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Analysis of Risk Factors and Construction of a Predictive Model for Readmission in Patients with Coronary Slow Flow
Changshun Yan1, Yankai Guo2, Guiqiu Cao1
1Department of Cardiology, Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, The Xinjiang Uygur Autonomous Region, People's Republic of China.
Insights
This study identified key predictors for readmission in patients with coronary slow flow phenomenon (CSFP). A predictive model can help identify at-risk individuals for early intervention.
Area of Science:
- Cardiology
- Clinical Prediction Models
Background:
- Coronary slow flow phenomenon (CSFP) involves delayed distal perfusion without significant coronary stenosis.
- CSFP patients face recurrent hospital readmissions due to chest pain and potential adverse events.
Purpose of the Study:
- To investigate risk factors associated with hospital readmission in CSFP patients.
- To construct a predictive model for early identification of high-risk CSFP patients.
Main Methods:
- A cohort of 397 CSFP patients was analyzed from June 2021 to January 2023.
- Multifactorial logistic regression and nomogram visualization were used to build and validate a predictive model.
- Model performance was assessed using ROC, DCA, and calibration curves.
Main Results:
- Readmission occurred in 34 out of 397 CSFP patients.
- Predictors of readmission included smoking history, creatine kinase-isoenzyme-MB, total cholesterol, and left ventricular ejection fraction.
- The nomogram model demonstrated good predictive ability (AUC=0.87) and clinical utility.
Conclusions:
- A validated prediction model aids in the early detection of CSFP patients at risk of readmission.
- Timely intervention based on model predictions can potentially improve patient outcomes.
Background:
Coronary slow flow phenomenon (CSFP) is a phenomenon in which distal vascular perfusion is delayed on angiography, but coronary arteries are not significantly narrowed and there is no other organic cardiac disease. Patients with CSFP may be repeatedly readmitted to the hospital because of chest pain or other symptoms of precordial discomfort, and there is a risk of adverse events. In order to investigate the risk factors affecting the readmission of CSFP patients, a prediction model was constructed with the aim of identifying patients at risk of readmission at an early stage and providing a reference for further clinical intervention.
Methods:
In this study, we collected clinical data from 397 CSFP patients between June 2021 and January 2023 in Xinjiang Medical University Hospital. Telephone follow-up clarified whether the patients were readmitted to the hospital. A predictive model for readmission of CSFP patients was constructed using multifactorial logistic regression. Nomogram was used to visualize the model and bootstrap was used to internally validate the model. ROC, DCA and Calibration curve were plotted to evaluate the calibration and discriminative ability of the column line graphs, respectively. Calibration and resolution of the column line graphs, respectively.
Results:
A total of 34 of 397 CSFP patients experienced readmission. Smoking history, creatine kinase isoenzyme-MB, total cholesterol, and left ventricular ejection fraction were the predictors of readmission in patients with CSFP. The area under the curve of the Nomogram model was 0.87, which indicated that the model had good predictive ability and differentiation, and the DCA and Calibration curves also indicated that the model had good consistency and was clinically useful.
Conclusion:
A readmission prediction model for patients with CSFP may facilitate early identification of patients at potential risk for readmission and timely interventional therapy to improve patient prognosis.
