Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population.
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ABSTRACT:
SUBMITTER: Mendonca MI
PROVIDER: S-EPMC8201463 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCoronary artery disease (CAD) and major adverse cardiovascular events (MACE) are the leading causes of death in the general population, but risk stratification remains suboptimal. CAD genetic risk scores (GRSs) predict risk independently from clinical tools, like QRISK3. We assessed the added value of GRSs for a variety of cardiovascular traits (CV GRSs) for predicting CAD and MACE and tested their early-life screening potential by comparing against the CAD GRS only.MethodsWe used data from 379 581 participants in the UK Biobank without known cardiovascular conditions (follow-up, 11.3 years; 3.3% CAD cases and 5.2% MACE cases). In a training subset (50%) we built 3 scores: QRISK3; QRISK3 and an established CAD GRS; and QRISK3, the CAD GRS and the CV GRSs. In an independent subset (50%), we evaluated each score's performance using the concordance index, odds ratio and net reclassification index. We then repeated the analyses without considering QRISK3.ResultsFor CAD, the combination of QRISK3 and the CAD GRS had a better performance than QRISK3 alone (concordance index, 0.766 versus 0.753; odds ratio, 5.47 versus 4.82; net reclassification index, 7.7%). Adding the CV GRSs did not significantly improve risk stratification. When only looking at genetic information, the combination of CV GRSs and the CAD GRS had a better performance than the CAD GRS alone (concordance index, 0.637 versus 0.625; odds ratio, 2.17 versus 2.07; net reclassification index, 3.3%). Similar results were obtained for MACE.ConclusionsIn individuals without known cardiovascular disease, the inclusion of CV GRSs to a clinical tool and an established CAD GRS does not improve CAD or MACE risk stratification. However, their combination only with the CAD GRS increases prediction performance indicating potential use in early-life screening before the advanced development of conventional cardiovascular risk factors.
| S-EPMC9584057 | biostudies-literature
Project description:BackgroundHypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.MethodsIn this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.ResultsThe study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.ConclusionsThis study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.
| S-EPMC9771902 | biostudies-literature
Project description:ObjectivesTo investigate the prognostic value of computed tomography fractional flow reserve (CT-FFR) in patients with diabetes and to establish a risk stratification model for major adverse cardiac event (MACE).MethodsDiabetic patients with intermediate pre-test probability of coronary artery disease were prospectively enrolled. All patients were referred for coronary computed tomography angiography and followed up for at least 2 years. In the training cohort comprising of 957 patients, two models were developed: model1 with the inclusion of clinical and conventional imaging parameters, model2 incorporating the above parameters + CT-FFR. An internal validation cohort comprising 411 patients and an independent external test cohort of 429 patients were used to validate the proposed models.Results1797 patients (mean age: 61.0 ± 7.0 years, 1031 males) were finally included in the present study. MACE occurred in 7.18% (129/1797) of the current cohort during follow- up. Multivariate Cox regression analysis revealed that CT-FFR ≤ 0.80 (hazard ratio [HR] = 4.534, p < 0.001), HbA1c (HR = 1.142, p = 0.015) and low attenuation plaque (LAP) (HR = 3.973, p = 0.041) were the independent predictors for MACE. In the training cohort, the Log-likelihood test showed statistical significance between model1 and model2 (p < 0.001). The C-index of model2 was significantly larger than that of model1 (C-index = 0.82 [0.77-0.87] vs. 0.80 [0.75-0.85], p = 0.021). Similar findings were found in internal validation and external test cohorts.ConclusionCT-FFR was a strong independent predictor for MACE in diabetic cohort. The model incorporating CT-FFR, LAP and HbA1c yielded excellent performance in predicting MACE.
| S-EPMC10032006 | biostudies-literature
Project description:BackgroundEmerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients.MethodsThis was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA).ResultsUltimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO2/FiO2 under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram.ConclusionsWe developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.
| S-EPMC9466355 | biostudies-literature
Project description:BackgroundST-segment elevation myocardial infarction (STEMI) patients are at a high residual risk of major adverse cardiovascular events (MACEs) after revascularization. Risk factors modify prognostic risk in distinct ways in different STEMI subpopulations. We developed a MACEs prediction model in patients with STEMI and examined its performance across subgroups.MethodsMachine-learning models based on 63 clinical features were trained in patients with STEMI who underwent PCI. The best-performing model (the iPROMPT score) was further validated in an external cohort. Its predictive value and variable contribution were studied in the entire population and subgroups.ResultsOver 2.56 and 2.84 years, 5.0% and 8.33% of patients experienced MACEs in the derivation and external validation cohorts, respectively. The iPROMPT score predictors were ST-segment deviation, brain natriuretic peptide (BNP), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), age, hemoglobin, and white blood cell (WBC) count. The iPROMPT score improved the predictive value of the existing risk score, with an increase in the area under the curve to 0.837 [95% confidence interval (CI): 0.784-0.889] in the derivation cohort and 0.730 (95% CI: 0.293-1.162) in the external validation cohort. Comparable performance was observed between subgroups. The ST-segment deviation was the most important predictor, followed by LDL-C in hypertensive patients, BNP in males, WBC count in females with diabetes mellitus, and eGFR in patients without diabetes mellitus. Hemoglobin was the top predictor in non-hypertensive patients.ConclusionThe iPROMPT score predicts long-term MACEs following STEMI and provides insights into the pathophysiological mechanisms for subgroup differences.
| S-EPMC10167292 | biostudies-literature
Project description:BackgroundAs a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice.ObjectiveGiven the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE occurrence within 6 months based on machine learning algorithms.MethodsA retrospective study was performed including 1004 patients who had undergone coronary revascularization at The People's Hospital of Liaoning Province and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from June 2019 to December 2020. According to the characteristics of available data, an oversampling strategy was adopted for initial preprocessing. We then employed six machine learning algorithms, including decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and extreme gradient boosting (XGBoost), to develop prediction models for MACE depending on clinical information and 6-month follow-up information. Among all samples, 70% were randomly selected for training and the remaining 30% were used for model validation. Model performance was assessed based on accuracy, precision, recall, F1-score, confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), and visualization of the ROC curve.ResultsUnivariate analysis showed that 21 patient characteristic variables were statistically significant (P<.05) between the groups without and with MACE. Coupled with these significant factors, among the six machine learning algorithms, XGBoost stood out with an accuracy of 0.7788, precision of 0.8058, recall of 0.7345, F1-score of 0.7685, and AUC of 0.8599. Further exploration of the models to identify factors affecting the occurrence of MACE revealed that use of anticoagulant drugs and course of the disease consistently ranked in the top two predictive factors in three developed models.ConclusionsThe machine learning risk models constructed in this study can achieve acceptable performance of MACE prediction, with XGBoost performing the best, providing a valuable reference for pointed intervention and clinical decision-making in MACE prevention.
| S-EPMC9069286 | biostudies-literature
Project description:Soluble TNF-like weak inducer of apoptosis (sTWEAK) is a proinflammatory cytokine belonging to the TNF superfamily. sTWEAK concentrations have been associated with the presence of CKD and cardiovascular disease (CVD). We hypothesized that sTWEAK levels may relate to a higher prevalence of atherosclerotic plaques, vascular calcification, and cardiovascular outcomes observed in patients with CKD.A 4-year prospective, multicenter, longitudinal study was conducted in 1058 patients with CKD stages 3-5D (mean age =58±13 years old; 665 men) but without any history of CVD from the NEFRONA Study (a study design on the prevalence of surrogate markers of CVD). Ankle-brachial index and B-mode ultrasound were performed to detect the presence of carotid and/or femoral atherosclerotic plaques together with biochemical measurements and sTWEAK assessment. Patients were followed for cardiovascular outcomes (follow-up of 3.13±1.15 years).Patients with more advanced CKD had lower sTWEAK levels. sTWEAK concentrations were independently and negatively associated with carotid intima-media thickness. sTWEAK levels were lower in patients with carotid atherosclerotic plaques but not in those with femoral plaques. After adjustment by confounders, the odds ratio (OR) for presenting carotid atherosclerotic plaques in patients in the lowest versus highest tertile of sTWEAK was 4.18 (95% confidence interval [95% CI], 2.89 to 6.08; P<0.001). Furthermore, sTWEAK levels were lower in patients with calcified carotid atherosclerotic plaques. The OR for presenting calcified carotid plaques was 1.77 (95% CI, 1.06 to 2.93; P=0.02) after multivariable adjustment. After the follow-up, 41 fatal and 68 nonfatal cardiovascular events occurred. In a Cox model, after controlling for potential confounding factors, patients in the lowest tertile of sTWEAK concentrations had a higher risk of fatal and nonfatal cardiovascular events (hazard ratio [HR], 2.40; 95% CI, 1.33 to 4.33; P=0.004) and cardiovascular mortality (HR, 2.67; 95% CI, 1.05 to 6.76; P=0.04).Low sTWEAK levels were associated with the presence of carotid atherosclerotic plaques in patients with CKD. Additionally, lower sTWEAK levels were associated with a higher risk of cardiovascular morbidity and mortality.
| S-EPMC4791813 | biostudies-other
Project description:ImportancePatients with stroke due to nontraumatic (spontaneous) intracerebral hemorrhage (ICH) often harbor vascular risk factors and comorbidities, but it is unclear which major adverse cardiovascular events (MACEs) occur more frequently among patients with a prior ICH than the general population.ObjectiveTo evaluate the risk of a MACE for patients with a prior ICH compared with the general population.Design, setting, and participantsThis cohort study identified 8991 patients with a first ICH in the Danish Stroke Registry from January 1, 2005, to June 30, 2018, who were aged 45 years or older and survived more than 30 days after an ICH. Patients in this ICH cohort were matched 1:40 on age, sex, and ICH-onset date with a comparison cohort of 359 185 individuals from the general population without a prior ICH. Both cohorts were followed up for 6 months or more until December 31, 2018, for outcomes using registry data. Data were analyzed from October 1, 2021, to July 19, 2022.ExposuresIntracerebral hemorrhage identified by a nationwide clinical database.Main outcomes and measuresThe main outcomes were ICH, ischemic stroke, myocardial infarction, and a composite of MACEs. For each outcome, a case-control study nested within the cohorts was also performed, adjusting for time-varying exposures and potential confounders. Crude absolute event rates per 100 person-years, adjusted hazard ratios (aHRs) and 95% CIs and, in the nested case-control analyses, crude and adjusted odds ratios and 95% CIs were calculated.ResultsThe ICH cohort (n = 8991; 4814 men [53.5%]; mean [SD] age, 70.7 [11.5] years) had higher event rates than the comparison cohort (n = 359 185; 192 256 men [53.5%]; mean [SD] age, 70.7 [11.5] years) for MACEs (4.16 [95% CI, 3.96-4.37] per 100 person-years vs 1.35 [95% CI, 1.33-1.36] per 100 person-years; aHR, 3.13 [95% CI, 2.97-3.30]), ischemic stroke (1.52 [95% CI, 1.40-1.65] per 100 person-years vs 0.56 [95% CI, 0.55-0.57] per 100 person-years; aHR, 2.64 [95% CI, 2.43-2.88]), and ICH (1.44 [95% CI, 1.32-1.56] per 100 person-years vs 0.06 [95% CI, 0.06-0.07] per 100 person-years; aHR, 23.49 [95% CI, 21.12-26.13]) but not myocardial infarction (0.52 [95% CI, 0.45-0.60] per 100 person-years vs 0.48 [95% CI, 0.47-0.49] per 100 person-years; aHR, 1.12 [95% CI, 0.97-1.29]). Nested case-control analyses returned risk estimates of similar magnitude as the cohort analyses.Conclusions and relevanceThe findings of this cohort study suggest that Danish patients with a prior ICH had statistically significantly higher rates of MACEs than the general population, indicating a need for attention to optimal secondary prevention with blood pressure lowering and antithrombotic and statin therapies after an ICH in clinical research and practice.
| S-EPMC9530971 | biostudies-literature
Project description:BackgroundUp to 30% of patients admitted to hospitals with invasive pneumococcal disease (IPD) experience major adverse cardiovascular event (MACE) including new/worsening heart failure, new/worsening arrhythmia, and/or myocardial infarction. Streptococcus pneumoniae (Spn) is the most frequently isolated bacterial pathogen among community-acquired pneumonia (CAP) patients and the only etiological agent linked independently to MACE. Nevertheless, no clinical data exist identifying which serotypes of Spn are principally responsible for MACE.MethodsThis was an observational multicenter retrospective study conducted through the Public Health Secretary of Bogotá, Colombia. We included patients with a confirmed clinical diagnosis of IPD with record of pneumococcal serotyping and clinical information between 2012 and 2019. Spn were serotyped using the quellung method by the National Center of Microbiology. MACE were determined by a retrospective chart review.ResultsThe prevalence of MACE was 23% (71/310) in IPD patients and 28% (53/181) in patients admitted for CAP. The most prevalent S. pneumoniae serotype identified in our study was the 19A, responsible for the 13% (42/310) of IPD in our cohort, of which 21% (9/42) presented MACE. Serotypes independently associated with MACE in IPD patients were serotype 3 (odds ratio [OR] 1, 48; 95% confidence interval [CI] [1.21-2.27]; P = .013) and serotype 9n (OR 1.29; 95% CI [1.08-2.24]; P = .020). Bacteremia occurred in 87% of patients with MACE. Moreover, serum concentrations of C-reactive protein were elevated in patients with MACE versus in non-MACE patients (mean [standard deviation], 138 [145] vs 73 [106], P = .01).ConclusionsMACE are common during IPD with serotype 3 and 9n independently of frequency.
| S-EPMC8599727 | biostudies-literature
Project description:ObjectiveFrailty is an emerging risk factor for adverse outcomes. However, perioperative frailty assessments derived from electronic health records have not been studied on a large scale. We aim to estimate the prevalence of frailty and the associated incidence of major adverse cardiovascular events (MACE) among adults hospitalized for noncardiac surgery.MethodsAdults aged ≥45 years hospitalized for noncardiac surgery from 2004-2014 were identified from the National Inpatient Sample. The validated Hospital Frailty Risk Score (HFRS) derived from International Classification of Diseases codes was used to classify patients as low (HFRS <5), medium (5-10), or high (>10) frailty risk. The primary outcome was MACE, defined as myocardial infarction, cardiac arrest, and in-hospital mortality. Multivariable logistic regression was used to estimate the adjusted odds of MACE stratified by age and HFRS.ResultsA total of 55,349,978 hospitalizations were identified, of which 81.0%, 14.4%, and 4.6% had low, medium, and high HFRS, respectively. Patients with higher HFRS had more cardiovascular risk factors and comorbidities. MACE occurred during 2.5% of surgical hospitalizations and was common among patients with high frailty scores (high HFRS: 9.1%, medium: 6.9%, low: 1.3%, P < .001). Medium (adjusted odds ratio [aOR] 2.05; 95% confidence interval [CI], 2.02-2.08) and high (aOR 2.75; 95% CI, 2.70-2.79) HFRS were associated with greater odds of MACE vs low HFRS, with the greatest odds of MACE observed in younger individuals 45-64 years (interaction P value < .001).ConclusionsThe HFRS may identify frail surgical inpatients at risk for adverse perioperative cardiovascular outcomes.
| S-EPMC10038881 | biostudies-literature