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Establishing a predictive model for aspirin resistance in elderly Chinese patients with chronic cardiovascular
Jian Cao1, Wei-Jun Hao2, Ling-Gen Gao1
1Department of Geriatric Cardiology, Chinese PLA General Hospital, Beijing, China.
Insights
This study developed a predictive model to identify patients resistant to aspirin therapy. The model uses seven risk factors to help clinicians manage anti-platelet treatment effectively.
Area of Science:
- Cardiology
- Pharmacology
- Clinical Risk Prediction
Background:
- Anti-platelet therapy is crucial for cardiovascular disease management.
- Resistance to aspirin poses significant risks to patient outcomes.
- Identifying patients at risk of aspirin resistance is essential for personalized treatment.
Purpose of the Study:
- To develop and validate a predictive model for aspirin resistance.
- To identify key risk factors associated with aspirin resistance.
- To provide a tool for stratifying patients undergoing anti-platelet therapy.
Main Methods:
- Logistic regression analysis was used to build the predictive model.
- A risk score was developed based on significant variables.
- Model calibration and discrimination were assessed using the Hosmer-Lemeshow test and ROC curves.
Main Results:
- Seven risk factors were identified: elevated serum creatinine, fasting blood glucose, hyperlipidemia, number of coronary arteries, body mass index, history of percutaneous coronary intervention, and smoking.
- The predictive model demonstrated good calibration (P ≥ 0.05) and discrimination (AUC ≥ 0.70).
Conclusions:
- A validated predictive model for aspirin resistance has been established.
- The model effectively quantifies risk factors for aspirin resistance.
- This tool can support clinical decision-making for patients on anti-platelet therapy.
Background:
Resistance to anti-platelet therapy is detrimental to patients. Our aim was to establish a predictive model for aspirin resistance to identify high-risk patients and to propose appropriate intervention.
Methods:
Elderly patients (n = 1130) with stable chronic coronary heart disease who were taking aspirin (75 mg) for > 2 months were included. Details of their basic characteristics, laboratory test results, and medications were collected. Logistic regression analysis was performed to establish a predictive model for aspirin resistance. Risk score was finally established according to coefficient B and type of variables in logistic regression. The Hosmer-Lemeshow (HL) test and receiver operating characteristic curves were performed to respectively test the calibration and discrimination of the model.
Results:
Seven risk factors were included in our risk score. They were serum creatinine (> 110 μmol/L, score of 1); fasting blood glucose (> 7.0 mmol/L, score of 1); hyperlipidemia (score of 1); number of coronary arteries (2 branches, score of 2; ≥ 3 branches, score of 4); body mass index (20-25 kg/m(2), score of 2; > 25 kg/m(2), score of 4); percutaneous coronary intervention (score of 2); and smoking (score of 3). The HL test showed P ≥ 0.05 and area under the receiver operating characteristic curve ≥ 0.70.
Conclusions:
We explored and quantified the risk factors for aspirin resistance. Our predictive model showed good calibration and discriminative power and therefore a good foundation for the further study of patients undergoing anti-platelet therapy.
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