Framingham risk score is a useful indicator of posttransplant cardiovascular events and survival among Korean kidney transplant recipients: a nationwide, prospective cohort study.

Background: Cardiovascular disease is an important risk factor for mortality among kidney transplant recipients. In this study, we aimed to investigate the association between cardiovascular risk score at kidney transplantation and long-term outcomes of patients. Methods: In this prospective, observational cohort study, we enrolled kidney transplant recipients who participated in the Korean Organ Transplantation Registry and underwent transplantation between April 2014 and December 2019. The cardiovascular risk status of kidney transplant recipients was assessed using the Framingham risk score. All-cause mortality, major adverse cardiovascular events, allograft failure, estimated glomerular filtration rates (eGFRs), and composite outcomes were evaluated after kidney transplantation. Results: Of the 4,682 kidney transplant recipients, 96 died during 30.7 ± 19.1 months of follow-up. The Kaplan-Meier survival analysis results showed that high Framingham risk scores were associated with all-cause mortality, major adverse cardiovascular events, and composite outcomes. According to the multivariable Cox analysis, high Framingham risk scores were associated with an increased risk of mortality (hazard ratio [HR], 3.20; 95% confidence interval [CI], 1.30-7.91), major adverse cardiovascular events (HR, 8.43; 95% CI, 2.41-29.52), and composite outcomes (HR, 2.05; 95% CI, 1.19-3.46). The eGFRs after transplantation were significantly higher among patients in the low Framingham risk score group (p < 0.001). However, Framingham risk scores were not associated with graft loss or rapid decline in eGFRs. Conclusion: The Framingham risk score is a useful indicator of cardiovascular events, mortality, and kidney function after kidney transplantation.

Artificial Intelligence Prediction of Cardiovascular Events Using Opportunistic Epicardial Adipose Tissue Assessments From Computed Tomography Calcium Score.

Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (eg, volume and mean Hounsfield unit [HU]) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACEs). Objectives: The purpose of this study was to create novel, hand-crafted EAT features, "fat-omics," to capture the pathophysiology of EAT and improve MACE prediction. Methods: We studied a cohort of 400 patients with low-dose cardiac computed tomography calcium score examinations. We purposefully used a MACE-enriched cohort (56% event rate) for feature engineering purposes. We divided the cohort into training/testing sets (80%/20%). We segmented EAT using a previously validated, deep-learning method with optional manual correction. We extracted 148 initial EAT features (eg, morphologic, spatial, and HU), dubbed fat-omics, and used Cox elastic-net for feature reduction and prediction of MACE. Bootstrap validation gave CIs. Results: Traditional EAT features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-indices 0.53/0.55/0.57, respectively). Significant improvement was obtained with the 15-feature fat-omics model (C-index = 0.69, test set). High-risk features included the volume-of-voxels-having-elevated-HU-[-50,-30-HU] and HU-negative-skewness, both of which assess high HU values in EAT, a property implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, with the high-risk group having an HR 2.4 times that of the low-risk group (P < 0.001). Conclusions: Preliminary findings indicate an opportunity to use finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

Incidence of cardiovascular events in a population-based Danish cohort with atopic dermatitis.

Background: The risk of cardiovascular disease in atopic dermatitis (AD) is not well established. Objectives: Our aims were to evaluate the incidence rate (IR) of venous thromboembolism (VTE) in patients with AD in a population-based cohort study and to assess atherosclerotic cardiovascular disease (ASCVD) risk factors and incidence of malignancies, major adverse cardiovascular events (MACE), and VTE in patients with AD and rheumatoid arthritis (RA) in a nested cohort analysis. Methods: Data from individuals age 12 years or older (nested cohort age ≥ 18 years) from January 1, 2000, to December 31, 2018, were extracted from the Danish National Patient Registry. Patients with AD were age- and sex-matched with 10 healthy controls. ASCVD risk factors included age 65 years or older and history of smoking, coronary artery disease, stroke, deep vein thrombosis (DVT), pulmonary embolism (PE), and malignancy. Results: The population-based cohort comprised 190,751 patients (17,341 patients with AD and 173,410 healthy controls). The IRs per 100 patient-years were comparable between the AD cohort and healthy controls for VTE (0.14 [95% CI = 0.12-0.16] vs 0.11 [95% CI = 0.11-0.12]), DVT (0.08 [95% CI = 0.06-0.09] vs 0.06 [95% CI = 0.06-0.07]), and PE (0.06 [95% CI = 0.05-0.08] vs 0.05 [95% CI = 0.05-0.05]). The IR for VTE was higher in the AD cohort age 65 years or older (0.71 [95% CI = 0.56-0.90]) than in the age-matched controls (0.50 [95% CI = 0.46-0.54]). ASCVD risk factors were more frequent in the patients with RA than in the patients with AD. The IRs for malignancies and MACE were higher with specific ASCVD risk factors. Conclusions: The IRs of cardiovascular events were comparable between the AD cohort and general population. The risk of VTE, malignancy, or MACE was higher with specific ASCVD risk factors, underscoring the need for patient monitoring.

Efficacy of Nicorandil in Preventing Myocardial Injury and Cardiovascular Outcomes in Patients Undergoing Percutaneous Coronary Intervention (PCI): A Systematic Review and Meta-Analysis.

Percutaneous coronary intervention (PCI) is a common procedure for treating coronary artery disease, but it carries a risk of periprocedural myocardial injury (PMI). This meta-analysis evaluated the efficacy of nicorandil, a hybrid compound with nitrate-like and potassium channel-opening properties, in preventing PMI during PCI. A comprehensive literature search identified 14 studies involving 1,762 patients, with 882 receiving nicorandil and 880 in the control group. The analysis revealed that nicorandil significantly reduced the incidence of PMI (RR: 0.73, 95% CI: 0.61-0.86) and major adverse cardiovascular events (MACE) (RR: 0.76, 95% CI: 0.58-0.99) compared to the control group. Nicorandil's cardioprotective effects are attributed to its ability to improve coronary blood flow, precondition the myocardium, and reduce oxidative stress and inflammation. These findings suggest that nicorandil could be a valuable adjunctive therapy during PCI, potentially improving patient outcomes. However, the study had limitations, including variations in drug administration methods and a lack of individual-level data for subgroup analysis. Future research should focus on optimizing dosing regimens and administration timing and comparing nicorandil's effectiveness with other cardioprotective agents.

Sarcopenia is associated with increased major adverse cardiovascular event incidence in maintenance hemodialysis patients: a prospective cohort study and mediation analysis.

Background: Few studies have investigated the relationship between sarcopenia and the incidence of major adverse cardiovascular events (MACE), which are common complications in maintenance hemodialysis (MHD) patients. This study thus explored the association between sarcopenia and MACE in a prospective cohort with mediation analysis. Methods: Adult MHD patients in Jiangdu People's Hospital in December 2019 were screened. The exposure was sarcopenia, as defined by the 2019 Asian Working Group. The primary endpoint was the occurrence of MACE, defined as the composite of all-cause mortality or hospital admission with a primary diagnosis of acute myocardial infarction, stroke, or heart failure during a 3-year follow-up period. Multivariate Cox regression analyses were used to test the association between sarcopenia and subsequent MACE incidence. Mediation analyses were used to investigate whether potential mediators influenced the association between sarcopenia and MACE. Results: Of the 230 patients enrolled, 57% were male, with a median age of 57 years (interquartile range [IQR]: 50 to 66), and a median dialysis vintage of 67 months (IQR: 32 to 119). The prevalence of sarcopenia was 45.2%. The presence of sarcopenia was significantly correlated with age (Spearman's r = 0.47, p < 0.001), C-reactive protein (Spearman's r = 0.13, p = 0.044), serum albumin (Spearman's r = -0.22, p < 0.001), 25(OH) vitamin D (Spearman's r = -0.26, p < 0.001), and coronary artery calcification score (Spearman's r = 0.20, p = 0.002). Over the 3-year follow-up period, MACE were observed in 59/104 (56.7%) patients with sarcopenia and 38/126 (30.2%) patients without sarcopenia (log-rank p < 0.001). After accounting for potential confounders, patients with sarcopenia presented a 66% (4-168%, p = 0.035) increase in their risk of MACE incidence as compared to non-sarcopenic individuals. However, adjusted mediation analyses did not detect any indication of a causal mediation pathway linking the effects of sarcopenic status on coronary artery calcification score, C-reactive protein, serum albumin, or 25(OH) vitamin D levels to MACE outcomes. Conversely, sarcopenia exhibited a potential direct effect (average direct effect range: -1.52 to -1.37, all p < 0.05) on MACE incidence. Conclusion: These results revealed that the presence of sarcopenia was associated with a higher incidence of MACE in MHD patients. The putative effects of sarcopenia on this cardiovascular endpoint are possibly not mediated by any causal pathways that include vascular calcification, inflammation, hypoalbuminemia, or vitamin D.

The predictive value of the triglyceride glucose index combined with cystatin C for the prognosis of patients with acute coronary syndrome.

Background: Recent studies have shown that the triglyceride glucose index (TyG) and cystatin C (CysC) are closely related to cardiovascular disease, but there is limited research on the prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). The aim of this study was to explore the predictive value of the combination of the TyG index and CysC in predicting major adverse cardiovascular events (MACEs) in ACS patients who underwent PCI. Methods: This retrospective study included 319 ACS patients who underwent PCI. The clinical endpoint was the occurrence of MACEs, including all-cause mortality, heart failure, non-fatal myocardial infarction, target vessel revascularization, and angina requiring hospitalization. Patients were classified into MACEs (65 cases) and non-MACEs (254 cases) groups. Univariate factor and multivariate analysis were used to identify predictors of MACEs. The receiver operating curve (ROC) of the prediction model of MACEs was determined. Additionally, the net reclassification improvement and integrated discrimination improvement indexes were calculated to further assess the additional predictive value of the risk factors for MACEs. Subgroup and interaction analysis between the TyG index combined with CysC and MACEs were conducted in various subgroups. Patients were stratified according to the optimal cutoff point value of the TyG index and the CysC determined by ROC curve analysis. The Kaplan-Meier analysis method was used to construct a survival curve 1 year after PCI. Results: During a median follow-up period of 14 months, 65 (20.38%) patients had experienced at least one primary endpoint event. Multivariate logistic regression analysis indicated that the TyG index and CysC were independently associated with an increased risk of MACEs after PCI (OR, 2.513, 95% CI 1.451-4.351, P= 0.001; and OR, 4.741, 95% CI 1.344-16.731, P=0.016, respectively). The addition of the TyG index and CysC to the baseline risk model had the strongest incremental effect for predicting MACEs in terms of the C-statistic from 0.789 (95% CI 0.723-0.855, P<0.001) to 0.799 (95% CI 0.733-0.865, P<0.001). Furthermore, Kaplan-Meier analysis demonstrated that a TyG index greater than 9.325 and a CysC value greater than 1.065 mg/ml were significantly associated with an increased risk of MACEs (log-rank, all P < 0.01). Conclusion: The TyG index predicts MACEs after PCI in patients with ASC independent of known cardiovascular risk factors. Adjustment of the CysC by the TyG index further improves the predictive ability for MACEs in patients with ACS undergoing PCI. Thus, both of them are expected to become new prognostic indicators for MACEs in patients with ACS after PCI.

The predictive value of coronary computed tomography angiography-derived fractional flow reserve for perioperative cardiac events in lung cancer surgery.

PURPOSE: As a non-invasive coronary functional examination, coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) showed predictive value in several non-cardiac surgeries. This study aimed to evaluate the predictive value of CT-FFR in lung cancer surgery. METHOD: We retrospectively collected 227 patients from January 2017 to June 2022 and used machine learning-based CT-FFR to evaluate the stable coronary artery disease (CAD) patients undergoing lung cancer surgery. The major adverse cardiac event (MACE) was defined as perioperative myocardial injury (PMI), myocardial infarction, heart failure, atrial and ventricular arrhythmia with hemodynamic disorder, cardiogenic shock and cardiac death. The multivariate logistic regression analysis was performed to identify risk factors for MACE and PMI. The discriminative capacity, goodness-of-fit, and reclassification improvement of prediction model were determined before and after the addition of CT-FFR≤0.8. RESULTS: The incidence of MACE was 20.7 % and PMI was 15.9 %. CT-FFR significantly outperformed CCTA in terms of accuracy for predicting MACE (0.737 vs 0.524). In the multivariate regression analysis, CT-FFR≤0.8 was an independent risk factor for both MACE [OR=10.77 (4.637, 25.016), P<0.001] and PMI [OR=8.255 (3.372, 20.207), P<0.001]. Additionally, we found that the performance of prediction model for both MACE and PMI improved after the addition of CT-FFR. CONCLUSIONS: CT-FFR can be used to assess the risk of perioperative MACE and PMI in patients with stable CAD undergoing lung cancer surgery. It adds prognostic information in the cardiac evaluation of patients undergoing lung cancer surgery.

Proposal of a novel cardiovascular risk prediction score in lupus nephritis.

Introduction: Patients with systemic lupus erythematosus are prone to develop cardiovascular disease (CVD), and have increased morbidity and mortality. Methods: We conducted a retrospective analysis on lupus nephritis patients to assess the occurrence and predictors of major adverse cardiovascular events (MACE). Data were collected from patients who underwent kidney biopsy between 2005 and 2020. Statistical analysis was performed to unveil correlations. Results: 91 patients were analyzed in this period, with a mean age of 37.3 ± 12.3 years and 86% being female. The mean follow-up time was 62 ± 48 months. 15.38% of the patients underwent at least one MACE. Two patients deceased of CVD. Increased age (35.81 ± 11.14 vs 45.5 ± 15.11 years, p=0.012) entailed a higher occurrence of MACEs. Neutrophil count (5.15 ± 2.83 vs 7.3 ± 2.99 Giga/L, p=0.001) was higher, whereas diastolic blood pressure (DBP) was lower (89.51 ± 10.96 vs 78.43 ± 6.9 mmHg, p<0.001) at the time of the biopsy in patients with MACE. Age, neutrophil count, and DBP proved to be independent predictors of MACEs. We propose a new model (CANDE - Cardiovascular risk based on Age, Neutrophil count, and Diastolic blood pressure Estimation score) calculated from these variables, which predicts the probability of MACE occurrence. Conclusion: This study underscores the importance of actively screening for cardiovascular risks in this vulnerable patient population. Age, neutrophil count, and diastolic blood pressure have been established as independent risk factors for MACE in lupus nephritis. The CANDE score derived from these parameters may serve as a prompt, cost-effective, and easily accessible estimation tool for assessing the likelihood of major adverse cardiovascular risk. These findings emphasize the necessity for comprehensive management strategies addressing both immune dysregulation and cardiovascular risk factors in systemic lupus erythematosus to mitigate adverse outcomes.

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.

BACKGROUND: Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes. METHODS: From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction. RESULTS: We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value. CONCLUSIONS: The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

Cardiovascular adverse events associated with PARP inhibitors for ovarian cancer: a real world study (RWS) with Bayesian disproportional analysis based on the FDA adverse event reporting system (FAERS).

BACKGROUND: To investigate the pharmacovigilance (PV) and make pairwise comparisons on reporting proportion, seriousness, and severity of outcomes of major adverse cardiovascular events (MACE) among poly(ADP-ribose) polymerase-inhibitors (PARPis) in treating ovarian cancer, fallopian tube carcinoma, and primary peritoneal cancer (collectively named EOC) from the US Food and Drug Administration Adverse Event Reporting System (FAERS). RESEARCH DESIGN AND METHODS: Data on adverse cardiovascular events reports related to EOC treatment submitted to FAERS from the first quarter of 2015 to the second quarter of 2023 were harvested. Three PARPis were identified: olaparib, niraparib, and rucaparib. RESULTS: Eventually, a total of 258,596 eligible records were enrolled with 12,331 reports including 5,292 reports of MACE and 7,039 reports of other cardiovascular events. For the primary composite endpoint, a PV signal associated with MACE was detected in niraparib (ROR = 1.12; IC025 = 0.03), whereas it was not detected in olaparib and rucaparib; For the secondary endpoint, PV signals associated with other cardiovascular events were detected in niraparib (ROR = 1.17;IC025 = 0.04), but not in olaparib and rucaparib. CONCLUSIONS: For EOC patients, close monitoring of blood pressure, heart rate, and coagulation function should be conducted when selecting niraparib for treatment.

Prognostic effect of the TyG index on patients with severe aortic stenosis following transcatheter aortic valve replacement: a retrospective cohort study.

BACKGROUND: The triglyceride glucose (TyG) index, as a reliable marker of insulin resistance, is associated with the incidence and poor prognosis of various cardiovascular diseases. However, the relationship between the TyG index and clinical outcomes in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR) remains unclear. METHODS: This study consecutively enrolled 1569 patients with AS underwent TAVR at West China Hospital of Sichuan University between April 2014 and August 2023. The outcomes of interest included all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Multivariate adjusted Cox regression and restricted cubic splines (RCS) regression analyses were used to assess the associations between the TyG index and the clinical outcomes. The incremental prognostic value of the TyG index was further assessed by the time-dependent Harrell's C-index, integrated discrimination improvement (IDI) and the net reclassification improvement (NRI). RESULTS: During a median follow-up of 1.09 years, there were 146, 70, and 196 patients experienced all-cause death, cardiovascular death, and MACE, respectively. After fully adjusting for confounders, a per-unit increase of TyG index was associated with a 441% (adjusted HR: 5.41, 95% CI: 4.01-7.32), 385% (adjusted HR: 4.85, 95% CI: 3.16-7.43), and 347% (adjusted HR: 4.47, 95% CI: 3.42-5.85) higher risk of all-cause mortality, cardiovascular mortality and MACE, respectively. The RCS regression analyses revealed a linear association between TyG index and endpoints (all P for non-linearity > 0.05) with 8.40 as the optimal binary cutoff point. Furthermore, adding TyG index to the basic risk model provided a significant incremental value in predicting poor prognosis (Time-dependent Harrell's C-index increased for all the endpoints; All-cause mortality, IDI: 0.11, P < 0.001; NRI: 0.32, P < 0.001; Cardiovascular mortality, IDI: 0.043, P < 0.001; NRI: 0.37, P < 0.001; MACE, IDI: 0.092, P < 0.001; NRI: 0.32, P < 0.001). CONCLUSIONS: In patients with severe AS receiving TAVR, there was a positive linear relationship between TyG index and poor prognosis, with 8.4 as the optimal bivariate cutoff value. Our findings suggest TyG index holds potential value for risk stratification and guiding therapeutic decisions in patients after TAVR.

Using ultrasound radiomics to forecast adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention.

OBJECTIVE: Exploring the performance of ultrasound-based radiomics in forecasting major adverse cardiovascular events (MACE) within 1 year following percutaneous coronary intervention (PCI) of acute coronary syndrome (ACS) patients. METHODS: In this research, 161 ACS patients who underwent PCI were included (114 patients were randomly assigned to the training set and 47 patients to the validation set). Every patient received echocardiography 3-7 days after PCI and followed up for 1 year. The radiomics features related to MACE occurrence were extracted and selected to formulate the RAD score. Building ultrasound personalized model by incorporating RAD score, LVEF, LVGLS, and NT-ProBNP. The model's capacity to predict was tested using ROC curves. RESULTS: Multifactorial logistic regression analysis of RAD score with clinical data and echocardiographic parameters indicated RAD score and LVGLS as independent risk factors for the occurrence of MACE. The RAD score predicted MACE, with AUC values of 0.85 and 0.86 in the training and validation sets. The ultrasound personalized model had a superior ability to predict the occurrence of MACE, with AUC values of 0.88 and 0.92, which were higher than those of the clinical model (with AUC of 0.72 and 0.80) without RAD score (Z = 3.711, 2.043, P < .001, P = .041). Furthermore, DCA indicated that the ultrasound personalization model presented a more favorable net clinical benefit. CONCLUSIONS: Ultrasound radiomics can be a reliable tool to predict the incidence of MACE after PCI in patients with ACS and provides quantifiable data for personalized clinical treatment.

Early coronary angiography in NSTEMI: a regional Victorian perspective.

BACKGROUND: Current guidelines highlight a paucity of evidence guiding optimal timing for non-ST-elevation myocardial infarction (NSTEMI) in high-risk and non-high-risk cases. AIM: We assessed long-term major adverse cardiovascular events (MACEs) in NSTEMI patients undergoing early (<24 h) versus delayed (>24 h) coronary angiography at 6 years. Secondary end-points included all-cause mortality and cumulative MACE outcomes. METHODS: Baseline characteristics and clinical outcomes were assessed among 355 patients presenting to a tertiary regional hospital between 2017 and 2018. Cox proportional hazard models were generated for MACE and all-cause mortality outcomes, adjusting for the Global Registry of Acute Coronary Events (GRACE) score, patient demographics, biomarkers and comorbidities. RESULTS: Two hundred and seventy patients were included; 147 (54.4%) and 123 (45.6%) underwent early and delayed coronary angiography respectively. Median time to coronary angiography was 13.3 and 45.4 h respectively. At 6 years, 103 patients (38.1%) experienced MACE; 41 in the early group and 62 in the delayed group (hazard ratio (HR) = 2.23; 95% confidence interval (CI) = 1.50-3.31). After multivariable adjustment, the delayed group had higher rates of MACE (HR = 1.79; 95% CI = 1.19-2.70), all-cause mortality (HR = 2.76; 95% CI = 1.36-5.63) and cumulative MACE (incidence rate ratio = 1.54; 95% CI = 1.12-2.11). Subgroup analysis of MACE outcomes in rural and weekend NSTEMI presentations was not significant between early and delayed coronary angiography (HR = 1.49; 95% CI = 0.83-2.62). CONCLUSION: Higher MACE rates in the delayed intervention group suggest further investigation is needed. Randomised control trials would be well suited to assess the role of early invasive intervention across all NSTEMI risk groups.

Derivation of an Annualized Claims-Based Major Adverse Cardiovascular Event Estimator in Type 2 Diabetes.

Background: Major adverse cardiovascular events (MACE) are a leading cause of morbidity and mortality among adults with type 2 diabetes. Currently, available MACE prediction models have important limitations, including reliance on data that may not be routinely available, narrow focus on primary prevention, limited patient populations, and longtime horizons for risk prediction. Objectives: The purpose of this study was to derive and internally validate a claims-based prediction model for 1-year risk of MACE in type 2 diabetes. Methods: Using medical and pharmacy claims for adults with type 2 diabetes enrolled in commercial, Medicare Advantage, and Medicare fee-for-service plans between 2014 and 2021, we derived and internally validated the annualized claims-based MACE estimator (ACME) model to predict the risk of MACE (nonfatal acute myocardial infarction, nonfatal stroke, and all-cause mortality). The Cox proportional hazards model was composed of 30 covariates, including patient age, sex, comorbidities, and medications. Results: The study cohort comprised 6,623,526 adults with type 2 diabetes, mean age 68.1 ± 10.6 years, 49.8% women, and 73.0% Non-Hispanic White. ACME had a concordance index of 0.74 (validation index range: 0.739-0.741). The predicted 1-year risk of the study cohort ranged from 0.4% to 99.9%, with a median risk of 3.4% (IQR: 2.3%-6.5%). Conclusions: ACME was derived in a large usual care population, relies on routinely available data, and estimates short-term MACE risk. It can support population risk stratification at the health system and payer levels, participant identification for decentralized clinical trials of cardiovascular disease, and risk-stratified observational studies using real-world data.

Eicosapentaenoic Acid Level Predicts Long-Term Survival and Cardiovascular or Limb Event in Peripheral Arterial Disease.

Objectives: We examined the relationship between plasma eicosapentaenoic acid (EPA) level and long-term all-cause death (ACD) and cardiovascular or limb events in patients with peripheral arterial disease (PAD). Method: We performed a prospective cohort study on 637 PAD patients. The endpoints were ACD, major adverse cardiovascular events (MACEs), and lower extremity arterial events (LEAEs). Results: The incidences of ACD, MACEs, and LEAEs had correlation with EPA levels (p <0.05). Plasma EPA level had significant positive correlations with high-density lipoprotein cholesterol, triglyceride, and estimated glomerular filtration rate (eGFR), and negative correlation with C-reactive protein (CRP). In Cox stepwise multivariate analysis, lower EPA (hazard ratio [HR]: 0.996, 95% confidence interval [CI]: 0.993-1.000, p = 0.034), ankle brachial pressure index (ABI), body mass index, serum albumin, eGFR, age, CRP, D-dimer, critical limb ischemia, diabetes, cerebrovascular disease (CVD), and statin were related to ACD (p <0.05); lower EPA (HR: 0.997, 95% CI: 0.994-1.000, p = 0.038), ABI, serum albumin, eGFR, age, diabetes, coronary heart disease, CVD, and statin were related to MACEs (p <0.05); and lower EPA (HR: 0.988, 95% CI: 0.982-0.993, p <0.001), ABI, and low-density lipoprotein cholesterol were related to LEAEs (p <0.05). Conclusions: Low plasma EPA level was a significant risk factor for ACD, MACEs, and LEAEs in patients with PAD.

Clinical impact of Academic Research Consortium for High Bleeding-Risk scores on clinical outcomes in patients with stable coronary artery disease undergoing percutaneous coronary intervention.

High bleeding risk (HBR), as defined by the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria, has been recently reported to be associated with an increased risk of major bleeding events and cardiovascular events. We investigated the association between the ARC-HBR score and clinical outcomes in patients with stable coronary artery disease (CAD) who underwent percutaneous coronary intervention (PCI). We assessed 328 consecutive patients with stable CAD who underwent PCI between January 2017 and December 2020. We scored the ARC-HBR criteria by assigning 1 point to each major criterion and 0.5 points to each minor criterion. Patients were stratified into low (ARC-HBR score < 1), intermediate (1 ≤ ARC-HBR score < 2), and high (ARC-HBR score ≥ 2) bleeding-risk groups. The primary outcome measure was major adverse cardiovascular events (MACE), defined as a composite of all-cause death, nonfatal myocardial infarction, and nonfatal stroke. We compared the discriminative abilities of the ARC-HBR score with the Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS2°P) and ARC-HBR score with Coronary Revascularization Demonstrating Outcome Study in Kyoto (CREDO-Kyoto) thrombotic risk score. The mean patient age was 70.1 ± 10.2 years (males, 76.8%). During the median follow-up period of 983 (618-1338) days, 44 patients developed MACE. Kaplan-Meier curves showed that a stepwise significant increase in the cumulative incidence of MACE as the ARC-HBR score increased (log-rank p < 0.001). In the time-dependent receiver-operating characteristic curve analysis for predicting MACE within 2 years, the area under the curve (AUC) of the ARC-HBR score was significantly higher than that of the TRS2°P (AUC: 0.825 vs. 0.725, p value for the difference = 0.023) and similar to that of CREDO-Kyoto thrombotic risk score (AUC: 0.825 vs. 0.813, p value for the difference = 0.627). Conclusions: The ARC-HBR score adequately stratified future risk of MACE in patients with stable CAD who underwent PCI. The ARC-HBR score showed a higher discriminative ability for predicting mid-term MACE than the TRS2°P.

Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

Applications of Nanotechnology in the Field of Cardiology.

Cardiovascular diseases (CVDs) are a leading cause of death globally, demanding innovative therapeutic strategies. Nanoformulations, including nanoparticles, address challenges in drug delivery, stem cell therapy, imaging, and gene delivery. Nanoparticles enhance drug solubility, bioavailability, and targeted delivery, with gas microbubbles, liposomal preparations, and paramagnetic nanoparticles showing potential in treating atherosclerosis and reducing systemic side effects. In stem cell therapy, nanoparticles improve cell culture, utilizing three-dimensional nanofiber scaffolds and enhancing cardiomyocyte growth. Gold nanoparticles and poly(lactic-co-glycolic acid) (PLGA)-derived microparticles promote stem cell survival. Stem cell imaging utilizes direct labeling with nanoparticles for magnetic resonance imaging (MRI), while optical tracking employs dye-conjugated nanoparticles. In gene delivery, polymeric nanoparticles like polyethylenimine (PEI) and dendrimers, graphene-based carriers, and chitosan nanoparticles offer alternatives to virus-mediated gene transfer. The potential of magnetic nanoparticles in gene therapy is explored, particularly in hepatocellular carcinoma. Overall, nanoparticles have transformative potential in cardiovascular disease management, with ongoing research poised to enhance clinical outcomes.

Plasma leucine-rich α-2 glycoprotein 1 in ST-elevation myocardial infarction: vertical variation, correlation with T helper 17/regulatory T ratio, and predictive value on major adverse cardiovascular events.

Objective: Leucine-rich α-2 glycoprotein 1 (LRG1) promotes inflammation and myocardial injury, but its clinical role in ST-elevation myocardial infarction (STEMI) is rarely disclosed. Herein, this prospective study aimed to explore the value of plasma LRG1 at different time points to predict major adverse cardiovascular event (MACE) risk in patients with STEMI. Methods: In total, 209 patients with STEMI were enrolled for determining plasma LRG1 at admission and on day (D)1/D7/D30 after admission via enzyme-linked immunosorbent assay, as well as for determination of peripheral blood T helper 17 (Th17) cells and regulatory T (Treg) cells by flow cytometry. In addition, plasma LRG1 was obtained from 30 healthy controls at enrollment. Results: LRG1 was increased in patients with STEMI at admission compared with healthy controls (P < 0.001). In patients with STEMI, LRG1 varied at different time points (P < 0.001), which elevated from admission to D1, and gradually declined thereafter. LRG1 at admission was positively associated with Th17 cells (P = 0.001) and Th17/Treg ratio (P = 0.014). LRG1 at admission (P = 0.013), D1 (P = 0.034), D7 (P = 0.001), and D30 (P = 0.010) were increased in patients with MACE compared with those without. LRG1 at D7 exhibited good ability to estimate MACE risk (area under curve = 0.750, 95% confidence interval = 0.641-0.858). LRG1 at admission > 60 µg/ml (P = 0.031) and D7 > 60 µg/ml (P = 0.018) were linked with increased accumulating MACE. Importantly, LRG1 at D7 > 60 µg/ml was independently correlated with increased MACE risk (hazard ratio = 5.216, P = 0.033). Conclusion: Plasma LRG1 increases from admission to D1 and gradually declines until D30, which positively links with Th17 cells and MACE risk in patients with STEMI.

Time to benefit of colchicine in patients with cardiovascular disease: A pooled analysis of randomized controlled trials.

Background: Low-dose colchicine has been shown to lower major adverse cardiovascular events (MACE) among those with cardiovascular disease (CVD). It remains unclear how long a CVD patient needs to live to potentially benefit from colchicine. Our study aimed to determine the time to benefit (TTB) of colchicine in individuals with CVD. Methods: Literature searches were performed in PubMed for the cardiovascular outcome trial of colchicine in patients with CVD until October 12, 2023. The primary outcome measured was MACE. Reconstructed individual participant data (IPD) and the stratified Cox proportional hazards model were used to calculate the hazard ratio (HR) and 95 % confidence interval (CI) to estimate the efficacy of colchicine, and Weibull survival curves were fitted to estimate TTB for specific absolute risk reduction (ARR) thresholds (0.002, 0.005, and 0.01). Results: Four trials randomizing 11,594 adults aged between 59.8 and 66.5 years were included (follow-up duration: 12-28.6 months). Compared with placebo, colchicine reduced the risk of MACE (HR 0.68, 95 % CI: 0.60 to 0.78) but had no impact on cardiovascular and all-cause mortality. A TTB of 11.0 months (95 % CI: 0.59 to 21.3) was estimated to be needed to prevent 1 MACE in 100-colchicine-treated patients. The TTB for acute coronary syndrome was similar compared to stable coronary artery disease (10.7 vs. 11.2 months for ARR = 0.010). Conclusions: By using reconstructed IPD, this pooled analysis demonstrated that colchicine was associated with reduced nonfatal MACE, and the TTB was approximately 11.0 months to prevent 1 MACE per 100 patients.