Predictor Of Major Adverse Cardiovascular Events Research Articles

The change of inflammatory markers may predict long-term major adverse cardiovascular events in elderly patients with coronary heart disease: a retrospective cohort study

BackgroundAt present, the relationship among inflammatory markers [monocytes/HDL-c (MHR), neutrophils/HDL-c (NHR) and lymphocytes/HDL-c (LHR)] and long-term prognosis of coronary heart disease (CHD) is still unclear. Therefore, this study explores the relationship between inflammatory indicators and the risk of long-term major adverse cardiovascular events (MACE) in elderly patients with CHD.MethodsA retrospective analysis was conducted on 208 elderly patients who underwent coronary angiography at Wuhan Fourth Hospital from August 2022 to August 2023. They were divided into the CHD group (N = 116) and control group (N = 92). Patients in the CHD group were followed up for 1 year and divided into the MACE group (N = 36) and the non-MACE group (N = 80) according to whether MACE occurred.ResultsIn elderly patients, logistic regression analysis shows that MHR is an independent risk factor for CHD (OR = 3.050, 95% CI 1.318–1.772). ROC curve analysis found that MHR (AUC = 0.865, 95% CI 0.811–0.919, p < 0.001) is higher than NHR and LHR. In patients with CHD, the spearman analysis show that MHR is positively correlated with Gensini score (R = 0.266, p = 0.004). The logistic regression analysis found that MHR is independent risk factors for MACE (OR = 6.048, 95% CI 1.224–1.941, p = 0.002). ROC analysis showed that the critical value of MHR to predict MACE was 0.651, the sensitivity of 58.3% and specificity of 90.0% could predict MACE, and the AUC was 0.793 (95% CI 0.702–0.884, p < 0.001) is higher than LHR.ConclusionIn elderly patients, MHR is an independent predictor of CHD and long-term MACE and is positively correlated with the severity of coronary artery lesions.

The nonlinear association between lipoprotein(a) and major adverse cardiovascular events in acute coronary syndrome patients with three-vessel disease

Lipoprotein(a) [Lp(a)] is a lipoprotein with potent atherogenic and thrombogenic potential. Its role in patients with acute coronary syndrome (ACS) combined with three-vessel disease (TVD) remains unclear. This study aimed to investigate the correlation between Lp(a) levels and the occurrence of major adverse cardiovascular events (MACE) in patients with ACS combined with TVD. Patients who underwent coronary angiography and were diagnosed with ACS combined with TVD were selected for the study. Patients were divided into three groups based on their Lp(a) levels. The correlation between Lp(a) and MACE was evaluated using univariate and multivariate Cox regression analysis, subgroup analysis, sensitivity analysis, Kaplan-Meier survival curve, receiver operating characteristic curve (ROC), and restricted cubic spline plot (RCS). A total of 1504 patients were included, with a median follow-up time of 38 months. Univariate Cox regression analysis showed that patients with higher Lp(a) levels had a significantly increased incidence of MACE (P < 0.001). After adjusting for confounding factors, multivariate Cox regression analysis indicated that high Lp(a) levels remained an independent predictor of MACE (P < 0.05). Subgroup analysis revealed that higher Lp(a) levels were significantly associated with a higher risk of MACE in subgroups including patients aged ≥ 60 years, males, those with hypertension, CKD, without diabetes, without hyperlipidemia, and without stroke (P < 0.05). Sensitivity analysis further confirmed the close correlation between Lp(a) and MACE (P < 0.05). Kaplan-Meier survival curve showed that the cumulative incidence of MACE in the high Lp(a) group was significantly higher than in the low-level group (P < 0.001). The ROC curve analysis indicated that Lp(a) had some predictive value for the occurrence of MACE (AUC: 0.623, 95% CI: 0.593–0.653, P < 0.001). The RCS plot demonstrated that after transforming Lp(a) to a normal distribution as Log10Lp(a), there was an approximately U-shaped nonlinear association between Log10Lp(a) and the risk of MACE (P nonlinear < 0.001). Lp(a) levels were significantly associated with the risk of MACE in patients with ACS combined with TVD.

A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.

Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD. Materials and Methods This retrospective study included consecutive symptomatic patients without known CAD referred for CCTA between December 2008 and January 2020. Patients with obstructive CAD (at least one ≥50% stenosis at CCTA) underwent stress cardiac MRI for functional assessment. Eighteen clinical, two electrocardiogram, nine CCTA, and 12 cardiac MRI parameters were evaluated as inputs for the ML model, which involved automated feature selection with the least absolute shrinkage and selection operator algorithm and model building with an XGBoost algorithm. The primary outcome was MACE, defined as a composite of cardiovascular death and nonfatal myocardial infarction. External testing was performed using two independent datasets. Performance was compared between the ML model and existing scores and other approaches using the area under the receiver operating characteristic curve (AUC). Results Of 2210 patients who completed cardiac MRI, 2038 (mean age, 70 years ± 12 [SD]; 1091 [53.5%] female participants) completed follow-up (median duration, 7 years [IQR, 6-9 years]); 281 experienced MACE (13.8%). The ML model exhibited a higher AUC (0.86) for MACE prediction than the European Society of Cardiology score (0.55), QRISK3 score (0.60), Framingham Risk Score (0.50), segment involvement score (0.71), CCTA data alone (0.76), or stress cardiac MRI data alone (0.83) (P value range, <.001 to .004). The ML model also exhibited good performance in the two external validation datasets (AUC, 0.84 and 0.92). Conclusion An ML model including both CCTA and stress cardiac MRI data demonstrated better performance in predicting MACE than traditional methods and existing scores in patients with newly diagnosed CAD. © RSNA, 2025 Supplemental material is available for this article.

Two-fold increased risk of cardiovascular events in people with MDR HIV: a matched cohort analysis with data from the PRESTIGIO registry.

Major adverse cardiovascular events (MACEs) may contribute to the high morbidity in people with four-class drug-resistant HIV (4DR-PWH). To explore the probability of MACEs in 4DR-PWH compared with non-4DR controls. This was a retrospective, propensity score-matched cohort study on 4DR-PWH (cases) and non-4DR-PWH (controls), on ART, without previous MACEs. Controls were matched with cases in a 4:1 ratio for age, sex-assigned-at-birth and ART duration. Incidence rates (IRs) and incidence rate ratio (IRR) of MACEs with 95% CIs were modelled by Poisson regression. Cumulative probabilities of the first incident MACE were estimated by Kaplan-Meier curves. A multivariable stepwise Cox proportional hazards model estimated predictors of incident MACEs among covariates with univariable P < 0.100. Overall, 223 4DR-PWH and 797 non-4DR-PWH were evaluated. During a median (IQR) follow-up of 8.2 (5.4-11.1) years [1833 person-years of follow-up (PY)], 23/223 (10.3%) 4DR-PWH developed 29 MACEs, IR = 1.6 (95% CI = 1.1-2.3)/100 PY. During a median follow-up of 8.4 (5.2-11.0) years (6450 PY), 42/797 (5.3%) non-4DR controls had 45 MACEs, IR = 0.7 (95% CI = 0.5-0.9)/100 PY, IRR (4DR/non-4DR) = 2.3 (95% CI = 1.4-3.6). The cumulative probabilities of the first MACE were more than doubled in 4DR-PWH (P = 0.006). At multivariable analysis, an increased risk of MACEs was associated with 4DR status [adjusted hazard ratio (aHR) = 1.9; 95% CI = 1.0-3.4], after adjusting for age, sex-assigned-at-birth, HIV load, CD4+ nadir, total cholesterol, HDL cholesterol, diabetes mellitus, statin use and baseline HCV serostatus. In PWH, MDR is significantly associated with a higher risk of cardiovascular events. Prompt implementation of prevention strategies is mandatory in this fragile population.

Prognostic implications of increased and final quantitative flow ratios in patients treated with drug-coated balloons physiological evaluation after DCB in de novo lesions

BackgroundFew studies investigated the implications of post-PCI QFR and post-PCI ΔQFR (absolute increase of QFR) in de novo lesions of small coronary disease after drug-coated balloon (DCB).ObjectivesWe sought to investigate the prognostic implications of post-PCI QFR and post-PCI ΔQFR in patients who received DCB only.MethodsPatients were divided according to the optimal cutoff value of the post-PCI QFR and the post-PCI ΔQFR. The primary outcome was major adverse cardiovascular events (MACE), including target vessel revascularization (TVR), cardiac death, and myocardial infarction (MI).ResultsThe optimal cutoff values of QFR and ΔQFR for the MACE rate were 0.86 and 0.57, respectively. There were 175 patients (61.2%) with a high QFR (≥ 0.86) and 113 patients (39.5%) with a high ΔQFR (≥ 0.57) after PCI. The MACE rate was significantly higher in patients with a low QFR compared to a high QFR (5.7% vs. 27.0%, hazard ratio [HR]: 3.632, 95% confidence interval [CI]: 1.872 to 7.044, P < 0.001). The MACE rate was higher in patients with a low ΔQFR increase compared to those with high ΔQFR (4.4% vs. 20.2%, HR: 4.700, 95%CI: 2.430 to 9.089, P = 0.001). In multivariable model, a low post-PCI QFR and a low post-PCI ΔQFR was independent predictor of MACE (adjusted HR: 4.071, 95%CI: 2.037 to 8.135, P = 0.001).ConclusionsAfter DCB in de novo lesions of small coronary disease, both post-PCI QFR and ΔQFR showed similar prognostic implications in MACE.

Predictive value of NT-proBNP for major adverse cardiovascular events within a 6-month period in patients with acute coronary syndrome.

The role of NT-proBNP as a cardiac biomarker for predicting short-term major adverse cardiovascular events (MACEs) in acute coronary syndrome (ACS) remains unclear. This study investigated the utility of the NT-proBNP level for predicting MACEs within a 6-month period in patients with ACS. This prospective study included 241 consecutively enrolled adults with ACS between September 2023 and February 2024. Demographic data, clinical characteristics, GRACE score, and high-sensitivity cardiac troponin T (hs-cTnT) and NT-proBNP levels were compared between patients who were MACE-positive vs. MACE-negative within a 6-month period. The overall mortality rate was 8.7%, and the incidence of MACEs was 43.2%. The mean serum levels of hs-cTnT and NT-proBNP were significantly higher in the MACE-positive than in the MACE-negative group. Age, concomitant coronary artery disease, NT-proBNP, and GRACE score were independent predictors of MACEs in patients with ACS. An NT-proBNP level of 250pg/mL had a sensitivity of 73.1% and a specificity of 88.3% for predicting MACEs, with an area under the curve of 0.847. The estimated risk of MACEs was 70% and 90% for NT-proBNP values of 600pg/mL and 900pg/mL, respectively. The NT-proBNP level measured at ED admission was strongly associated with short-term MACEs in patients with all ACS subtypes and was an important prognostic biomarker. Therefore, combining the NT-proBNP level with the GRACE score in ACS patients may provide significant benefits in terms of predicting MACEs and obtaining a more accurate risk stratification.

Body composition of metabolically unhealthy normal-weight patients with aortic stenosis: a prospective cohort study

BackgroundThe aim of this study was to evaluate the prognostic impact of computed tomography (CT)-based body composition parameters in metabolically unhealthy normal-weight patients (MUHNW) with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR).MethodsThis prospective cohort study involved adults with normal weight scheduled for TAVR. Patients were divided into two groups: metabolically healthy normal-weight (MHNW) and MUHNW. The primary outcome was major adverse cardiovascular events (MACEs), and the secondary outcomes were all-cause mortality and prolonged hospital stay. Logistic regression was used to explore the relationships between variables and outcomes, and Cox regression models were applied to examine the effects of different parameters on patient prognosis. Incremental discriminative improvement (IDI) and the C-index were used to determine the influence of hybrid parameters on the predictive efficiency of the model.ResultsThe cohort study included 182 patients divided into two groups: MHNW metabolically healthy normal-weight (n = 97) and MUHNW (n = 85). Over a median follow-up of 0.8 years, multivariable logistic regression analysis revealed significant associations of the skeletal muscle index (SMind) (HR: 0.50, 95% CI: 0.29 – 0.84, p = 0.01), subcutaneous adipose tissue index (SATind) (HR: 0.63, 95% CI: 0.45 – 0.88, p = 0.01), and visceral adipose tissue index (VATind) (HR: 1.34, 95% CI: 1.10 – 1.63, p < 0.01) with the risk of experiencing MACEs, whereas epicardial adipose tissue (EAT) was not significantly associated with the risk of experiencing MACEs in the multivariable model (HR: 1.10, 95% CI: 0.97 – 1.24, p = 0.14). For the primary outcome, adjusted for significant covariates, the model had an IDI of 0.25 and a C-index of 0.8, with significant associations of SMind (HR: 0.73, 95% CI: 0.62 – 0.87, p < 0.01), SATind (HR: 0.87, 95% CI: 0.78 – 0.97, p = 0.01), and VATind (HR: 1.25, 95% CI: 1.12–1.40, p < 0.01) with the risk of experiencing MACEs. For the secondary outcome, the model had an IDI of 0.36 and a C-index of 0.93, with EAT showing a significant protective effect against all-cause mortality and prolonged hospital stay (HR: 0.97, 95% CI: 0.93 – 0.99; p = 0.04).ConclusionsBody composition parameters, including, VATind, SMind, and SATind, are significant predictors of MACEs in patients undergoing TAVR. Additionally, EAT shows a significant protective effect against all-cause mortality and prolonged hospital stay. These findings highlight the potential importance of comprehensive body composition assessments in the risk stratification and management of AS patients.Graphical

Development of a predictive model for postoperative major adverse cardiovascular events in elderly patients undergoing major abdominal surgery

ObjectiveTo investigate the predictive value of a Short Physical Performance Battery (SPPB) for postoperative major adverse cardiovascular events(MACEs) in elderly patients undergoing major abdominal surgery and to develop a nomogram risk prediction model.MethodsA total of 427 elderly patients aged ≥ 65 years who underwent major abdominal surgery at our hospital between June 2023 and March 2024 were selected for the study, and 416 patients were ultimately included. The preoperative SPPB score was measured, and the patients were divided into two groups: a high SPPB group (≥ 10) and a low SPPB group (< 10). The subjects’ clinical datasets and postoperative major adverse cardiovascular event (MACEs) occurrence data were recorded. LASSO regression analysis was performed to screen predictor variables and develop a nomogram risk prediction model for predicting MACEs. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the model’s clinical efficacy.ResultsThe incidence of postoperative MACEs in elderly patients who underwent major abdominal surgery was 5%. LASSO regression analysis revealed that arrhythmia, creatine kinase, SPPB, anesthesia duration, age, intraoperative minimum heart rate, BMI, and coronary artery disease were significant predictors of MACEs. The nomogram risk prediction model based on SPPB and clinical indicators can better predict the occurrence of MACEs and can guide preoperative interventions and help to improve perioperative management.The decision curve indicated encouraging clinical effectiveness, the calibration curve demonstrated good agreement, and the area under the curve (AUC) was 0.852 (95% CI, 0.749–0.954).ConclusionThe nomogram risk prediction model based on SPPB and clinical indicators can better predict the occurrence of MACEs and can guide preoperative intervention and help to improve perioperative management.

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and perioperative management plans, but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study developed and validated a machine learning (ML) model using accessible preoperative clinical data to predict perioperative MACEs in stable coronary artery disease (SCAD) patients undergoing noncardiac surgery (NCS). We collected data from 9171 adult SCAD patients who underwent NCS and extracted 64 preoperative variables. First, the optimal data imputation, resampling, and feature selection methods were compared and selected to deal with missing data values and imbalances. Then, nine independent machine learning models (logistic regression (LR), support vector machine, Gaussian Naive Bayes (GNB), random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine, categorical boosting (CatBoost), and deep neural network) and a stacking ensemble model were constructed and compared with the validated Revised Cardiac Risk Index's (RCRI) model for predictive performance, which was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), calibration curve, and decision curve analysis (DCA). To reduce overfitting and enhance robustness, we performed hyperparameter tuning and 5-fold cross-validation. Finally, the Shapley additive interpretation (SHAP) method and a partial dependence plot (PDP) were used to determine the optimal ML model. Of the 9,171 patients, 514 (5.6 %) developed MACEs. 24 significant preoperative features were selected for model development and evaluation. All ML models performed well, with AUROC above 0.88 and AUPRC above 0.39, outperforming the AUROC (0.716) and AUPRC (0.185) of RCRI (P < 0.001). The best independent model was XGBoost (AUROC = 0.898, AUPRC = 0.479). The calibration curve accurately predicted the risk of MACEs (Brier score = 0.040), and the DCA results showed that XGBoost had a high net benefit for predicting MACEs. The top-ranked stacking ensemble model, consisting of CatBoost, GBDT, GNB, and LR, proved to be the best (AUROC 0.894, AUPRC 0.485). We identified the top 20 most important features using the mean absolute SHAP values and depicted their effects on model predictions using PDP. This study combined missing-value imputation, feature screening, unbalanced data processing, and advanced machine learning methods to successfully develop and verify the first ML-based perioperative MACEs prediction model for patients with SCAD, which is more accurate than RCRI and enables effective identification of high-risk patients and implementation of targeted interventions to reduce the incidence of MACEs.

Wide variation in shape of hypoplastic left ventricles undergoing recruitment and biventricular repair: A statistical shape modeling study.

Patients with hypoplastic left ventricles (LV) who undergo volume-loading procedures (recruitment, biventricular [BIV] repair) are at risk for adverse outcomes, including heart failure and death. We investigated pre-BIV LV shape as a predictor of outcome after BIV repair in patients with hypoplastic LVs. Baseline and post-recruitment cardiac magnetic resonance imaging and computed tomography data were analyzed in patients with hypoplastic LV (<50mL/m2). Statistical shape modeling (SSM) was utilized to generate a model of the shape and variability of LVs. Traditional measures of LV sphericity and eccentricity were also measured. Major adverse cardiovascular events (MACE) included heart failure, transplant, and death. Of 95 patients with baseline mean LV volume 29±13mL/m2, 45/95 (47%) had a right dominant atrioventricular canal defect, 31/95 (33%) had a variant of hypoplastic left heart syndrome, and 18/95 (19%) had endocardial fibroelastosis (EFE). A wide variation in LV shape was found by SSM, and shape modes were associated with right ventricle (RV) and LV size, and diagnosis. BIV repair was achieved in 74/95 (78%) patients; 13/74 (18%) of BIV patients had MACE. Predictors of MACE following BIV repair included EFE, higher RV mass index, and higher RV end-diastolic volume index. No baseline or post-recruitment LV shape parameter was associated with the outcome after BIV repair. The shape model of hypoplastic LVs demonstrated a wide array of LV shapes. LVs gained sphericity and size and lost eccentricity with recruitment. Though the ventricles changed shape with recruitment, no specific LV shape characteristic at the baseline or post-recruitment stage was predictive of decision to proceed with BIV repair or outcome. Higher RV mass and volume may represent new biomarkers that predict outcomes following BIV repair in patients with hypoplastic LV. Further investigation could determine the reproducibility of these findings.

Prognostic Value of Coronary CT Angiography-Derived Quantitative Flow Ratio in Suspected Coronary Artery Disease.

Background The prognostic value of coronary CT angiography (CTA)-derived quantitative flow ratio (CT-QFR) remains unknown. Purpose To determine the prognostic value of CT-QFR in predicting the long-term outcomes of patients with suspected coronary artery disease (CAD) in comparison with invasive coronary angiography (ICA)/SPECT and to determine the influence of prior percutaneous coronary intervention (PCI) on the prognostic value of CT-QFR. Materials and Methods In this secondary analysis of the prospective international CORE320 study, 379 participants who underwent coronary CTA and SPECT within 60 days before ICA between November 2009 and July 2011 were included for follow-up. The coronary CTA images were analyzed to determine CT-QFR. The primary outcome was major adverse cardiovascular events (MACEs) in the 5-year follow-up. Kaplan-Meier curves, multivariable Cox regression models adjusted for clinical variables, and areas under the receiver operating characteristic curves (AUCs) were used to assess and compare the predictive ability of CT-QFR and ICA/SPECT. Results CT-QFR computation and 5-year follow-up data were available for 310 participants (median age, 62 years), of whom 205 (66%) were male. CT-QFR (hazard ratio, 1.9 [95% CI: 1.0, 3.5]; P = .04) and prior myocardial infarction (hazard ratio, 2.5 [95% CI: 1.5, 4.0]; P < .001) were independent predictors of MACE occurrence in the 5-year follow-up. MACE-free survival rates were similar in participants with normal CT-QFR and ICA/SPECT (82% vs 80%; P = .45) and in participants with abnormal CT-QFR and ICA/SPECT findings (60% vs 57%; P = .40). In participants with prior PCI, CT-QFR had a lower AUC in predicting MACEs than in participants without prior PCI (0.44 vs 0.70; P < .001). Conclusion CT-QFR was an independent predictor of MACEs in the 5-year follow-up in participants with suspected CAD and showed similar 5-year prognostic value to ICA/SPECT; however, prior PCI affected CT-QFR ability to predict MACEs. Clinical trial registration no. NCT00934037 © RSNA, 2024 Supplemental material is available for this article.

Influence of sodium-glucose cotransporter 2 inhibitors on the triglyceride-glucose index in acute myocardial infarction patients with type 2 diabetes mellitus.

As a novel oral anti-hyperglycemic agent, sodium-glucose cotransporter 2 inhibitors (SGLT2-i) have been demonstrated to improve cardiovascular outcomes in acute myocardial infarction (AMI) patients with type 2 diabetes mellitus (T2DM). However, the mechanism responsible for the beneficial effects remains unclear. Recently, extensive studies have demonstrated a close relationship between elevated fasting triglyceride-glucose (TyG) index and the risk of AMI. Additionally, research has identified that SGLT2-i can reduce the TyG index in T2DM patients. However, it remains ambiguous whether the benefit of SGLT2-i in patients with AMI and T2DM is due to an improvement in the TyG index. Consequently, we aimed to assess the impact of SGLT2-i on the TyG index in AMI patients with T2DM. A retrospective and cross-sectional study was conducted on 180 AMI patients with T2DM admitted to the chest pain center of the Second Affiliated Hospital of Anhui Medical University from January 2020 to January 2023. Based on the hypoglycemic regimens administered after admission, the patients were categorized into a control group (79 cases treated with sulfonylureas, α-glycosidase inhibitors, metformin, etc.) and a SGLT2-i group (101 cases administered with dapagliflozin or empagliflozin). Propensity score matching (PSM) was adopted to balance the baseline characteristics of patients and minimize selection bias to confirm the robustness of the results. After PSM, control group remained 32 patients, and SGLT2-i group remained 37 patients. All patients underwent regular follow-up after discharge, and comparisons were made between the two groups in terms of clinical indicators and major adverse cardiovascular events (MACEs) in 1 year. Univariate and Multivariate Cox regression analysis was performed to identify the predictors of MACE. Significant differences were observed between the two groups in terms of various parameters before PSM, included age, proportion of insulin use, Gensini score, serum creatinine (Cr), total cholesterol (TC), and cardiac troponin I (cTnI). After PSM, there were no statistically significant differences in baseline clinical indicators and laboratory tests. The median follow-up period was 11 months in both cohorts. The comparison of follow-up results between the two groups after matching confirmed statistically significant differences in triglyceride (TG) reduction index reduction, left ventricular end-diastolic diameter (LVDD) reduction, and white blood cell (WBC) reduction in the SGLT2-i group (all P<0.05). Additionally, a higher incidence of MACEs was observed in the control group (P=0.01). Univariate analysis showed that usage of SGLT2-i, Cr, low-density lipoprotein cholesterol (LDL-C), TyG index at baseline, and changes of TyG index (TyG at follow-up minus TyG at baseline) were associated with the risk of MACE. However, multivariate analysis showed only usage of SGLT2-i was associated with the risk of MACE [hazard ratio (HR) =0.077; 95% confidence interval (CI): 0.009-0.682; P=0.02]. In AMI patients with T2DM, the use of SGLT2-i was associated with a lower risk of MACE and an improvement of TyG index during 11 months follow-up. Our findings offer new insights into the cardio-protective mechanisms of SGLT2-i in the context of AMI.

Acute kidney injury as a key predictor of cardiovascular events in chronic kidney disease patients: the CKD-REIN study.

Cardiovascular diseases are a leading cause of morbidity and mortality in patients with chronic kidney disease (CKD). Acute kidney injury (AKI) has been increasingly recognized as a potential exacerbating factor for cardiovascular events in these patients. The CKD-REIN study aims to explore the relationship between AKI and the risk of major adverse cardiovascular events (MACE) in a cohort of CKD patients. We hypothesize that AKI is a significant and independent predictor of MACE in patients with CKD, and that the severity of AKI correlates with the risk of subsequent cardiovascular events. This prospective cohort study included 3033 adult CKD patients from 40 outpatient nephrology clinics in France. Patients were followed for a median of 5.2 years. AKI episodes were identified and staged based on the KDIGO-AKI criteria. Cardiovascular events, including myocardial infarction, stroke, heart failure hospitalization, and cardiovascular death, were systematically recorded. The association between AKI and MACE was analyzed using a multivariable Cox model, adjusting for confounders such as demographic characteristics, medical history, and baseline kidney function. During the follow-up, 530 patients experienced at least one episode of AKI. The cumulative incidence of MACE at 1 year post-AKI was 8.1%. Patients with AKI had a significantly increased risk of MACE, with an adjusted hazard ratio (HR) of 5.78 (P<.001). The risk was consistent across different MACE components and was independent of age, sex, CKD stage, or comorbidities. The risk of MACE was higher for more severe AKI stages and for AKI events requiring hospitalization or associated with incomplete renal recovery. The findings of this study confirm that AKI is a significant independent predictor of MACE in CKD patients, demonstrating a strong severity-response relationship. These results underscore the importance of vigilant cardiovascular monitoring and preventive strategies in CKD patients following AKI episodes. Understanding the mechanisms linking AKI to cardiovascular outcomes is crucial for developing targeted interventions to mitigate these risks.

Investigation of the impact of the deep learning based CT fractional flow reserve on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease

Objective: To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease. Methods: In this single-center retrospective cohort study, consecutive patients with obstructive coronary heart disease (with at least one stenosis≥50%) on their first coronary computed tomography angiography (CCTA) in Beijing Anzhen Hospital from February 2017 to July 2018 were included. Baseline clinical and CT characteristics were collected. Deep-learning-based CT-FFR and Leiden CCTA risk score were calculated. All patients enrolled were followed up for at least 5 years. The study endpoint was major adverse cardiovascular events (MACE), defined as the composite of cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and unplanned revascularization. Receiver operating characteristic (ROC) curves were drawn to define the optimal cut-off point of the Leiden score in predicting the 5-year MACE, and survival analysis and Cox regression were performed to explore the related factors of MACE. Results: A total of 622 patients, aged 61 (54, 66) years, with 407 (65.4%) males were included. Diagnostic coronary angiography was performed in 78 patients after their baseline CCTA, with 34 (43.6%) patients had CT-FFR>0.80. During a follow-up time of 2 181 (2 093, 2 355) days, 155 patients (24.9%) suffered from MACE. ROC derived optimal cut-off point of Leiden score for predicting MACE was 15.48. Survival analysis found that male patients, Leiden risk score>15 and CT-FFR≤0.80 had worse prognosis. Multivariate Cox regression analysis identified CT-FFR≤0.80 as an robust and independent predictor of MACE (HR=4.98, 95%CI 3.15-7.86, P<0.001). Conclusion: Deep-learning-based CT-FFR aids in clinical decision-making and the evaluation of long-term prognosis in patients with obstructive coronary heart disease.

Prognostic value of extracellular volume fraction in myocardial infarction and myocardial infarction with nonobstructive coronary arteries: A multicenter study

The aim of the present retrospective study was to evaluate the prognostic role of the extracellular volume fraction (ECV) in patients with myocardial infarction (MI) and myocardial infarction with nonobstructive coronary arteries (MINOCA). The present study hypothesized that ECV is associated with major adverse cardiovascular events (MACEs) in MI and MINOCA patients. Cardiac magnetic resonance (CMR) imaging was performed on 351 consecutive patients (mean age: 58±12 years; 252 [71.8%] males) who were diagnosed with MI between October 2015 and November 2023. From CMR imaging, the extent of late gadolinium enhancement (LGE), native T1 and ECV were derived. Patients were categorized into groups according to the degree of coronary artery stenosis, namely, patients with MINOCA and patients with obstructive MI. Follow-up was performed to assess MACEs. The final cohort consisted of 61 MINOCA patients and 290 obstructive MI patients. During a mean follow-up of 27±16 months, there was no statistically significant difference in the incidence of MACEs between patients with MINOCA and those with obstructive MI, and the two groups of patients had similar ECVs (32.2±3.6 vs. 32.3±6.0, p=0.864). According to the multivariate Cox regression, ECV was an independent predictor of MACEs (HR: 1.13; p<0.001) and significantly improved the prognostic value of the baseline multivariate models (C-statistic improvement: 0.816-0.864, p=0.001). Similarly, ECV maintained an independent association with MACEs in the MINOCA (HR: 1.35; p<0.001) and obstructive MI (HR: 1.13; p<0.001) groups. In MI and MINOCA patients, ECV is an independent predictor of MACEs. MINOCA is not a benign disease, and its long-term prognosis is as poor as that of patients with obstructive MI.

Aortic Pulse Wave Velocity Determined with Oscillometric Pulse Wave Analysis Algorithm Antares Is an Independent Predictor of Major Adverse Cardiovascular Events: A Prospective Cohort Study.

Background/Objectives: Aortic pulse wave velocity (aPWV) is a well-established surrogate marker of arterial stiffness. The Antares algorithm offers a method for determining aPWV from oscillometric blood pressure waveforms without requiring additional inputs. This prospective study aimed to evaluate the association and prognostic value of aPWV, determined by Antares, in predicting major adverse cardiovascular events (MACE). Methods: In total, 240 patients (median age 69, 25.4% female) underwent oscillometric blood pressure measurements, from which aPWV was calculated using the Antares algorithm. MACE, comprising myocardial infarction, stroke, or all-cause mortality, occurred in 19.2% of patients during a median follow-up of 43 months. Survival analyses were performed using continuous aPWV values, a 10 m/s threshold, and aPWV quartiles. Kaplan-Meier curves and log-rank tests were used to compare survival across aPWV groups. Cox proportional hazards models were applied to assess the independent predictive value of aPWV. Results: Patients with aPWV < 10 m/s showed significantly higher event-free survival compared to those with aPWV ≥ 10 m/s (log-rank p = 0.044). Quartile analysis reinforced this, with the highest event rate in the highest aPWV quartile (log-rank p < 0.01). Multivariable analysis confirmed aPWV as an independent predictor of MACE (HR per 1 m/s: 1.24, 95% CI: 1.08-1.41; HR per 1 SD: 1.53, 95% CI: 1.17-2.00, p = 0.002). Adding aPWV to a risk model improved predictive accuracy (C-index 0.68 to 0.71). Conclusions: In the investigated cohort, aPWV derived using the Antares algorithm is an independent predictor of cardiovascular events. This non-invasive approach is promising for improving simple outpatient risk stratification and targeting preventive measures.

Abstract 4119992: 12-lead electrocardiograms predict adverse cardiovascular outcomes of emergency department patients

Introduction: Chest pain is a common cause for presentation to the emergency department (ED). Identifying patients at increased risk of adverse cardiovascular outcomes may help guide testing. Aims: We sought to determine if deep learning enabled 12-lead electrocardiogram (ECG) analysis may improve prediction of adverse cardiovascular outcomes of ED patients presenting with chest pain compared to conventional approaches. Methods: A pretrained convolutional neural network was applied to derive numerical representations of the index ECG waveforms from patients presenting to the Brigham and Women’s Hospital (BWH) ED with chest pain. We trained a neural network model (‘CP-AI’) to predict 30-day major adverse cardiovascular events (MACE) defined as myocardial infarction (MI), pulmonary embolism (PE), aortic dissection (AD), or mortality using age, sex, cardiac biomarkers (troponin and d-dimer), and the numerical ECG representations as inputs ( Figure 1 ). In a holdout sample of Massachusetts General Hospital (MGH) ED patients, we compared the performance of the CP-AI model to alternative models fit on (1) demographics (‘Age and Sex’) (2) demographics + cardiac biomarkers (‘Biomarker Model’), and (3) demographics + numerical ECG representations (‘ECG Model’). Results: The BWH derivation sample included 18,811 individuals (51% female, mean age 65 ± 16 years). The MGH validation sample included 14,476 individuals (45% female, mean age 65 ± 15 years). The CP-AI model significantly outperformed the comparison models for prediction of 30-day MACE with an area under the receiver operating characteristic curve (AUROC) of 0.82 [95% CI 0.81-0.83] ( Figure 1 ). CP-AI also significantly outperformed the comparison models for classification of the 30-day MACE components including MI (AUROC 0.91 [95% CI 0.90-0.92]), PE (AUROC 0.74 [95% CI 0.72-0.76]), and mortality (AUROC 0.86 [95% CI 0.85-0.87]), but not aortic dissection (0.71 [95% CI 0.68-0.75]). Conclusions: The integration of deep learning ECG analysis with conventional assessment improved prediction of adverse cardiovascular of ED patients presenting with chest pain.

Abstract 4117834: Utility of Abdominal Aortic Calcification Assessment for the Prediction of Major Adverse Cardiovascular Events in Liver Transplant Patients

Background: Cardiovascular disease is a leading cause of postoperative morbidity and mortality following liver transplantation (LT). Presence of abdominal aortic calcification (AAC) has been linked to cardiovascular events in the general population. We investigated whether AAC on pre-transplant computed tomography (CT) predicts major adverse cardiovascular events (MACE) post-LT. Methods: Consecutive LT patients between 2010-2018 from the Victorian Liver Transplant Unit (Australia) were included. Extent of AAC was quantified in a blinded fashion, with high AAC defined as a calcium score of ≥500. MACE was defined as any recorded episodes of acute coronary syndrome (ACS), ventricular arrhythmia, decompensated heart failure, or stroke. The primary outcome was post-LT MACE at 30 days and the secondary outcome was all-cause mortality at maximal follow-up. Results: Of 461 patients undergoing LT, 350 had suitable CTs for analysis, of which 90 (25.7%) had also undergone CT coronary angiography. High AAC was identified in 98 patients (28.0%). This finding demonstrated a moderate correlation with high coronary artery calcium score (CACS) ≥400 (r=0.52, p&lt;0.001). Fifty-eight MACE occurred in 42 patients (12.0%) within 30-days. High AAC was associated with increased risk of 30-day MACE (OR=2.68 (95%CI 1.39-5.17), p=0.004), and long-term mortality (HR=2.52 (95%CI 1.29-4.92), p=0.007) at a median follow-up of 4.0 years. In addition, high AAC predicted an increased risk of ACS (OR 3.80 (95%CI 1.18-12.27), p=0.025), heart failure (OR 3.07 (95%CI 1.21-7.80), p=0.020) and atrial fibrillation (OR 2.04 (95%CI 1.02-4.07), p=0.048), with a trend to increased prevalence of ventricular arrhythmia (OR 2.67 (95%CI 0.84-8.50), p=0.10). Following multivariate analysis, high AAC remained a strong independent predictor of MACE (OR=3.49, 95%CI 1.21-10.06, p=0.021). Addition of AAC to the revised cardiac risk index (RCRI) improved model fit for predicting MACE (net reclassification improvement of 52.7% (p&lt;0.001)). Conclusions: This study demonstrates for the first time that high AAC on routine abdominal CT scans is associated with a 3-fold increased risk of 30-day MACE post-LT, and improves cardiovascular risk prediction compared to traditional risk scores. Quantification of AAC may offer a simple method of improving cardiovascular risk assessment and implementation of preventative strategies in liver transplant patients, using pre-existing scans without additional cost or patient risk.

Abstract 4125093: Association of Systemic Inflammatory Index and Systemic Inflammatory Response Index with Major Adverse Cardiovascular Events in Patients with Acute Coronary Syndrome Who Had Primary Percutaneous Coronary Intervention

Introduction: Inflammation plays an important role in the pathogenesis of coronary artery disease and Acute Coronary Syndrome (ACS). Inflammatory indicators such as neutrophil count and monocyte count potentially may predict patients’ outcomes and prognosis in ACS. White blood cells count is an affordable and accessible way to assess the systemic immune response. Considering their multidirectional effect on atherosclerosis, new inflammatory biomarkers integrating various leukocyte subgroups have been proposed to calculate the systemic inflammatory response index (SIRI) and systemic inflammatory index (SII). Aim: The aim of this study was to assess the association between the systemic inflammatory response index (SIRI) and Systemic inflammatory index (SII) at admission with major adverse cardiovascular events (MACE) 30-days after Primary Percutaneous Coronary Intervention (PPCI) among patients with Acute Coronary Syndrome (ACS) at one referral hospital in Jordan. Method: A Prospective design was used to assess and follow 150 adults presented with ACS and received PPCI at one referral hospital in Jordan. Data was collected from patients and their medical records during hospital stay using a structured data collection form. The MACE was assessed 30-days after PPCI through phone calls with patients and confirmed from medical records. Results: The study included 150 patients with ACS, 82.7% (n= 124) of them were males. The mean age of patients was 57.68 (SD= 11.19) years. Types of ACS include stable angina 5.3% (n=8), unstable angina 24% (n=36), NSTEMI 28.7% (n=43), and STEMI 24% (n=36). All patients had interventional PCI with balloon and stent insertion. The mean of SIRI was 24.45 (SD=25.49) and the mean of SII was 663.30 (SD= 403.75). Rate of MACE as composite outcome 30-days after PPCI as 24.7% (n=37). The most MACE was unplanned repeat revascularization (n=14, 38% of MACE). Significant association between SIRI and SII, and MACE was found SIRI:(Χ 2 (2)&gt; = 12.63, p = 0.001), SII:(Χ 2 (2)&gt; = 4.23, p = 0.03) . Conclusion: The SIRI and SII are inflammatory biomarkers that should be studied as potentially predictors of MACE among patients presented with ACS.

Biomarker-enhanced cardiovascular risk prediction in patients with cancer: a prospective cohort study.

Continuously improving cancer-specific survival puts a growing proportion of cancer patients at risk of major adverse cardiovascular events (MACE), but tailored tools for cardiovascular risk prediction remain unavailable. To assess a broad panel of cardiovascular biomarkers and risk factors for the prediction of MACE and cardiovascular death in cancer patients. In total, 2192 patients with newly diagnosed or recurrent cancer were followed prospectively for the occurrence of 2-year MACE and 5-year cardiovascular death. Univariable and multivariable risk models were fit to assess independent associations of cardiovascular risk factors and biomarkers with adverse outcomes, and a risk score was developed. Traditional cardiovascular risk factors and selected cancer types were linked to higher MACE risk. While levels of Lp(a), CRP, and GDF-15 did not associate with MACE, levels of ICAM-1, P-/E-/L-selectins, and NT-proBNP were independently linked to 2-year MACE risk. A clinical risk score was derived, assigning+1 point for male sex, smoking, and age of ≥60 years and+2 points for atherosclerotic disease, yielding a bootstrapped C-statistic of 0.76 (95% CI: 0.71-0.81) for the prediction of 2-year MACE. Implementation of biomarker data conferred improved performance (0.83, 95% CI: 0.78-0.88), with a simplified model showing similar performance (0.80, 95% CI: 0.74-0.86). The biomarker-enhanced and simplified prediction models achieved a C-statistic of 0.82 (95% CI: 0.71-0.93) and 0.74 (95% CI: 0.64-0.83) for the prediction of 5-year cardiovascular death. Biomarker-enhanced risk prediction strategies allow the identification of cancer patients at high risk of MACE and cardiovascular death. While external validation studies are ongoing, this first-of-its-kind risk score may provide the basis for personalized cardiovascular risk assessment across cancer entities.