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.

Keywords: percutaneous coronary intervention, 30-day readmission, nomogram, prediction model


中文翻译：

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预测 PCI 后 30 天意外再入院的风险：新预测列线图的开发和验证

目的：本研究旨在开发和验证一种风险预测模型，该模型可用于识别 30 天计划外再入院高风险的经皮冠状动脉介入治疗 (PCI) 患者。
患者和方法：我们基于 1348 名 PCI 患者的训练数据集开发了一个预测模型。数据收集时间为 2020 年 1 月至 2020 年 12 月。使用医院数据库收集临床特征、实验室数据和危险因素。应用 LASSO 回归方法过滤变量和选择预测变量，并使用最小绝对收缩优化 30 天再入院风险模型的特征选择。多元逻辑回归用于构建列线图。列线图的性能和临床效用通过受试者工作特征 (ROC) 曲线、校准曲线和决策曲线分析 (DCA) 进行评估。使用自举验证进行预测准确性的内部验证。
结果：预测列线图中包括的预测因素是医疗保险、住院时间、入院时左心室射血分数、高血压病史、慢性肺病的存在、贫血的存在和入院时的血清肌酐水平。预测模型的受试者工作特征曲线下面积为 0.735（95% CI：0.711–0.759）。Hosmer-Lemeshow 拟合优度检验的 P 值为 0.326，表明校准良好，校准曲线显示分类与实际观察结果吻合良好。DCA 还证明列线图在临床上是有用的。在内部验证期间获得了 0.723 的高 c-index 值。
结论：我们开发了一个易于使用的列线图模型来预测 PCI 患者出院后 30 天再入院的风险。该风险预测模型可作为高危患者出院时筛查和PCI患者资源分配的指南，可为预防性护理干预提供参考。

关键词：经皮冠状动脉介入治疗，30天再入院，列线图，预测模型