我国常见细菌耐药趋势预测研究:基于灰色GM (1,1)模型

 目的 通过对耐甲氧西林金黄色葡萄球菌（MRSA）、耐碳青霉烯类铜绿假单胞菌（CRPA）、耐碳青霉烯类鲍曼不动杆菌（CRAB）、耐第三代头孢菌素的大肠埃希菌（3GCR-E.coli）、耐第三代头孢菌素的肺炎克雷伯菌（3GCR-KP）等细菌耐药数据构建灰色预测模型，分析细菌耐药特征的变化趋势，探讨灰色预测模型在细菌耐药领域的应用价值。方法 采用2014-2018年全国细菌耐药监测报告中MRSA、CRPA和CRAB、3GCR-E.coli、3GCR-KP等耐药率数据构建灰色预测GM （1，1）模型。用后验差比C值和小误差概率P值评估模型精度，用相对误差和级比偏差评估模型拟合效果，并用2019-2020年数据对模型预测效果进行验证。最终根据模型对2021-2023年的耐药率进行预测。结果 本研究构建的GM （1，1）模型对MRSA、CRPA、CRAB、3GCR-E.coli和3GCR-KP等细菌耐药率预测效果较好，根据该模型预测到2023年其耐药率分别可降低至23.9%、15.2%、50.2%、43.8%、26.1%。结论 全国针对细菌耐药情况采取的控制措施取得明显成效，GM （1，1）模型对细菌耐药率预测效果较好，可在细菌耐药管理领域推广应用。

Prediction of drug resistance trends of common bacteria in China based on Grey Prediction Model GM(1,1)

Objective To construct a grey prediction model based on bacterial drug resistance data of methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB), third-generation cephalosporin-resistant Escherichia coli (3GCR-E. coli), and third-generation cephalosporin-resistant Klebsiella pneumoniae (3GCR-KP), analyze the trends in bacterial drug resistance characteristics, and explore the application value of grey prediction model in the field of bacterial drug resistance. Methods A grey prediction model GM(1,1) was constructed based on drug resistance rate data of MRSA, CRPA, CRAB, 3GCR-E. coli, and 3GCR-KP from the national bacterial drug resistance surveillance reports in 2014-2018. The precision of the model was assessed with posterior error ratio (C) and the small error probability (P). The fitting effectiveness of the model was evaluated with relative error and grade ratio deviation. The prediction effectiveness of the model was verified with the data from 2019 to 2020. Final prediction of drug resistance rates from 2021 to 2023 was made based on the constructed model. Results The GM (1,1) model constructed in this study has good prediction effectiveness on drug resistance rates of MRSA, CRPA, CRAB, 3GCR-E. coli and 3GCR-KP. According to this model, resistance rates of the above bacteria were predicted to be reduced to 23.9%, 15.2%, 50.2%, 43.8%, and 26.1% respectively by 2023. Conclusion The control measures taken against bacterial drug resistance in China have achieved remarkable results. GM (1,1) model is effective in predicting bacterial drug resistance rate and can be promoted for the application in the field of bacterial drug resistance management.