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目的采用偏最小二乘(PLS)回归算法分析耐甲氧西林金黄色葡萄球菌(MRSA)检出率与抗菌药物使用的相关性,为指导抗菌药物的合理使用提供依据。方法以抗菌药物的用药频度(DDDs)为自变量,以同期及滞后不同时期MRSA的检出率为因变量,建立PLS回归模型。根据各抗菌药物的回归系数,筛选与MRSA检出率相关性较显著的品种,明确不同品种对MRSA检出率的贡献度。结果 MRSA的检出率与头孢地秦、克拉霉素的DDDs呈显著正相关,与氟康唑的DDDs呈显著负相关。头孢地秦、克拉霉素、氟康唑的PLS回归系数分别为0.119、0.119、-0.115。结论本研究证明了PLS回归算法在MRSA检出率与抗菌药物使用相关性研究中的有效性,可为病原菌耐药性研究提供新的思路。
Abstract：AIM To provide information for reasonable application of antibacterial drugs,the partial least squares( PLS) regression algorithm was applied to investigate the correlation between the detection rate of methicillin-resistant Staphylococcus aureus( MRSA) and utilization of antibacterial drugs. METHODS The PLS regression models were established with the defined daily doses( DDDs) of antibacterial drugs as independent variables and the detection rates of MRSA with same and different periods as dependent variables. The antibacterial drugs which were highly correlated to the detection rate of MRSA were screened. And the contribution of these drugs was calculated according to the regression coefficients. RESULTS The detection rate of MRSA was significantly positively correlated with the DDDs of cefodizime and clarithromycin,while negatively correlated with the DDDs of fluconazole. The regression coefficients of cefodizime,clarithromycin and fluconazole were 0. 119,0. 119 and-0. 115,respectively. CONCLUSION This case study demonstrates that it is effective to employ PLS regression algorithm to analyze the correlation between detection rate of MRSA and utilization of antibacterial drugs. This research provides a new way for investigating the drug resistance of pathogenic bacteria.
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基本信息:
DOI：10.19577/j.cnki.issn10074406.2017.01.005
中图分类号:R446.5
引用信息:
[1]蒋程,吕健刚,徐金波,等.基于PLS回归算法的MRSA检出率与抗菌药物使用相关性分析[J].中国临床药学杂志,2017,26(01):19-23.DOI:10.19577/j.cnki.issn10074406.2017.01.005.
基金信息:
浙江省公益技术应用研究计划(2016C33127); 浙江省药学会医院药学专项科研(编号2014ZYY10); 浙江省中医药优秀青年人才基金(编号2015ZQ007)
2016-01-08
2016
2016-05-26
2016
2016-04-27
1
2017-01-25
2017-01-25