基于多元回归-灰色模型的非小细胞肺癌靶向药物用量预测分析

目的建立非小细胞肺癌(NSCLC)常用靶向药物的用量预测模型,为指导医疗机构抗肿瘤靶向药物的采购和库存管理提供数据支撑。方法参考天津市肿瘤医院2019年各月的靶向药物用量数据,以4种临床常用的NSCLC靶向药物(吉非替尼、埃克替尼、奥希替尼和克唑替尼)为例,建立多元回归模型、GM(1,1)灰色模型以及多元回归-灰色组合模型,并对3种预测模型进行评价和验证。结果多元回归模型在描述靶向药物用量的波动变化方面具有优势,灰色模型可以更好地描述靶向药物用量的增长趋势,而组合模型兼备描述用量变化趋势和短期波动的能力。吉非替尼、埃克替尼、奥希替尼和克唑替尼4种靶向药物运用组合模型得到的的预测值与实际值误差分别为4.30%、2.87%、3.62%、4.42%。结论多元回归-灰色组合模型运行良好,与单一模型相比表现出更高的精准度,可应用于医疗机构靶向药物的用量预测,从而实现靶向药物的精准化管理。

Prediction of the usage of targeted drugs for non-small cell lung cancer based on multiple regression-gray model

Objective To establish a prediction model of the commonly used targeted drugs for non-small cell lung cancer (NSCLC), providing data support for the procurement and management of targeted anti-tumor drugs in medical institutions. Methods The multiple regression model, gray model and the combined model were established with four commonly used targeted drugs for non-small cell lung cancer (gefitinib, icotinib, osimertinib and crizotinib), based on monthly usage of Tianjin Medical University Cancer Institute and Hospital in 2019, and the prediction models were evaluated and verified. Results The advantage of the multiple regression model is to describe the fluctuation of the usage of the targeted drugs. The gray model can reflect the increasing trend, while the combined model has the characteristics of describing the increasing trend and short-term fluctuations. The error between the predicted value and the actual value of the combined prediction model for gefitinib, icotinib, osimertinib and crizotinib was 4.30%, 2.87%, 3.62% and 4.42%, respectively. Conclusion Compared with the single model, the multiple regression-gray combined prediction model works better and shows higher accuracy. It can be applied to the prediction of the usage of targeted drugs in medical institutions, which is conducive to the accurate management for targeted drugs.