抗生素耐药性时间序列分析与相关性研究
Journal: Basic Medical Theory Research DOI: 10.12238/bmtr.v8i2.18882
Abstract
目的：抗生素耐药性是全球公共卫生的重大挑战，理解其动态变化及相关性对临床用药和药物控制至关重要。方法：本研究基于血流感染细菌检测联盟（BRICS）数据库的金黄色葡萄球菌药敏结果（2017 年-2021 年），选取 14 种常用抗菌药物的药敏数据，采用时间序列分解（加法模型）和 ARIMA 建模分析其变化趋势、季节性和残差特征，并对比 HWES 和 ARIMA 算法的预测性能。通过 Pearson 相关系数和层次聚类分析探究抗生素间耐药性之间的关联关系。结果：研究的 14 种抗菌素中 OXA（苯唑西林）、CIP（环丙沙星）等 7 种抗生素的耐药性呈高度相关（r>0.8），聚类分析归为一类；SXT（复方新诺明）与其他药物耐药性差异显著（r<0.2），独立成类；ARIMA 模型预测性能优于 HWES（如 OXA的RMSE=4.123 优于4.203，且经95% 置信区间 3.87-4.36 及 Diebold-Mariano 检验 P=0.032 验证）。结论：本研究的结果为临床联合用药策略和耐药机制研究提供了数据支持。
Keywords
抗生素耐药性；时间序列分析；ARIMA；相关性分析；层次聚类
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