0.04) with no difference in MACE (HR, 0.97; 95 % CI: 0.85-1.11; P=0.68). Anatomic complexity modified these findings, with lower 5-year mortality after CABG only at higher DJS (P _interaction =0.003), and lower 10-year mortality after PCI-DES only at lower DJS (P _interaction <0.0001). Conclusions: In patients with diabetes and MVD, 5-year all-cause mortality did not differ significantly between groups, although PCI-DES was associated with higher 5-year MACE rates. At 10 years, multivessel PCI-DES was associated with lower 10-year mortality and no difference in MACE. Anatomic complexity modified the effects, favouring CABG at higher DJS and PCI-DES at lower DJS. Our findings should be considered when planning revascularization in diabetes with MVD.

The COVID-19 pandemic presented a unique opportunity for researchers to use pragmatic methods to obtain information about the virus and promptly disseminate the findings for healthcare providers to utilize them. One such approach to studying patients who presented with COVID-19 and ST-Elevation Myocardial Infarction (STEMI) was the development of the North American COVID-19 STEMI (NACMI) registry. By leveraging social media and communication platforms such as WhatsApp, cardiologists across North America initiated the development of the NACMI registry within 14 days—an accomplishment which was unfathomable pre-pandemic. This was followed by ethics approval through a centralized Institutional Review Board, which led to patient consent being waived, and the use of a single coordinating center (Minneapolis Heart Institute Foundation) responsible for sites, database, and analysis. The NACMI findings were published rapidly across various media, including high-profile cardiovascular journals. Early results demonstrated an elevated mortality rate, disproportionality based on race, and angiographic differences showing unique mechanistic differences in the etiology of STEMI due to COVID-19. Additionally, NACMI provided early evidence that vaccination for COVID-19 reduced the risk of mortality in those with COVID-19 and STEMI. NACMI provided a framework for informing cardiologists early on regarding the optimal treatment of STEMI in patients with COVID-19. The lessons learned may be valuable for developing policy in future pandemics to treat patients using real-world application and routinely collected variables.

BACKGROUND: Complete revascularization is superior to culprit lesion–only percutaneous coronary intervention (PCI) in reducing ischemic events in patients with ST-segment–elevation myocardial infarction and multivessel disease. However, the relationship between the extent of revascularization and the benefits of a complete revascularization strategy remains unclear. The aim of this substudy of the COMPLETE trial was to evaluate how the degree of anatomic completeness of revascularization, measured by the core laboratory-derived modified residual SYNTAX score (R′SS), relates to major cardiovascular events. METHODS: We conducted an exploratory post hoc analysis of the COMPLETE trial (n=3738), stratifying patients randomized in the complete revascularization group based on the R′SS assessed after staged nonculprit lesion–PCI. Complete revascularization was defined by an R′SS=0, whereas incomplete revascularization was defined by an R′SS>0. A stratified Cox proportional hazards model was used, with the culprit-only PCI arm designated as the reference group for comparison. The first coprimary outcome was a composite of cardiovascular death or new myocardial infarction. The second coprimary outcome was a composite of cardiovascular death, new myocardial infarction, or ischemia-driven revascularization. RESULTS: Among patients randomized to a complete revascularization strategy, 90% achieved complete revascularization (R′SS=0), whereas 10% did not (R′SS>0). In patients with R′SS=0, the first coprimary outcome occurred less frequently (6.6%) compared with those randomized to the culprit lesion–only PCI strategy (10.7%; adjusted hazard ratio, 0.61 [95% CI, 0.47–0.78]). Among patients with an R′SS>0, the first coprimary outcome was similar (10.7%) to those in the culprit lesion–only PCI group (10.7%; adjusted hazard ratio, 1.01 [95% CI, 0.61–1.67]). A similar result was observed for the second coprimary outcome. CONCLUSIONS: This exploratory analysis of the COMPLETE trial suggests that the benefit of a complete revascularization strategy in patients with ST-segment–elevation myocardial infarction and multivessel disease may be related to the extent of anatomic completeness of revascularization. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01740479.

Care processes and outcomes for patients undergoing primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI) remain heterogeneous. A “patient pathway” framework—defined as the sequence of clinically relevant events from symptom onset through diagnosis, reperfusion, and early recovery—can help identify real-world points of failure and opportunities for system-level improvement. In this narrative review, we contrast an “ideal” STEMI pathway with the pathways commonly observed in routine practice for patients treated with primary PCI, and we contextualize deviations from best practice from patient, clinician, health service, and societal perspectives. From the patient's perspective, the priority is rapid symptom recognition and seeking care; however, delays are frequent, particularly in individuals with mild, atypical, or non-classical presentations, prolonging total ischemic time and increasing myocardial injury. Clinicians aim to diagnose STEMI promptly and initiate evidence-based therapy and reperfusion without delay, yet diagnostic uncertainty and competing differentials can contribute to missed or late diagnoses. Health systems seek to provide timely, efficient, and cost-effective emergency revascularization, but performance is influenced by pre-hospital logistics, triage, catheterization laboratory availability, and inter-hospital transfer processes. At the societal level, STEMI imposes substantial mortality, morbidity, and economic burden through premature death and disability. We synthesize evidence on delays to revascularization, misdiagnosis, populations at risk for atypical presentation, and pragmatic interventions to improve care. We conclude that pathway-based analyses offer a structured approach to defining desirable STEMI care trajectories and to reducing missed opportunities for better outcomes.

Aims: Contemporary guidelines support the use of a pharmaco-invasive (PI) strategy with immediate transfer to a percutaneous coronary intervention (PCI)-capable hospital for ST-elevation myocardial infarction when a timely primary PCI (pPCI) is unattainable. However, when reperfusion with fibrinolysis fails to occur, rescue PCI is recommended. Methods and results: In a pre-specified analysis from STREAM-2, we explored patients randomized to PI treatment and compared those receiving half-dose tenecteplase and required rescue intervention to those with successful fibrinolysis undergoing scheduled angiography. To provide context for those randomized to pPCI, we also explored the relationship between sites of randomization, i.e. community hospital (CH) vs. ambulance on clinical outcomes. Resolution of ST-elevation following angiography and the composite of 30-day all-cause death, shock, heart failure, and reinfarction, as well as safety, reflected by stroke and non-intracranial bleeding, were measured. Of the 583 patients in the current study, 168 patients required rescue intervention (43.5%), 218 patients had successful fibrinolysis scheduled for angiography, and 197 were randomized to pPCI. Rescue PCI patients, compared with those undergoing scheduled angiography, had less ST resolution ≥50% (76.3 vs. 92.5%, P < 0.001) and worse clinical composite outcomes at 30 days (16.7 vs. 6.0%, P < 0.001) with a higher risk of intracranial haemorrhage (2.4 vs. 0.5%). Intermediate outcomes were observed for patients undergoing pPCI (ST resolution ≥50%: 78.7%; a 30-day composite outcome: 12.2%). Rescue intervention deployed in CH patients required 10 min longer compared with ambulance patients; however, there was a similar ST resolution of ≥50% (72.2 vs. 80.5%, P = 0.219) and comparable 30-day composite outcomes [17.6 vs. 15.7%, relative risk (RR) 0.97, 95% confidence interval (CI) 0.50–1.87], irrespective of location. Primary PCI required 48 min longer in CH patients, but resulted in similar outcomes to ambulance patients (ST resolution ≥50%: 77.0 vs. 80.2%, P = 0.595; 30-day composite outcome: 9.3 vs. 15.6%, RR 1.57, 95% CI 0.72–3.41, respectively). Conclusion: Contemporary PI with half-dose tenecteplase in older patients requiring rescue intervention led to less ST resolution and worse 30-day outcomes compared with those with successful fibrinolysis receiving scheduled angiography. Notably, delays to deploying rescue PCI in CH patients were shortened over those previously achieved thereby resulting in similar outcomes to those randomized in the ambulance. Our results reinforce the benefits of functional hub and spoke models with rapid transfer to a PCI-capable facility.

Background: Invasive coronary angiography (ICA) is the gold standard in the diagnosis of coronary artery disease (CAD). Being invasive, it carries rare but serious risks including myocardial infarction, stroke, major bleeding, and death. A large proportion of elective outpatients undergoing ICA have nonobstructive CAD, highlighting the suboptimal use of this test. Coronary computed tomographic angiography (CCTA) is a noninvasive option that provides similar information with less risk and is recommended as a first-line test for patients with low-to-intermediate risk of CAD. Leveraging artificial intelligence (AI) to appropriately direct patients to ICA or CCTA based on the predicted probability of disease may improve the efficiency and safety of diagnostic pathways. Objective: he CarDIA-AI (Coronary computed tomographic angiography to optimize the Diagnostic yield of Invasive Angiography for low-risk patients screened with Artificial Intelligence) study aims to evaluate whether AI-based risk assessment for obstructive CAD implemented within a centralized triage process can optimize the use of ICA in outpatients referred for nonurgent ICA. Methods: CarDIA-AI is a pragmatic, open-label, superior randomized controlled trial involving 2 Canadian cardiac centers. A total of 252 adults referred for elective outpatient ICA will be randomized 1:1 to usual care (directly proceeding to ICA) or to triage using an AI-based decision support tool. The AI-based decision support tool was developed using referral information from over 37,000 patients and uses a light gradient boosting machine model to predict the probability of obstructive CAD based on 42 clinically relevant predictors, including patient referral information, demographic characteristics, risk factors, and medical history. Participants in the intervention arm will have their ICA referral forms and medical charts reviewed, and select details entered into the decision support tool, which recommends CCTA or ICA based on the patient’s predicted probability of obstructive CAD. All patients will receive the selected imaging modality within 6 weeks of referral and will be subsequently followed for 90 days. The primary outcome is the proportion of normal or nonobstructive CAD diagnosed via ICA and will be assessed using a 2-sided z test to compare the patients referred for cardiac investigation with normal or nonobstructive CAD diagnosed through ICA between the intervention and control groups. Secondary outcomes include the number of angiograms avoided and the diagnostic yield of ICA. Results: Recruitment began on January 9, 2025, and is expected to conclude in mid to late 2025. As of April 14, 2025, we have enrolled 81 participants. Data analysis will begin once data collection is completed. We expect to submit the results for publication in 2026. Conclusions: CarDIA-AI will be the first randomized controlled trial using AI to optimize patient selection for CCTA versus ICA, potentially improving diagnostic efficiency, avoiding unnecessary complications of ICA, and improving health care resource usage. Trial Registration: ClinicalTrials.gov NCT06648239; https://clinicaltrials.gov/study/NCT06648239/ International Registered Report Identifier (IRRID): DERR1-10.2196/71726

BACKGROUND Invasive coronary angiography (ICA) is the gold standard in the diagnosis of coronary artery disease (CAD). Being invasive, it carries rare but serious risks including myocardial infarction, stroke, major bleeding, and death. A large proportion of elective outpatients undergoing ICA have nonobstructive CAD, highlighting the suboptimal use of this test. Coronary computed tomographic angiography (CCTA) is a noninvasive option that provides similar information with less risk and is recommended as a first-line test for patients with low-to-intermediate risk of CAD. Leveraging artificial intelligence (AI) to appropriately direct patients to ICA or CCTA based on the predicted probability of disease may improve the efficiency and safety of diagnostic pathways. OBJECTIVE he CarDIA-AI (Coronary computed tomographic angiography to optimize the Diagnostic yield of Invasive Angiography for low-risk patients screened with Artificial Intelligence) study aims to evaluate whether AI-based risk assessment for obstructive CAD implemented within a centralized triage process can optimize the use of ICA in outpatients referred for nonurgent ICA. METHODS CarDIA-AI is a pragmatic, open-label, superior randomized controlled trial involving 2 Canadian cardiac centers. A total of 252 adults referred for elective outpatient ICA will be randomized 1:1 to usual care (directly proceeding to ICA) or to triage using an AI-based decision support tool. The AI-based decision support tool was developed using referral information from over 37,000 patients and uses a light gradient boosting machine model to predict the probability of obstructive CAD based on 42 clinically relevant predictors, including patient referral information, demographic characteristics, risk factors, and medical history. Participants in the intervention arm will have their ICA referral forms and medical charts reviewed, and select details entered into the decision support tool, which recommends CCTA or ICA based on the patient’s predicted probability of obstructive CAD. All patients will receive the selected imaging modality within 6 weeks of referral and will be subsequently followed for 90 days. The primary outcome is the proportion of normal or nonobstructive CAD diagnosed via ICA and will be assessed using a 2-sided z test to compare the patients referred for cardiac investigation with normal or nonobstructive CAD diagnosed through ICA between the intervention and control groups. Secondary outcomes include the number of angiograms avoided and the diagnostic yield of ICA. RESULTS Recruitment began on January 9, 2025, and is expected to conclude in mid to late 2025. As of April 14, 2025, we have enrolled 81 participants. Data analysis will begin once data collection is completed. We expect to submit the results for publication in 2026. CONCLUSIONS CarDIA-AI will be the first randomized controlled trial using AI to optimize patient selection for CCTA versus ICA, potentially improving diagnostic efficiency, avoiding unnecessary complications of ICA, and improving health care resource usage. CLINICALTRIAL ClinicalTrials.gov NCT06648239; https://clinicaltrials.gov/study/NCT06648239/ INTERNATIONAL REGISTERED REPORT DERR1-10.2196/71726