Clinical Risk Research Articles

Individual level risk factors associated with episodes of heat as recorded within primary care records: a time-stratified case-cross over study

Abstract Background A key recommended action outlined in Heat-Health Action Plans is to identify individuals at risk and deploy targeted interventions, however this is not routinely undertaken. A major contributing factor is a lack of understanding of individual-level risk factors to support an evidence-based approach to targeted prevention. Aim To use electronic health data to identify individual level characteristics associated with increased risk of mortality during heatwaves in England. Methods A time-stratified case-crossover analysis was undertaken of individual-level clinical and socio-environmental (SE) risk factors. Conditional logistic regression was used to characterise associations between temperature and the risk of death on hot days, with analysis stratified by potential risk modifying factors. Results Heat mortality risk was modified by a large range of chronic conditions, with cardio-respiratory, mental health and cognitive function conditions, diabetes and Parkinson’s, all increasing risk. Prescribed medications also increased odds of death during heatwaves. Heat mortality risk was modified by a range of SE determinants of health investigated, with ORs increasing with age; OR differences by sex, ethnicity and living arrangement; an increasing trend in ORs by alcohol intake and body mass index, excluding the obese-3 group. Results suggest the least deprived have the lowest OR and the most deprived have the highest. Conclusions This study has important implications for patient medication management during heat events, incorporating heat-risk considerations into other health policies. The results also provide important evidence on the role of disadvantage in driving the inequitable distribution of climate impacts, and the need for better socio-economic data sets linked to health records. The findings highlight the importance of incorporating an assessment of individual SE circumstances when prioritising patients at highest risk during heat events.

Increased risk of stroke in non-rheumatic valvular disease associated with antiphospholipid syndrome (APS): a US nationwide study 2017-2019

Abstract Introduction Antiphospholipid syndrome (APS) is an autoimmune syndrome characterized by the presence of antiphospholipid autoantibodies, arterial and venous thrombosis, recurrent pregnancy loss and thrombocytopenia. Cardiac manifestations are also common which include coronary artery disease (CAD), non rheumatic valvular heart disorders (NRVHD), myocardial dysfunction, intracardiac thrombi and pulmonary hypertension. Valvular lesions occur likely due to immune complex deposition on the valves, causing fibrosis, calcification and subsequently valvular deformities. Literature review suggests prevalence of NRVHD in APS population is 30% and left sided valves are more involved than right. An increased risk of stroke has also been seen in APS patients with valvular disorders. Purpose Studies report variations in incidence of NRVHD in association with APS. To estimate the prevalence of NRVHD and their correlation with APS, we conducted a retrospective database analysis of hospitalizations across the United States. Additionally we investigated whether this association indicates a increased risk of ischemic stroke in this specific subpopulation. Methods The national inpatient sample (NIS) from 2017-2019 was queried for adult patients (age>18 yrs) admitted with a primary or secondary diagnosis of APS. These patients were stratified for different NRVHD including tricuspid stenosis, tricuspid insufficiency, pulmonary stenosis, pulmonary regurgitation, mitral stenosis, mitral insufficiency (MI), aortic stenosis (AS), and aortic regurgitation. Baseline risk factors and cardiac complications were compared. Multiple regression analysis was performed to study the association of NRVHD with stroke as the primary outcome in the subpopulation of APS hospitalizations. Results A total of 567600 adult patients hospitalized with diagnosis of APS were identified from the NIS 2017-2019 database. Among those, 33,095 had NRVHD with a mean age of 69 years. Of these, prevalence of males was 52.41% compared to females of 47.59%. In the cohort of APS, patients with NRVHD had a higher prevalence of coronary artery disease (43.78% vs 20.41%), diabetes mellitus (30.65% vs 27.56%), prior stroke (10.42% vs 7.78%) than those without valvular disorders. Among the APS population the most prevalent NRVHD were mitral insufficiency (MI), 2.45% and aortic stenosis (AS), 2.13%. In the cohort of NRVHD in APS hospitalizations, the odds of stroke was found to be 1.3 times higher with statistical significance (confidence interval (CI): 1.13-1.48, p <0.001) adjusted for relevant clinical and demographic risk factors such as tobacco abuse, CAD, diabetes mellitus, prior stroke. Conclusion This study shows 6.2% prevalence of NRVHD in APS population, of which MI and AS were common. NRVHD is also associated with higher odds of stroke in APS. Further studies are warranted in identifying the risk of stroke with NRVHD in APS population and emphasizing the need for early detection.

The added value of deep learning to submaximal exercise electrocardiogram for the 10-year prediction of major adverse cardiovascular and cerebrovascular events

Abstract Background The added value of exercise ECG data to traditional risk factors in predicting cardiovascular events remains unclear. Deep learning of ECG signals has demonstrated state-of-the-art performance in detecting subtle abnormalities indicative of cardiovascular disease. We investigated whether deep learning analysis of exercise ECGs could improve the prediction of major cardiovascular and cerebrovascular events (MACCE) with respect to established risk models in a large population-based cohort. Purpose To assess the added value of submaximal exercise ECG measurements to established cardiovascular risk prediction models. Methods We obtained ECG recordings from 41,076 UK Biobank participants without cardiovascular disease (CVD) who underwent a submaximal exercise ECG test. We obtained ECG recordings from 41,076 UK Biobank participants without cardiovascular disease (CVD) who underwent a submaximal exercise ECG test. We created two deep neural network ECG risk scores: one derived from conventional ECG parameters, measured at rest, peak exercise and late-stage recovery, and another based on the complete ECG (Q-ECG). We assessed the added value of conventional ECG parameters and Q-ECG risk scores to the UK's current prediction algorithm (QRISK3). We estimate the association between ECG parameters and MACCE, using Cox Proportional hazard models, following adjustment for traditional risk factors. All models were internally validated via 5-fold cross-validation and 1000 bootstrap iterations. Predictive performance was evaluated using Harrel's C-index, Net Reclassification Index (NRI) and net benefit. Findings: Incident MACCE was reported in 4,082 (9.9%) individuals in the study population and 3,463(9.7%) individuals with valid ECG parameters over a median follow-up period of 12.5 years. We found combined conventional ECG and Q-ECG scores were independently associated with MACCE, following adjustment for multiple testing: adjusted hazard ratio [HZ] = 1.76 (95% CI:1.63-1.91); HZ = 1.14 (95% CI:1.10-1.18), respectively, per standard deviation increase. Both conventional ECG parameters and Q-ECG were predictive of MACCE, independent of clinical risk factors, C-index = 0.64 (95%CI 0.63-0.65); net benefit = 0.09(95% CI 0.07 - 0.11) and C-index = 0.56 (95% CI 0.55-0.57); net benefit = 0.07(95% CI 0.05 - 0.09). ECG measurement's modestly improved model discrimination over the bassline QRISK3 risk score when combined with QRISK3 risk factors for conventional markers and Q-ECG score, respectively; ΔC-index 0.03 (95% CI: 0.02 – 0.04) and ΔC-index 0.03 (95% CI: 0.02 – 0.03); However, we observed no significant improvements in classification at the current recommended threshold of 10%. Conclusion In individuals without a history of prior cardiovascular disease, ECG measures independently predict the risk of MACCE. When combined with QRISK3, neural-network-derived ECG risk scores marginally improve cardiovascular risk prediction over QRISK3.

Severe aortic stenosis is associated with left atrial prothrombotic flow changes that persists despite successful aortic valve replacement

Abstract Background Severe aortic stenosis (AS) is associated with ischaemic stroke independently of atrial fibrillation (AF) or other causes. Even after aortic valve replacement (AVR), patients remain affected by up to 15% five-year rate of incident ischaemic strokes independently of AVR-related complications and with knowledge gaps in the underlying pathophysiology. Purpose To assess whether severe AS leads to prothrombotic left atrial (LA) flow changes with reduction of LA velocities and vorticity which do not normalise after AVR. Methods Seventy-seven participants without history of AF were recruited in this study: N=40 with severe AS (60% male; age 73 ±10 years, BMI 28 ±5 Kg/m2, median CHA2DS2VASc = 3) and N=37 controls with similar clinical risk profile (51% male; age 71 ±6 years, BMI 28 ±5 Kg/m2, median CHA2DS2VASc = 3). All participants underwent a cardiovascular magnetic resonance (CMR) scan to quantify left ventricular (LV) volumes, mass, and ejection fraction (LVEF), LV global longitudinal strain (GLS) as a sensitive marker of longitudinal dysfunction, LA maximum volume and emptying fraction (LAEF), LA blood flow peak velocity and vorticity by 4-dimensional flow CMR. Out of the 40 patients with severe AS, N=23 (58%) underwent a successful AVR and were followed-up with a repeat CMR scan at approximately 6 months after surgery. Results When compared to matched controls, patients with severe AS displayed significantly higher LV mass, smaller LA and LV volumes (i.e. concentric LV hypertrophy), significantly reduced GLS but higher (more hyperdynamic) LVEF (all p<0.05), as expected. LAEF (i.e. global LA function) was not significantly different from controls but despite this, severe AS was associated with slightly lower LA peak flow velocity (mean 0.25 vs. 0.28 m/sec respectively) and lower LA flow vorticity (mean 16.6 vs. 21.1 radians) compared to controls (both p<0.05). Successful AVR led to complete regression of LV hypertrophy (p<0.05) with LV mass that was no longer different from controls (p>0.05). Nevertheless, post-AVR patients displayed a persistent adverse morpho-functional phenotype characterised by persistently smaller LA and LV volumes, persistently hyperdynamic LV function with reduced GLS (all p<0.05), as well as adverse LA flow changes with significantly lower velocity (mean 0.24 vs 0.28 m/sec, p=0.003) and vorticity when compared to controls (17.1 vs 21.1 radians, p<0.001). Conclusions Patients with severe AS display adverse LA physiology and structure (prothrombotic LA flow characteristics in smaller LA size) and LV changes (smaller size with reduced longitudinal function and hyperdynamic function), which persist after successful AVR despite normalisation of the LV hypertrophy.Figure 1

Machine-learning models to predict the risk of recurrent VT following catheter ablation in patients with structural heart disease

Abstract Background Ventricular tachycardia (VT) is a life-threatening arrhythmia that can complicate structural heart disease. While catheter ablation is effective in treating this condition, the recurrence of VT after the procedure continues to be a concern. Consequently, there is a need for a reliable system to evaluate the likelihood of arrhythmia recurrence following an ablation. Objective To predict recurrence or repeat ablation after a VT catheter ablation procedure using machine learning (ML) models. Methods We conducted a single-center, retrospective study of all patients undergoing catheter ablation for scar-related VT between 2012 and 2022. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Python version 3.8.5 for exploratory data analysis (EDA) and visualization. Using six different ML models from the training set (90:10 split), we used 34 variables to predict the primary outcome, including VT recurrence or repeat catheter ablation. The accuracy of these models was compared using ROC curves based on their performance on the test set. Results Out of 508 VT ablation procedures, 261 experienced a recurrence. Support Vector Classifier (SVM) performed the best, having an AUC of 0.73, followed by logistic regression at 0.69 (Figure A). SVM yielded sensitivity, specificity, positive and negative predictive values of 0.73, 0.73, 0.79, and 0.67, respectively. The F1 score was 0.70. Among the factors with the highest feature importance in the model, the most influential variable was the proceduralist’s experience level, followed by QRS width at baseline, LVEF, and patient’s age (Figure B). Conclusion Our model can predict recurrent VT after ablation with reasonable accuracy, outperforming the existing clinical risk scores significantly. Larger training sample sizes will help achieve more robust ML models and improve predictive accuracy further.Figure A: ROC curve depicting the AUROCB. Feature importance graph

Proteomic and genomic evaluation of plasma protein biomarkers for atrial fibrillation

Abstract Background Atrial fibrillation (AF) is a common arrhythmia and an important cause of ischemic stroke. Several plasma proteins have been suggested as biomarkers for the risk of AF and subsequent stroke in previous studies, but validation in independent cohorts is needed. Large-scale integration of proteomics with genetic and clinical data holds potential for refinement of disease prediction and risk stratification. Purpose This study aimed to evaluate previously suggested plasma protein biomarkers of AF and ischemic stroke using plasma protein measurements in a large cohort. Methods Ten tentative protein biomarkers previously associated with AF or ischemic stroke in AF patients, were evaluated in a nested case-control study comprising 49,757 individuals from the UK biobank (UKB). Plasma proteins were measured by normalized protein expression on the Olink platform. Outcomes were incident AF and ischemic stroke. The proteins were evaluated in Cox regression models adjusted for sex, age, and clinical risk factors. To assess whether the proteins were independent predictors, they were evaluated in sensitivity analyses with additional adjustment for NT-proBNP levels. Polygenic risk scores were calculated for plasma protein concentrations using the PRS-CS-auto algorithm and tested for association with AF or ischemic stroke in an independent subset of the UKB cohort without plasma protein measurements (n=379,392). Results All ten plasma proteins were associated with incident AF in the primary model, and eight were associated with incident ischemic stroke (P-values < 0.0025 for all). Adjusted for NT-proBNP levels, the proteins interleukin-6, GDF15, angiopoietin 2, and Troponin-I remained independently associated with AF, and the proteins GDF15, Cystatin-C, interleukin-6, ADAMTS13, and FGF23 remained independently associated with ischemic stroke. Polygenic risk scores for NT-proBNP and angiopoietin 2 were associated with increased rates of AF. No polygenic scores were significantly associated with ischemic stroke. Conclusion Large-scale proteomic evaluation validated ten plasma proteins associated AF and found eight to be associated with ischemic stroke. Seven proteins were associated with AF or ischemic stroke independently of NT-proBNP levels. Additional genomic evaluation provided evidence of associations between incident AF and genetically predicted plasma levels of angiopoietin 2 and NT-proBNP. As proteomic analyses of the plasma proteome become cheaper and more readily available, these findings could contribute to future risk stratification and selection of individuals for AF screening programs.Proteins associated with AFProteins associated with ischemic stroke

AI-enabled cardiac chambers volumetry in coronary calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The Multi-Ethnic Study of Atherosclerosis

Abstract Introduction Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score. Methods We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% women, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC versus NT-proBNP, Agatston score, and 10 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP) for predicting incident HF over 15 years. Results Over 15 years of follow-up, 256 HF events accrued. The AUC for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free Net Reclassification Index (NRI) significantly improved on clinical risk factors (0.32), NT-proBNP (0.46), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001). Conclusion AI-CAC significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

Telehealth-aided outpatient management of acute heart failure in a specialist virtual ward compared to standard care

Abstract Background/Introduction Acute decompensated heart failure (ADHF) leads to hospitalisations, frequent re-hospitalisations and mortality. The safety and efficacy of telehealth-guided outpatient ADHF management (virtual ward-VW) as an alternative to hospitalisation has not been assessed previously. Aim The aim of this study was to assess the safety and outcomes of our acute heart failure virtual ward (HFVW) pathway (Figure 1) when compared to hospitalised ADHF patients. Methods This cohort study (May 2022-October 2023) assessed the outcomes of telehealth-guided outpatient ADHF management using bolus intravenous furosemide in a HF-specialist VW. We compared baseline patient characteristics, NTproBNP, ejection fraction, NYHA Class, clinical risk score (Get With the Guidelines-Heart Failure-GWTG-HF), comorbidities (Charlson Co-morbidity Index-CCI), frailty (Rockwood Clinical Frailty Score-CFS), HF therapies and measured clinical outcomes at 1, 3, 6 and 12 months (re-hospitalisations, mortality) in the HFVW cohort versus standard care (ADHF patients managed without telehealth in 2021). Results 554 HFVW ADHF patients (age 73.1±10.9 years; 46% female) were compared with 402 ADHF patients (74.2±11.8; p=0.15 and 49% female) in the standard care cohort (SC). Despite similar baseline patient characteristics, GWTG-HF score, CCI and CFS, re-hospitalisations were significantly lower in the HFVW compared to standard care (1 month - 11.6% vs. 21%, p=0.002; 3 months - 20.4% vs. 30%, p=0.001; 6 months -29.3% vs 41%, p=0.02 and 12 months-48% vs. 57%,p=0.03) whereas mortality was lower at 1 month (6% vs. 14%; p<0.001), 3 months (10.5% vs. 15%; p=0.02) and 6 months (15.5% vs. 21%; p=0.04) (Figure 2). Multivariate logistic regression analysis showed that an increased daily step count whilst on HFVW independently predicted reduced odds of re-hospitalisations at 1 month (OR 0.85; 95% CI 0.7-0.9; p=0.005), 3 months [OR 0.95 (0.93-0.98); p=0.003] and 1 month mortality [OR 0.85 (0.7-0.95), p=0.01]. Whereas CCI predicted adverse 12-month outcomes [OR 1.2 (1.1-1.4), p=0.03]. Higher GWTG-HF score independently predicted increased odds of re-hospitalisation [1-month OR 1.2 (1.1-1.3), p=0.01; 12-month OR 1.1, 1.05-1.2, p=0.03) as well as mortality [1-month OR 1.2 (1.1-1.4), p=0.01; 12-month OR 1.3 (1.1-1.7), p=0.02]. Similarly higher CFS also independently predicted increased odds of re-hospitalisations [1-month OR 1.5 (1.1-2.2), p=0.03; 12-month OR 1.9 (1.2-3, p=0.01] and mortality [1-month OR 2 (1.1-3.5), p=0.02; 12-month OR 2.6 (1.6-10); p=0.02] throughout the follow-up period. Conclusions A telehealth-guided specialist HFVW management strategy for ADHF may offer a safe and efficacious alternative to hospitalisation in suitable patients. Daily step count, GWTG, CCI and CFS can play a vital role in assessing suitability for VW and in predicting risk of adverse clinical outcomes.Heart Failure Virtual Ward PathwayMortality & Re-hospitalisations outcomes

Epidemiology, clinical characterization, and outcomes for heart failure with improved ejection fraction

Abstract Background Contemporary guidelines propose a new classification of heart failure with improved ejection fraction (HFimpEF). The prevalence of HFimpEF is expected to increase with the implementation of guideline-directed medical therapy (GDMT); however, there are limited data on the epidemiology of and outcomes for this novel clinical entity. Purpose We describe the prevalence, GDMT use and outcomes among patients with HFimpEF within a large, integrated healthcare delivery system. Methods We identified adults ≥18 years with incident HF with reduced EF (HFrEF ≤40%) between January 2013 and December 2018 from a large, integrated healthcare system serving >4.5 million members. HFimpEF was defined as known HFrEF with a follow-up EF measurement >40% within 12 months after incident HFrEF. Among HFimpEF patients, we examined GDMT use at discharge after incident HFrEF, incident HFimpEF, and at 6- and 12-months post-HFimpEF. From 12 months post initial HFrEF diagnosis, we calculated incidence rates of subsequent worsening HF (WHF) events (i.e., HF-related hospitalization, emergency department or urgent outpatient visits adjudicated using validated natural language processing-based algorithms) and all-cause death through December 31, 2019. We compared rates between patients with HFimpEF and those with persistent HFrEF (i.e., having all LVEF measurements within 12 months of incident HFrEF ≤40%) using Cox proportional hazards models adjusted for the EF value at incident HFrEF. Results Among 12,639 patients with incident HFrEF, 4,124 (33%) experienced HFimpEF within 12 months. Among patients with HFimpEF, use of β-blockers (82%) and angiotensin-converting enzyme inhibitor/angiotensin II receptor blockers (86%) was high, while angiotensin receptor-neprilysin inhibitor (1.2%) or mineralocorticoid receptor antagonist (28.2%) use was low. β-blockers, ACEi/ARB, and MRA use declined after incident HFimpEF (Figure, Panel A). Rates of WHF were 19.7 (95% Confidence Interval [CI]: 18.7 to 20.6) per 100 person-years among patients with HFimpEF vs. 40.0 (95% CI: 38.5 to 41.5) among patients with persistent HFrEF (hazard ratio [HR]: 0.55, 95% CI: 0.49 to 0.62; Figure, Panel B). Rates of death were 5.2 (95% CI: 4.7 to 5.7) and 8.4 (95% CI: 7.8 to 9.1) per 100 person-years among HFimpEF and persistent HFrEF, respectively (HR: 0.59, 95% CI: 0.52 to 0.67). Among patients with HFimpEF, those with an absolute EF improvement of ≥20% were less likely to experience WHF (HR: 0.82, 95% CI: 0.67 to 0.99) and death (HR: 0.85, 95% CI: 0.69 to 1.04) compared with those with <20% improvement. Conclusions One in three patients experienced improvement in EF within 12 months of initial HFrEF diagnosis. HFimpEF is associated with a reduced risk of WHF events and death, although these patients remain at significant clinical risk. Further research is necessary to assess the potential impact of sustained adherence to GDMT and therapeutic optimization on HFimpEF patient outcomes.

Reduced inflammation assessed by pericoronary adipose tissue is associated with coronary plaque calcification

Abstract Introduction Pericoronary adipose tissue attenuation (PCAT) quantified from coronary computed tomography angiography (CCTA) is increasingly recognized as an inflammation marker in coronary artery disease (CAD). Inflamed PCAT has been linked to non-calcified plaque (NCP) and cardiovascular morbidity and mortality. Nevertheless, gaps remain in understanding how PCAT behaves during plaque stabilization. Purpose We investigated associations between coronary artery inflammation assessed by PCAT and the presence and type of coronary plaques, plaque burden, and coronary calcification. Methods An observational study involving 506 patients with suspected chronic coronary syndrome who underwent CCTA. PCAT was averaged across the proximal 40 mm segments of all three coronary vessels to represent the patient level. Plaque presence and types (calcified, mixed, and soft) were assessed. Total, NCP, and calcified plaque (CP) burdens were measured as normalized atheroma volumes. Coronary calcification was quantified using Agatson coronary artery calcification (CAC) score and categorized based on cut-offs of 0, 100, 400. Results The mean PCAT was -81.1 ±6.3 Hounsfield Unit (HU) without plaque and -79.7 ±6.0 HU with plaque (p= 0.01). PCAT differed between calcified (-81.8 HU), mixed (-79.8 HU), and soft (-77.0 HU) plaques (p<0.0001). Multivariate linear analysis (adjusted for clinical risk factors, cardiovascular medication, and tube voltage) showed PCAT positively associated with total plaque burden (estimate= 1.1 [95% CI: 0.3-1.8], p=0.004) and NCP burden (estimate= 2.4 [95% CI: 1.4-3.4]; p<0.0001), while the association with CP burden was negative, but not statistically significant (estimate= -1.7 [95%CI : -4.1-0.7], p=0.2). Multivariate linear analysis, which accounted for both calcified and non-calcified plaque, reaffirmed the positive association to NCP burden (estimate= 2.7 [95% CI: 1.7-3.7]; p<0.0001) and a significant negative association between CP burden (estimate= - 3.3 [95% CI: -5.8- -1.0]; p=0.007) and PCAT. No significant differences in PCAT levels were found among patients with a CAC score of 0 and those with CAC scores exceeding 400, or across different stages of CAC score categories (p>0.8). Conclusion PCAT increased with plaque presence, rising from calcified to mixed to soft plaques. Increased NCP burden associated with higher PCAT, while higher CP burden seemed to reduce PCAT, though not statistical significant. Interestingly, when NCP was absent, higher levels of CP burden were associated with reduced inflammation. This supports the hypothesis of plaque stabilization, proposing that calcification might stabilize the vessel and indirectly contribute to reducing inflammation.Figure 1 PCAT by plaque typesTable 1 PCAT & plaque burden

Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.

Left ventricular dysfunction, pulmonary congestion, or both and cardiovascular risk after acute myocardial infarction

Abstract Background Patients with acute myocardial infarction (AMI) complicated by left ventricular systolic dysfunction (LVSD), pulmonary congestion, or both are at increased risk for adverse cardiovascular events, including long-term heart failure. The EMPACT-MI trial studied the efficacy and safety of early initiation of a sodium-glucose cotransporter-2 inhibitor (SGLT2i) in this population. We set out to evaluate the contemporary real-world risk associated with AMI presenting with LVSD and/or congestion and the projected impact of implementing early SGLT2i post-MI across the spectrum of risk. Purpose To evaluate the independent risk of presenting with LVSD, congestion, or both in patients with AMI. We will further explore the potential impact of the implementation of early initiation of SGLT2i in real-world clinical practice. Methods We conducted a retrospective population-based cohort study in Ontario, Canada, analyzing AMI hospitalizations from April 2009 to March 2022, emulating the EMPACT-MI trial cohort. Patients that survived to hospital discharge were stratified by baseline presentation with LVSD (defined as LVEF<50%), signs or symptoms of congestion or both and compared with a referent group without either risk factor (none). Exclusion criteria were previously established heart failure, prior SGLT2i use, eGFR < 20 ml/min/1.73m2 or chronic dialysis. The primary outcome was a composite of HF hospitalization or all-cause mortality at 30 days and 1 year. We used a multivariable logistic regression model to report an odds ratio (OR) and 95% confidence intervals (CI) adjusted for clinical risk factors. Results Among 36,837 AMI patients, 3% presented with congestion, 55% with LVSD, 10% with both, and 32% with none. Age/sex-adjusted 30-day event rates for the primary endpoint were 30%, 22%, 57%, and 7%, respectively. The corresponding adjusted ORs were 3.34 (95% CI: 2.05-5.46) for congestion, 3.58 (95% CI: 2.72-4.71) for LVSD, and 7.01 (95% CI: 5.09-9.65) for both, versus the reference group (none) (Figure 1). At year 1, a similar pattern of risk was observed (Figure 2). Based on the results of the EMPACT-MI trial being reported at the ACC conference in April 2024, relative and absolute risk differences in outcomes with implementation of early initiation of SGLT2i post-MI in real-world clinical practice across the spectrum of presentation will be reported. Conclusion Patients presenting with pulmonary congestion and/or LVSD remain at high risk of adverse cardiovascular events following AMI. This high-risk group represents an unmet need with a potential impact for improvement with novel interventions such as SGLT2i.

Multi-centre post-mortem study on the use of coronary inflammation in CCTA to improve the diagnosis of the coronary related cause of death with histopathology validation

Abstract Background/Introduction A limitation of post-mortem computed tomography (PMCT) is the diagnostic accuracy of coronary related death, which constitute majority of investigations. Measurements using coronary inflammation with fat attenuation index (FAI) Score, clinical risk factors and coronary plaque metrics from CCTA to predict fatal cardiac events (AI-Risk) in living patients has not been used in post-mortem setting. Purpose This study investigates two new techniques to aid the diagnosis of coronary related death in the post-mortem setting: (1) minimally invasive interventional technique to perform PMCT; (2) diagnostic accuracy of FAI-assisted PMCT with histopathology validation. Method Adults with sudden death of unknown cause <48 hours from death were included. Ultrasound guided femoral access to deliver guidewire to the aortic root (Figure 1A), followed by inflation of aortic occlusion balloon and injection of contrast for angiogram. A cardiac radiologist performed the coronary and FAI analysis (1B &C). Two experienced radiologists reported the cause of death without/with aid of FAI score results. The technique was introduced at a second PMCT centre. Results PMCT analysis of 37 participants (27 males, 10 females, age range 42-98) – 15 minimally invasive PMCT, and 22 PMCT from previous study showed FAI score (1D) and AI-Risk (1E) were significantly higher in participants who had coronary related death vs non-coronary related causes. In the receiver operating characteristic (ROC, 1F) analysis, thresholding at 42% for AI-Risk and 34 for Mean FAI score, was able to detect all 18 true positives, 2 false positives (1xpulmonary embolism, and 1xdual pathology of ruptured abdominal aneurysm with severe coronary artery disease; these cases showed high FAI score and AI-Risk), and all true negatives. Both radiologists identified 17/18 coronary related death. There were 5 cases of false positives for coronary related death in Radiologist 1 (3 years’ experience); 8 false positives in Radiologists 2 (10 years’ experience). In non-coronary related deaths, Radiologist 1 identified 14/19 true negative cases, and Radiologist 2 identified 11/19 true negative cases. In all the false positive and negative cases, the radiologists’ diagnostic confidence level was either possible, or unascertain. With the aid of FAI score results, all the true coronary related deaths were identified and no false negative case. This method was introduced at a second centre with 108 PMCTs analysed. There were 40 cases of non-coronary death, and 68 cases of coronary related death on PMCT (imaging diagnosis only). In the non-coronary group, FAI-score assisted results showed 100% concordance. In the coronary group, there was 10% dis-concordance (7/68). Conclusion Minimally invasive PMCT with FAI score-assisted diagnosis has the potential to increase the diagnostic confidence and accuracy of coronary-related cause of death.

GJA4 gene inhibition may represent a potential target for novel antithrombotic therapies

Abstract The GJA4 gene encodes a protein called connexin 37, a component of gap junctions in vascular endothelial cells, crucial in regulating endothelial function and influencing inflammation, platelet adhesion and thrombus formation. This gene deletion in mice diminished thrombus formation in vitro in human blood. In these circumstances, connexin inhibition with pharmacological agents may represent potential avenues for developing novel antithrombotic agents. Aim To investigate whether the GJA4 rs618675 T>C variant is a risk factor for progressing atherosclerosis and cardiovascular (CV) events in an asymptomatic free of apparent CAD from a Portuguese population. Methods A prospective study was performed with 1421 individuals without apparent CV disease (73.6% male, aged 52.2±8.3 years) followed during an extended period of 7.2±5.1 years). GJA4 rs618675 T>C variant was genotyped by TaqMan real-time PCR technique (Applied Biosystems). Conventional, anthropometric, biochemical and clinical risk factors were studied. Bivariate analysis and multivariate Cox regression adjusted for age, gender, traditional risk factors, biochemical markers, and the genotypes under study was performed to determine which variables were, significantly and independently, associated with CV events. Kaplan-Meier's estimate assessed the prognosis. Results Bivariate analysis showed that 50.6% of wild genotype (TT) carriers had CV events vs. 66.2% without events. Of the risk genotype (CC) carriers, 10.1% had CV events, and 3.4% did not. After the Kaplan-Meier analysis, the TT carriers had 90% CV event-free survival time and the CC risk carriers had only 64% of event-free survival. Finally, after adjustment, CC genotype remained in the equation with an HR of 3.1 (p=0.003) and CT heterozygous with HR of 1.6 (p=0.043), together with Hypertension (HR 3.0; p<0.0001), Smoking habits (HR 2.3; p=0.001) and Diabetes (HR 1.9; p=0.015). Conclusion: The CC risk genotype of the GJA4 rs618675 represented an independent risk factor for progressing atherosclerosis and CV events in a Portuguese Population. Thrombus formation often begins with the adhesion of platelets to the endothelium, and connexin 37 may influence this process. The inhibition of connexin 37 emerges as a promising candidate for future cardiovascular prevention as well as for the development of new antithrombotic drugs.

Endomyocardial biopsy in patients with myocarditis - still justified in the CMR era? A single-centre experience

Abstract Background Endomyocardial biopsy (EMB) is still considered the diagnostic gold standard for myocarditis. However, in the past decades, cardiac magnetic resonance (CMR) was established as a non-invasive tool supporting the diagnosis of myocarditis. Furthermore, there is often reluctance in performing EMB due to rare but potentially severe complications. We therefore sought to identify patient subgroups that could potentially derive benefits from EMB. This involved a comprehensive analysis of the diagnostic and therapeutic outcomes resulting from the procedure at our centre. Methods In this retrospective single-centre study, data of all patients presenting with (peri-) myocarditis between 01/2016 and 06/2023 at our tertiary care centre were included. Frequency of histological confirmation of acute myocarditis and histology leading to immunosuppressive therapy were analysed according to patient risks. Prespecified risk factors were (i) LV-EF ≤30%, (ii) presence of severe arrhythmias (VF, VT, high grade AV-block) or (iii) pre-existing autoimmune disease. Furthermore, the subgroup of recurrent myocarditis cases was analysed separately. Results A total of 150 consecutive patients (36.7 ± 15.5 years, 77.3% male) were included in this study. 26/150 patients had a documented EF ≤30%, 13/150 an EF >30% with another risk factor and 111/150 an EF>30% without another risk factor. EMB was performed in 21/26 patients with EF ≤30% (80.7%), in 7/13 patients with EF >30% and another risk factors (53.8%) and in 16/111 (14%) patients without risk factors. EMB led to the initiation of immunosuppressive therapy in 11/28 patients with risk factors (39.3%) and in none of the patients without risk factors (0/16, 0%, p=0.003)(figure 1). With respect to the subgroup of patients presenting with recurrent myocarditis (n=10), no specific therapy was performed. Conclusion Due to a high therapeutic yield for initiation of immunosuppressive therapy in non-infectious myocarditis, performing EMB should be considered in all high-risk patients. In patients without clinical risk factors including cases of recurrent or relapsing myocarditis no specific therapy was initiated.

Predicting progression from coronary artery disease to heart failure in community cohorts

Abstract Background Approximately 10% of people develop heart failure (HF) within 5 years after an acute coronary event. Once ischaemic damage has occurred, the heart undergoes remodeling, which can lead to subclinical dysfunction and HF. However, remodeling varies considerably between patients and is difficult to predict. Purpose To assess whether adding genetic information to clinical factors improves the accuracy of predicting incident non-fatal/fatal HF within 5 years in people with coronary artery disease (CAD). Methods In the UK Biobank (UKB) [1], we developed a clinical risk model (predictors age, sex, ethnicity, body mass index, social deprivation, smoking, personal medical history, time since most recent CAD admission, lipids, HbA1C, creatinine, medications). Separately, we performed a genome-wide association meta-analysis of 13,360 (6,315 + 7,045) cases and 58,126 (37,245 + 20,881) non-cases from the UKB and deCODE [2] cohorts, relating common genetic variants to non-fatal/fatal HF or cardiomyopathy in people with CAD. For the current analysis of the UKB cohort, we derived a polygenic risk score using summary statistics from deCODE alone. We then compared risk estimates and assessed reclassification of 5-year risk using the clinical risk model with and without polygenic risk score, in UKB. Risk groups were <5%, 5-9.9%, 10-14.9%, ≥15%. Results Among people with CAD in the 5 years prior to assessment in UKB (n=8,880, mean age 61y, 27% women) or deCODE (n=10,113, mean age 66y, 34% women), 377 (4%) and 915 (9%) developed non-fatal/fatal HF over 5 years follow-up, respectively. In the UKB cohort, median 5-year risk with the clinical model was 3.2% (IQR 2-5.4%, C-statistic 0.72, 95% CI 0.70–0.74, Table 1) and plots of predicted versus observed risk showed very good calibration across deciles. The polygenic risk score was predictive of non-fatal/fatal HF in the UKB (hazard ratio 1.03, 95% CI 1.00 to 1.05 per standard deviation) with significance decreasing after adjusting for the prognostic index of the clinical risk model (hazard ratio 1.02, 95% CI 0.99 to 1.05 per standard deviation). Median 5-year risk by the clinical+polygenic model was 3.0% (IQR 1.9-5.0%). Net reclassification with the addition of the polygenic score improved accuracy of risk prediction for 3.6% of non-cases but deteriorated accuracy of risk prediction for 6% of cases. Overall net reclassification was deterioration of -0.025. Conclusion Our data suggest that common genetic variants may be associated with progression from CAD to HF, but that adding genetic data did not improve the accuracy of predicting progression from CAD to HF beyond established clinical predictors in the UKB cohort. Confirmation of these findings in deCODE and other large cohorts is warranted.

Left atrial function for predicting atrial fibrillation: a machine learning approach

Abstract Background Common risk scores for atrial fibrillation (AF) are based on clinical risk factors, and little is known about the contribution of left atrial (LA) function parameters to risk assessment. Purpose To determine whether a machine learning-based model that includes LA function parameters outperforms a commonly used risk scores, the CHARGE-AF, in predicting AF. Methods Participants in the Atherosclerosis Risk in Communities (ARIC) Study (a community-based cohort study in the USA) who attended visit 5 (2011-2013) and without a history of AF were included in this analysis. Incident AF was ascertained from ECG during study visits, hospitalization discharge codes, and death certificates. Random survival forest methodology was used to build a prediction model for AF; candidate variables included traditional clinical risk factors and measures of LA structure and function. Dataset was randomly split into a training (75%) and testing (25%) cohort. For each variable, importance was measured based on the reduction in Harrell’s c-statistic if the variable was randomly permuted prior to obtaining predicted values. Variables significantly associated with AF were included in the final model. Model calibration was tested using the Greenwood-Nam-D'Agostino (GND) test. Discrimination ability of our model was assessed in the testing cohort using Harrell’s c-statistic and compared with that of the CHARGE-AF model. Results 4,909 participants (59% female, 22% Black, mean age 75 years) were included. During a median follow-up of 7.0 years (1st-3rd quartile: 5.4-7.8), 650 participants developed AF. Variables significantly associated with incident AF were race, triglycerides, systolic blood pressure, coronary heart disease, age, LA maximal volume index, NT-proBNP, and LA reservoir strain (Figure 1). LA reservoir strain was the variable most strongly associated with AF. The final model showed good discrimination (c-statistic (95% CI): 0.734 (0.698-0.768)). The GND test did not detect statistically significant miscalibration (GND p=0.08). Compared with our model, CHARGE-AF had lower discrimination ability (c-statistic (95% CI): 0.635 (0.594-0.677)) and the difference was statistically significant (difference in c-statistic (95% CI): 0.099 (0.043-0.152)). Conclusion A machine learning-based model with few variables including LA function outperforms a commonly used risk model in predicting AF. Incorporation of LA functional assessment may help inform clinicians regarding individual risk of AF.

Is pulmonary vascular remodeling an intermediate link between hyperglycemia and adverse outcomes in patients with idiopathic pulmonary arterial hypertension? Insights from a multi-center cohort study

BackgroundHyperglycemia upon admission is associated with poor prognosis of many cardiovascular diseases. However, the relationship of stress hyperglycemia ratio (SHR), admission blood glucose (ABG), and hemoglobin A1c (HbA1c) with pulmonary hypertension has not been reported. This study aimed to explore the association of hyperglycemia indices with disease severity and long-term adverse outcomes in patients with idiopathic pulmonary arterial hypertension (IPAH).MethodsThis multi-center cohort study included 625 consecutive patients diagnosed with or treated for IPAH between January 2015 and June 2023. SHR was calculated using the followings: ABG (mmol/L)/(1.59 × HbA1c [%] − 2.59). The primary endpoint was defined as clinical worsening events. Multivariable Cox regression and restricted cubic spline analyses were employed to evaluate the association of SHR, ABG, and HbA1c with endpoint events. The mediating effect of pulmonary hemodynamics was evaluated to investigate the potential mechanism between hyperglycemia and clinical outcomes.ResultsDuring a mean follow-up period of 3.8 years, 219 (35.0%) patients experienced all-cause death or clinical worsening events. Hyperglycemia indices correlated with well-validated variables that reflected the severity of IPAH, such as the World Health Organization functional class, 6-min walk distance, and N-terminal pro-brain natriuretic peptide levels. Multivariable Cox regression analyses indicated that SHR (hazard ratio [HR] 1.328, 95% confidence intervals [CI]: 1.185, 1.489 per 0.1-unit increment, P < 0.001) and ABG (HR 1.317, 95% CI: 1.134, 1.529 per 1.0-unit increment, P < 0.001) were independent predictors of primary endpoint events. Mediation analysis indicated that pulmonary vascular resistance mediated 5.65% and 14.62% of the associations between SHR and ABG and clinical worsening events, respectively. The addition of SHR significantly improved reclassification, discrimination ability, and model fit beyond the clinical risk prediction model.ConclusionsSHR is positively associated with clinical worsening in patients with IPAH. The association appeared to be partially mediated through the pathway of pulmonary vascular remodeling, indicating that SHR may serve as a valuable indicator for providing additional risk information.

Improving prehospital risk stratification using deep learning ECG interpretation in suspected acute coronary syndrome

Abstract Introduction Most patients presenting with chest pain in the Emergency Medical Services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the electrocardiogram (ECG) is challenging. Recent advances in artificial intelligence (AI) models for ECG interpretation show promising results. Purpose The aim of this study is to develop and validate a Convolutional Neural Network (CNN)-based model to diagnose NSTE-ACS within the prehospital ECG and to compare it to current prehospital available diagnostic tools. Methods For this study, a training/internal validation cohort (n= 4891) and external validation cohort (n= 754) were used, both consisting of suspected NSTE-ACS patients, and the first prehospital standard 12-lead ECG at presentation was extracted. A CNN (ECG-AI) was trained and validated to detect NSTE-ACS. The training/internal validation cohort was extracted from suspected NSTE-ACS patients presenting to the emergency department. The external validation cohort was extracted from a prehospital, prospective cohort study in which subjects were assessed by EMS paramedics. A validated prehospital clinical risk score (preHEART) was calculated, which includes manual on-site ECG analyses by EMS paramedic (ECG-EMS) and a point-of-care (POC) troponin assessment, and compared to ECG-AI in diagnosing NSTE-ACS. Results In the external validation cohort, 202 (27%) subjects were diagnosed with NSTE-ACS. ECG-AI had a better diagnostic performance than ECG-EMS (AUC 0.70 (0.66–0.74) vs. AUC 0.65 (0.61–0.70), p=0.045) for diagnosing NSTE-ACS. The overall diagnostic accuracy of preHEART was AUC 0.78 (0.74–0.82) and superior compared to ECG-AI (p= 0.001). Incorporating ECG-AI instead of ECG-EMS into preHEART led to a significant improvement in diagnostic performance (AUC 0.83 (0.79–0.86,p &amp;lt; 0.001)). Conclusion Integrating AI in prehospital ECG interpretation improves NSTE-ACS diagnosis. However, clinical risk scores still yield the best diagnostic performance in the prehospital setting and their accuracy could be further enhanced through AI.ROC prehospital diagnostic toolsROC preheart and preheart-AI

Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P &amp;lt; 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P &amp;lt; 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)