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Predicting the Risk of Unplanned Readmission at 30 Days After PCI: Development and Validation of a New Predictive
Wenjun Xu1,2, Hui Tu1, Xiaoyun Xiong1
1Department of Nursing, the Second Affiliated Hospital of Nanchang University, NanChang, Jiangxi, 330000, People's Republic of China.
Insights
This study developed a nomogram to predict 30-day unplanned readmissions in percutaneous coronary intervention (PCI) patients. The model identifies high-risk individuals, aiding resource allocation and preventive care for PCI survivors.
Area of Science:
- Cardiology
- Health Services Research
- Medical Informatics
Background:
- Unplanned readmissions after percutaneous coronary intervention (PCI) increase healthcare costs and impact patient outcomes.
- Identifying patients at high risk for readmission is crucial for effective resource allocation and targeted interventions.
Purpose of the Study:
- To develop and validate a user-friendly risk prediction model for 30-day unplanned readmissions in patients undergoing PCI.
- To identify key clinical predictors associated with readmission risk post-PCI.
Main Methods:
- A predictive model was developed using a training dataset of 1348 PCI patients from January to December 2020.
- LASSO regression was employed for variable selection, followed by multivariate logistic regression to construct a nomogram.
- Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), with internal validation via bootstrapping.
Main Results:
- The final nomogram included predictors such as medical insurance, length of stay, left ventricular ejection fraction, hypertension history, chronic lung disease, anemia, and serum creatinine.
- The model demonstrated good predictive accuracy with an area under the ROC curve of 0.735 and a c-index of 0.723.
- Calibration analysis confirmed good agreement between predicted and observed readmission risks, and DCA indicated clinical utility.
Conclusions:
- An accessible nomogram was created to predict 30-day readmission risk in PCI patients.
- This tool can guide the screening of high-risk patients, optimize resource allocation, and inform preventive strategies for PCI patients post-discharge.
Objective:
This study aimed to develop and validate a risk prediction model that can be used to identify percutaneous coronary intervention (PCI) patients at high risk for 30-day unplanned readmission.
Patients And Methods:
We developed a prediction model based on a training dataset of 1348 patients after PCI. The data were collected from January 2020 to December 2020. Clinical characteristics, laboratory data and risk factors were collected using the hospital database. The LASSO regression method was applied to filter variables and select predictors, and feature selection for a 30-day readmission risk model was optimized using least absolute shrinkage. Multivariate logistic regression was used to construct a nomogram. The performance and clinical utility of the nomogram were evaluated with a receiver operating characteristic (ROC) curve, a calibration curve, and decision curve analysis (DCA). Internal validation of the predictive accuracy was performed using bootstrapping validation.
Results:
The predictors included in the prediction nomogram were medical insurance, length of stay, left ventricular ejection fraction on admission, history of hypertension, the presence of chronic lung disease, the presence of anemia, and serum creatinine level on admission. The area under the receiver operating characteristic curve for the predictive model was 0.735 (95% CI: 0.711-0.759). The P value of the Hosmer-Lemeshow goodness of fit test was 0.326, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA also demonstrated that the nomogram was clinically useful. A high c-index value of 0.723 was obtained during the internal validation.
Conclusion:
We developed an easy-to-use nomogram model to predict the risk of readmission 30 days after discharge for PCI patients. This risk prediction model may serve as a guide for screening high-risk patients and allocating resources for PCI patients at the time of hospital discharge and may provide a reference for preventive care interventions.
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