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Objective To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Design Retrospective diagnostic accuracy study. Setting Three sites within a single US tertiary health system. Participants Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 included 1426 patients who underwent transcatheter aortic valve replacement. Primary and secondary outcome measures The reference standard was clinician manual chart adjudication. Outcomes included ischaemic stroke or transient ischaemic attack, myocardial infarction (MI), heart failure (HF) exacerbation or hospitalisation and a composite major adverse cardiovascular events (MACE) outcome. Automated retrieval methods included International Classification of Diseases (ICD) codes, primary diagnosis, problem list and a zero-shot LLM workflow. Area under the (receiver operating characteristic) curve (AUC), sensitivity, specificity and net reclassification improvement were assessed. Results In Cohort 1, the LLM achieved the highest AUC for stroke (0.920; 95% CI 0.881 to 0.958), MI (0.938; 95% CI 0.905 to 0.971) and composite MACE (0.880; 95% CI 0.854 to 0.907), whereas ICD-based retrieval had a higher AUC for HF (0.882; 95% CI 0.845 to 0.918 vs 0.873; 95% CI 0.831 to 0.914). In Cohort 2, the LLM achieved the highest AUC for all evaluated outcomes: stroke (0.915; 95% CI 0.862 to 0.968), MI (0.928; 95% CI 0.839 to 1.000), HF (0.844; 95% CI 0.803 to 0.884) and composite MACE (0.862; 95% CI 0.829 to 0.895). In Cohort 1, differences in AUC between the LLM and ICD methods were not statistically significant across outcomes, whereas in Cohort 2 the LLM showed significantly higher AUC for stroke and composite MACE. Conclusion In this multisite retrospective validation study, the LLM-assisted workflow showed strong but context-dependent performance for identifying cardiovascular events from the EMR. Performance varied by outcome and cohort, and ICD-based retrieval remained competitive for some use cases. These findings support a complementary role for LLM-assisted extraction in retrospective cardiovascular outcomes research.

Background:The development of symptoms or left ventricular dysfunction in severe aortic stenosis (AS) is associated with high morbidity and mortality. While elective transcatheter aortic valve replacement (TAVR) is supported by robust randomized evidence, data on TAVR performed in non-elective settings (urgent, emergent, or salvage) remain limited and heterogeneous. In these settings, outcomes may be influenced more by baseline clinical severity than by procedural factors. This study aimed to systematically evaluate the safety and effectiveness of non-elective TAVR and to compare the associated outcomes with those of elective TAVR.Methods:This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and registered with PROSPERO. The PubMed, Embase, and Scopus databases were searched for studies published after 2002 evaluating urgent, emergent, or salvage TAVR in adult patients with severe AS. Eligible studies were required to include elective TAVR as a comparator. Outcomes included mortality and major procedural complications. Prespecified era-based analyses and sensitivity analyses excluding studies at critical risk of bias were performed. Risk of bias was assessed using the ROBINS-I tool.Results:A total of 17 observational studies published between 2015 and 2025 were included, comprising 215,141 patients. Compared with elective patients, those undergoing non-elective TAVR had more advanced heart failure, higher NYHA class, greater comorbidity burden, and higher surgical risk scores, whereas baseline echocardiographic severity of AS was similar. Pooled outcomes for non-elective TAVR showed in-hospital, 30-day, and 1-year mortality rates of 5.1%, 12.4%, and 26.7%, respectively. Non-elective TAVR was associated with higher mortality at all time points compared with elective procedures; meanwhile, rates of stroke, vascular complications, and permanent pacemaker implantation were similar, while major bleeding and acute kidney injury were more frequent. Era-based analyses showed a stable relative mortality risk over time.Conclusions:Although non-elective TAVR is associated with worse outcomes than elective procedures, these differences appear to be largely driven by baseline clinical severity rather than by the transcatheter intervention. In unstable patients with severe AS, non-elective TAVR remains the most effective definitive treatment option compared with balloon aortic valvuloplasty or conservative management.The PROSPERO Registration:CRD420251239620, https://www.crd.york.ac.uk/PROSPERO/view/CRD420251239620.

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes.

Coronary microvascular dysfunction (CMD) has emerged as a crucial contributor to cardiovascular morbidity and mortality, particularly in patients with ischemia and non-obstructive coronary arteries (INOCA). The condition arises from a complex interplay of structural and functional abnormalities within the small coronary vessels, driven by underlying molecular mechanisms including endothelial nitric oxide synthase (eNOS) uncoupling, oxidative stress, and chronic inflammation. Lipid metabolism plays a central role in this pathology, especially in the setting of elevated low-density lipoprotein cholesterol (LDL-C). Furthermore, the protective capacity of high-density lipoprotein (HDL) is increasingly understood to depend on its functionality rather than absolute levels, as it can become dysfunctional and pro-inflammatory in pathological states. Emerging evidence has identified lipoprotein(a) [Lp(a)] and triglyceride-rich lipoproteins as significant, independent contributors to microvascular injury. Comprehensive clinical assessment of microvascular dysfunction therefore requires integration of advanced lipid profiling, including apolipoprotein B (ApoB), [Lp(a)], and the triglyceride-glucose (TyG) index with invasive and non-invasive measures of coronary flow reserve to more precisely stratify risk. In this narrative review, we synthesize current observational, mechanistic, and early interventional data linking diverse lipid phenotypes to coronary microvascular dysfunction. We propose a concept of lipid-driven CMD endotypes, such as ApoB-/particle overload, dysfunctional HDL, Lp(a)-mediated risk, and metabolic/TyG-high states, and map these to a practical, mechanism-informed management framework. While intensive LDL-C lowering with high-intensity statins and combination therapy remains guideline-directed care for high-risk patients, evidence for dedicated microvascular benefit from newer lipid and cardiometabolic agents is still largely hypothesis-generating. A personalized approach that aligns lipid phenotyping, CMD endotyping, and existing guideline-based therapies may help refine risk assessment and inform future trials.

Background In low-gradient severe aortic stenosis (AS), reduced left ventricular ejection fraction (LVEF <50%) is practically used to define low flow and prompt dobutamine stress echocardiography to discern pseudo-severe AS. In patients with preserved LVEF, stroke volume index (SVI) <35 mL/m² is typically used. However, both are volume-based surrogates. Transaortic flow rate (TAFR), calculated as stroke volume divided by left ventricular ejection time, may better reflect true flow. Nonetheless, comparative data between TAFR and established metrics remain limited. We aimed to evaluate the prognostic value of TAFR in symptomatic low-gradient severe AS. Methods We retrospectively identified patients with low-gradient severe AS (AVA ≤1 cm 2 and peak velocity (Vmax) <4 m/s or mean gradient (MG) <40 mm Hg) who underwent transcatheter aortic valve replacement at Mayo Clinic sites (2017–2023). The primary outcome was 1-year all-cause mortality. Survival was assessed using Kaplan-Meier and Cox proportional hazards models. Results Among 475 patients included (mean age 85±8 years; 49% women), 242 (51%) had TAFR <220 mL/s, 165 (35%) had EF <50%, and 221 (47%) had SVI <35 mL/m 2 . Low TAFR was significantly associated with higher 1-year mortality even after stratifying by EF or SVI. In multivariate analysis, TAFR was an independent predictor of mortality (HR 2.38; 95% CI 1.19 to 4.76, p=0.014) after adjusting for reduced LVEF, low SVI, gender, chronic kidney disease and mitral and tricuspid regurgitation. Conclusions In patients with symptomatic low-gradient severe AS, low TAFR, not SVI or LVEF, is independently associated with mortality and may offer more accurate measure of flow state for clinical staging.

Abstract Aims Obesity is linked with worse outcomes in hypertrophic cardiomyopathy (HCM). While both bariatric surgery (BS) and GLP-1 receptor agonists (GLP-1RA) promote weight loss and may improve cardiometabolic risk, their comparative outcomes in HCM remain unclear. Methods and results Adults with HCM and body mass index (BMI) ≥ 30 kg/m were divided into two cohorts: (i) GLP-1 RA users who never had BS, and (ii) BS patients who never received GLP-1 RA. Propensity score matching (1:1) was performed to balance demographics, comorbidities, and medications. Outcomes over 24 months include all-cause mortality, acute heart failure (HF) hospitalization, arrhythmias, change in BMI, and cardiovascular symptoms. Hazard ratios with 95% confidence intervals were estimated using Cox models. Of 5002 patients, 955 were included in each matched cohort after matching (mean age 54 ± 13 years, 70% female, BMI 40.0 kg/m). GLP-1 RA was associated with a significantly lower observed rate of all-cause mortality compared with BS (2.0% vs. 4.4%; HR 0.46; 95% CI 0.27–0.80; P = 0.004) and HF hospitalization (8.7% vs. 14.0%; HR 0.60; 95% CI 0.46–0.79; P &lt; 0.001). Similarly, GLP-1 RA use was associated with a lower incidence of composite cardiovascular outcomes (10.7% vs. 17.2%; HR 0.60; 95% CI 0.47–0.77; P &lt; 0.001). Rates of arrhythmia and cardiovascular symptoms were comparable between the two groups. BMI reduction was greater after bariatric surgery (−4.0 vs. −2.6 kg/m; P &lt; 0.001). Conclusion In obese patients with HCM, GLP-1 RA therapy was associated with lower all-cause mortality and fewer HF events compared with BS. Future studies are warranted to confirm our findings.

Abstract Background Treatment with immune-checkpoint inhibitors (ICI) may be associated with adverse cardiac events. Troponin elevation is part of the diagnostic criteria for ICI related myocarditis (ICIrM) and myocardial infarction (MI), however there is limited data for causes of troponin elevation independent of MI and ICIrM and their clinical outcomes with ICI therapy. Purpose To assess the diagnostic and prognostic value of high-sensitivity troponin T (hs-TnT) in patients treated with ICI based on different etiologies and association with cardiac events. Methods All patients treated with ICI who had hs-TnT measured at a multi-center institution from 2011 to 2022 were included. Medical records were reviewed for baseline characteristics and long-term outcomes, including cardiac death and MACE (MI, stroke, heart failure [HF]), and cause of hs-TnT elevation. Risk of cardiac events and mortality were compared for the different etiologies of hs-TnT elevation. Results Of 5,423 patients treated with ICI, 1669 had post-ICI hs-TnT measurement. Mean age was 68.7±11.3 years, 58.3% were male, with follow-up through one year from ICI first administration. Elevated hs-TnT was seen in 1136 patients, with the different causes for elevation depicted in Figure 1, panel A. ICIrM patients exhibited the highest median hs-TnT value of 364 ng/L (IQR 1096), followed by a median of 98 ng/L for MI/Type 2 ischemia, 61 ng/L for infection/sepsis, 51 ng/L for pulmonary embolus, 50 ng/L for HF and 27 ng/L for indeterminate causes, Figure 1, panel B. Additionally, median time from first ICI dose to hs-TnT elevation according to each of the diagnoses are depicted in Figure 1, panel C, with ICIrM showing the shortest median time of 44 days (IQR 74). Hs-TnT elevation in patients with ICIrM had the highest risk for cardiac death (HR 52.7, 95% CI 11.7-238.0, p&lt;0.001), followed by hs-TnT elevation due to HF (HR 15.9, 95% CI 2.9-87.0, p=0.001), MI/type 2 ischemia (HR 11.6, 95% CI 2.4-56.1, p=0.002), and infection/sepsis (HR 5.7, 95% CI 1.0-31.0, p=0.045) compared to those with no hs-TnT elevation, Figure 2, panel A. Similarly, patients with ICIrM had highest risk for MACE (HR 8.2, 95% CI 4.4-15.3, p&lt;0.001), followed by increased hs-TnT due to MI/type 2 ischemia (HR 8.1, 95% CI 5.0-13.1, p&lt;0.001), HF (HR 7.6, 95% CI 4.2-13.7, p&lt;0.001), pulmonary embolism (HR 5.1, 95% CI 2.5-10.8, p&lt;0.001), infection/sepsis (HR 4.1, 95% CI 2.4-7.0, p&lt;0.001), and indeterminate cause (HR 2.4, 95% CI 1.5-3.8, p&lt;0.001), Figure 2, panel B. Conclusions Hs-TnT measurements have prognostic value for cardiac events in cancer patients receiving ICI therapy, including in those without ICIrM. Patients with ICIrM demonstrated the highest median hs-TnT levels and the poorest outcomes, with a graded response observed; the worst prognosis was seen in patients with hs-TnT elevation due to ICIrM, followed by other causes, and those without hs-TnT elevation had the best prognosis.Figure 1 Figure 2

Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population; however, their prognostic value in KTx recipients is relatively unknown. Methods Retrospective analysis was conducted on KTx recipients without AF, with at least one pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High-risk scores were associated with true new-onset AF at 30 days (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.

ABSTRACT Background Cardiac amyloidosis (CA) is frequently complicated by atrial fibrillation (AF), yet outcomes after left atrial appendage occlusion (LAAO) in this population remain poorly defined. Methods We conducted a retrospective TriNetX study of adults with AF undergoing LAAO, comparing patients with and without CA after 1:1 propensity matching. Outcomes were assessed using Kaplan–Meier analyses and Cox regression. Results Among 532 matched pairs, mortality and major adverse cardiovascular events were similar between groups, whereas major bleeding was higher in CA (HR 1.90). Conclusions LAAO yields comparable ischemic outcomes in CA, though bleeding risk is increased.