环孢素血药浓度超限列线图预测模型的构建与验证

目的 构建并验证环孢素血药浓度超限列线图预测模型,筛选血药浓度超限的高危人群,提高环孢素治疗的安全性。方法 回顾性分析2019年10月-2022年10月西安国际医学中心医院服用环孢素并接受血药浓度监测的207例住院患者的临床资料,采用Logistic回归方程分析环孢素血药浓度超限的影响因素,构建列线图预测模型,并对该模型进行评估。结果 日剂量、合用唑类抗真菌药是环孢素血药浓度低于正常值下限的独立影响因素;日剂量、肾功能不全、合用达那唑、合用糖皮质激素、合用唑类抗真菌药是环孢素血药浓度超出正常值上限的独立影响因素。根据所筛选出的5项环孢素血药浓度超出正常值上限的独立影响因素,建立了环孢素血药浓度超限的列线图预测模型,该模型的C-index指数为0.841,表明该列线图模型具有较好的区分度,对该模型进行验证后发现预测值和观察值基本一致,表明该列线图预测模型具有较好的一致性。结论 构建的环孢素血药浓度超限列线图预测模型有良好的区分度及一致性,具有较高的临床应用价值,可甄别易发生环孢素血药浓度超限的高危人群,有目的性地开展血药浓度监测,提高药物治疗安全性。

Construction and verification of the prediction model of blood concentration overrun of cyclosporine by nomogram

Objective To construct and verify the prediction model of the blood concentration of cyclosporine exceeding the limit, screen the high-risk groups of the blood concentration exceeding the limit, and improve the safety of the treatment of cyclosporine. Methods The clinical data of 207 inpatients who took cyclosporine and received blood concentration monitoring in Xi''an International Medical Center Hospital from October 2019 to October 2022 were retrospectively analyzed. The influencing factors of excessive blood concentration of cyclosporine were analyzed by using Logistic regression equation, and the prediction model was built and evaluated. Results Daily dose and the combination of azole antifungal drugs were the independent factors influencing the blood concentration of cyclosporine below the lower limit of normal value. Daily dose, renal insufficiency, combination of danazol, glucocorticoid, and azole antifungal drugs were independent factors influencing the concentration of cyclosporine beyond the upper limit of normal value. Based on the five selected independent factors affecting the concentration of cyclosporine exceeding the upper limit of normal value, a column-line prediction model for the concentration of cyclosporine exceeding the upper limit of normal value was established. The C-index of the model was 0.841, indicating that the column-line model had a good degree of differentiation. After verifying the model, it was found that the predicted value was basically consistent with the observed value. The results show that the prediction model has good consistency. Conclusion The prediction model built in this study has good differentiation and consistency, and has high clinical application value, which can identify high-risk groups prone to excessive blood concentration of cyclosporine, carry out purposeful blood concentration monitoring, and improve the safety of drug treatment.