Dataset Information

Predictive Value of Plasma Big Endothelin-1 in Adverse Events of Patients With Coronary Artery Restenosis and Diabetes Mellitus: Beyond Traditional and Angiographic Risk Factors.

ABSTRACT:

Background

Patients with diabetes are a high-risk group for coronary in-stent restenosis (ISR), so it would be valuable to identify biomarkers to predict their prognosis. The plasma big endothelin-1 (big ET-1) level is closely related to cardiovascular adverse events; however, for patients with ISR and diabetes who undergo percutaneous coronary intervention (PCI), whether big ET-1 is independently correlated with prognosis is still uncertain.

Methods

Patients with drug-eluting stent (DES) restenosis who underwent successful re-PCI from January 2017 to December 2018 at the Chinese Academy of Medical Sciences Fuwai Hospital were enrolled and followed up for 3 years. The patients were divided into the tertiles of baseline big ET-1. The primary end points were major adverse cardiovascular events (MACEs): cardiac death, non-fatal myocardial infarction (MI), target lesion revascularization (TLR), and stroke. A Cox multivariate proportional hazard model and the C-statistic were used to evaluate the potential predictive value of big ET-1 beyond traditional and angiographic risk factors.

Results

A total of 1,574 patients with ISR were included in this study, of whom 795 were diabetic. In patients with ISR and diabetes, after an average follow-up of 2.96 ± 0.56 years, with the first tertile of big ET-1 as a reference, the hazard ratio [HR] (95% CI) of MACEs after adjustment for traditional and angiographic risk factors was 1.24 (0.51-3.05) for the second tertile and 2.60 (1.16-5.81) for the third. Big ET-1 improved the predictive value for MACEs over traditional risk factors (C-statistic: 0.64 vs. 0.60, p = 0.03). Big ET-1 was not significantly associated with the risk of MACEs in patients without diabetes.

Conclusion

Increased plasma big ET-1 was associated with a higher risk of adverse cardiovascular prognosis independent of traditional and angiographic risk factors, and therefore, it might be used as a predictive biomarker, in patients with ISR and diabetes.

SUBMITTER: Ma Y

PROVIDER: S-EPMC9177997 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictive Value of Plasma Big Endothelin-1 in Adverse Events of Patients With Coronary Artery Restenosis and Diabetes Mellitus: Beyond Traditional and Angiographic Risk Factors.

Ma Yue Y Tian Tao T Wang Tianjie T Wang Juan J Guan Hao H Yuan Jiansong J Song Lei L Yang Weixian W Qiao Shubin S

Frontiers in cardiovascular medicine 20220526

<h4>Background</h4>Patients with diabetes are a high-risk group for coronary in-stent restenosis (ISR), so it would be valuable to identify biomarkers to predict their prognosis. The plasma big endothelin-1 (big ET-1) level is closely related to cardiovascular adverse events; however, for patients with ISR and diabetes who undergo percutaneous coronary intervention (PCI), whether big ET-1 is independently correlated with prognosis is still uncertain.<h4>Methods</h4>Patients with drug-eluting ste ...[more]

PMID: 35694656

Similar Datasets

Project description:BackgroundIn sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.MethodsA total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.ResultsIn sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).ConclusionThe nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.

Project description:BACKGROUND:There exist several predictive risk models for cardiovascular disease (CVD), including some developed specifically for patients with type 2 diabetes mellitus (T2DM). However, the models developed for a diabetic population are based on information derived from medical records or laboratory results, which are not typically available to entities like payers or quality of care organizations. The objective of this study is to develop and validate models predicting the risk of cardiovascular events in patients with T2DM based on medical insurance claims data. METHODS:Patients with T2DM aged 50 years or older were identified from the Optum™ Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006-09/30/2016). Risk factors were assessed over a 12-month baseline period and cardiovascular events were monitored from the end of the baseline period until end of data availability, continuous enrollment, or death. Risk models were developed using logistic regressions separately for patients with and without prior CVD, and for each outcome: (1) major adverse cardiovascular events (MACE; i.e., non-fatal myocardial infarction, non-fatal stroke, CVD-related death); (2) any MACE, hospitalization for unstable angina, or hospitalization for congestive heart failure; (3) CVD-related death. Models were developed and validated on 70% and 30% of the sample, respectively. Model performance was assessed using C-statistics. RESULTS:A total of 181,619 patients were identified, including 136,544 (75.2%) without prior CVD and 45,075 (24.8%) with a history of CVD. Age, diabetes-related hospitalizations, prior CVD diagnoses and chronic pulmonary disease were the most important predictors across all models. C-statistics ranged from 0.70 to 0.81, indicating that the models performed well. The additional inclusion of risk factors derived from pharmacy claims (e.g., use of antihypertensive, and use of antihyperglycemic) or from medical records and laboratory measures (e.g., hemoglobin A1c, urine albumin to creatinine ratio) only marginally improved the performance of the models. CONCLUSION:The claims-based models developed could reliably predict the risk of cardiovascular events in T2DM patients, without requiring pharmacy claims or laboratory measures. These models could be relevant for providers and payers and help implement approaches to prevent cardiovascular events in high-risk diabetic patients.

Dataset Information

Predictive Value of Plasma Big Endothelin-1 in Adverse Events of Patients With Coronary Artery Restenosis and Diabetes Mellitus: Beyond Traditional and Angiographic Risk Factors.

Background

Methods

Results

Conclusion

Publications

Predictive Value of Plasma Big Endothelin-1 in Adverse Events of Patients With Coronary Artery Restenosis and Diabetes Mellitus: Beyond Traditional and Angiographic Risk Factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets