Coronary Artery Disease, Elderly (Aged >70)
Conditions
Keywords
coronary computed tomography angiography, frailty, biomarker
Brief summary
This study is a prospective multi-center observational cohort involving elderly patients (≥70 years old) with multi-vessel coronary disease identified by coronary computed tomography angiography (cCTA) and scheduled for invasive coronary angiography. Data collection about cCTA, frailty and biomarkers and two-year follow-up will be conducted to develop and validate a risk model of coronary revascularization.
Interventions
OTHERNo Intervention: Observational Cohort
No intervention will be conducted.
Sponsors
Central China Fuwai Hospital of Zhengzhou University
Chinese Academy of Medical Sciences, Fuwai Hospital
China National Center for Cardiovascular Diseases
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
70 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥ 70 years. 2. Multivessel coronary artery disease identified on coronary computed tomography angiography (CCTA), defined as ≥50% stenosis in at least two of the three major epicardial coronary arteries. 3. Scheduled to undergo invasive coronary angiography as part of standard clinical care. 4. Able to provide informed consent, or consent provided by a legally authorized representative.
Exclusion criteria
1. Previous coronary revascularization, including percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). 2. Acute myocardial infarction at admission, according to electrocardiographic findings and elevated cardiac biomarkers consistent with acute MI. 3. Poor-quality CCTA images that prevent accurate assessment of coronary plaque characteristics or stenosis severity. 4. Refusal or inability to participate in the study or to provide informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) for Risk Model Performance | At completion of 2-year follow-up | AUC evaluates the model's discrimination performance in predicting MACCEs at 24 months. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Specificity of the Prediction Model | At completion of 2-year follow-up | The proportion of participants who did not experience MACCEs that were correctly identified by the prediction model as low risk. |
| Positive Predictive Value (PPV) | At completion of 2-year follow-up | The probability that subjects identified by the model as high risk truly experienced the adverse events (MACCEs). |
| Negative Predictive Value (NPV) | At completion of 2-year follow-up | The probability that subjects identified by the model as low risk truly did not experience the adverse events. |
| Sensitivity of the Prediction Model | At completion of 2-year follow-up | The proportion of participants who actually experienced MACCEs (Major Adverse Cardiac and Cerebral Events) that were correctly identified by the prediction model as high risk. |
| Calibration Slope | At completion of 2-year follow-up | Assessment of the agreement between observed outcomes and predicted risks. A slope (beta) of 1 indicates ideal calibration. |
| Decision Curve Analysis (DCA) | At completion of 2-year follow-up | Evaluation of clinical net benefit across a range of threshold probabilities. |
| Hosmer-Lemeshow Goodness-of-Fit Statistic | At completion of 2-year follow-up | A statistical test to assess the calibration of the model. It evaluates whether the observed event rates match the expected event rates in subgroups of the model population. A non-significant p-value indicates good fit. |
Contacts
Primary ContactZhe Zheng, MD, PhD
Outcome results
None listed