Correlation analysis between the detection rate of CR-KP and the utilization of antibacterial drugs based on PLSR algorithm

Abstract

Abstract:

AIM: To provide guidance for the reasonable clinical usage of antibacterial drugs by employing the partial least squares regression (PLSR) algorithm to analyze the correlation between the detection rate of carbapenem-resistant Klebsiella pneumoniae (CR-KP) and the utilization of antibacterial drugs. METHODS: Different PLSR models were established with the defined daily doses (DDDs) of antibacterial drugs in different quarters as independent variables and the detection rate of CR-KP with same and different periods (lagged 1-4 quarters) as dependent variables. The lag time between the detection rate of CR-KP and the utilization of antibacterial drugs was investigated. The antibacterial drugs, which were highly correlated to the detection rate of CR-KP were screened. The influences of the antibacterial drugs on the detection rate of CR-KP were then investigated according to the regression coefficients. RESULTS:The detection rate of CR-KP lagged about two quarters behind the utilization of antibacterial drugs. It was significantly positively correlated with the DDDs of amoxicillin-clavulanic acid and sulbenicillin, while negatively correlated with the DDDs of itraconazole. The regression coefficients of amoxicillin-clavulanic acid, itraconazole and sulbenicillin were 0.085 2, -0.083 9 and 0.076 3, respectively. CONCLUSION: It is effective to employ PLSR algorithm to simultaneously analyze the correlation between the detection rate of CR-KP and the utilization of multiple antibacterial drugs, which provides a new scientific tool for controlling the detection rate of CR-KP.

Key words: partial least squares regression, carbapenem-resistant Klebsiella pneumoniae, drug resistance, antibacterial drugs, defined daily doses

CLC Number:

R311

JIANG Cheng, LI Fangqiong, YE Zuowu, SUN Yunfeng, ZHANG Meiling, LI Gonghua . Correlation analysis between the detection rate of CR-KP and the utilization of antibacterial drugs based on PLSR algorithm[J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2017, 22(8): 904-909.

References

［1］徐安, 卓超, 苏丹虹, 等. 2005-2014年CHINET克雷伯菌属细菌耐药性监测［J］. 中国感染与化疗杂志, 2016, 16(3): 267-274.
［2］陈帆, 张艳亭, 乔慧捷, 等. 肺炎克雷伯菌肝脓肿的临床特征分析［J］. 临床肝胆病杂志, 2016, 32(4): 764-768.
［3］全国细菌耐药监测网. 2015年全国细菌耐药监测报告（简要版）［R/OL］.［2015-12-26］. http://www.carss.cn/Download/Details/282.
［4］胡付品, 朱德妹, 汪复, 等. 2014年CHINET中国细菌耐药性监测［J］. 中国感染与化疗杂志, 2015, 15(5): 401-410.
［5］肖永红. 临床抗菌药物学［M］. 重庆: 重庆出版社, 2004: 129.
［6］蒋程, 吕健刚, 韩冰, 等. 抗菌药物使用的多变量监控［J］. 中国现代应用药学, 2015, 32(3): 370-375.
［7］Chan CO, Chu CC, Mok DK, et al. Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection［J］. Anal Chim Acta, 2007, 592(2): 121-131.
［8］段金菊, 尹冬虹, 何志强, 等. 不同统计方法对1998-2010年鲍曼不动杆菌耐药率与抗菌药物使用相关性研究［J］. 中国执业药师, 2011, 8(6): 33-38.
［9］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(1):19-23.
［10］蒋程, 吕健刚, 李芳琼, 等. 克林霉素耐药金黄色葡萄球菌检出率与抗菌药物使用的相关性分析［J］. 中国临床药理学与治疗学, 2017,22(4):439-442.
［11］叶莺, 陈崇帼, 林熙. 偏最小二乘回归的原理及应用［J］. 海峡预防医学杂志, 2005, 11(3): 3-6.
［12］秦浩, 林志娟, 陈景武. 偏最小二乘回归原理、分析步骤及程序［J］. 数理医药学杂志, 2007, 20(4): 450-451.
［13］Huang C, Tu S, Huang C, et al. Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy［J］. Biomed Res Int, 2013, 2(1): 1-9.
［14］Jiang C, Liu Y, Qu HB. Data fusion strategy based on near infrared spectra and ultraviolet spectra for simultaneous determination of ginsenosides and saccharides in Chinese herbal injection［J］. Anal Methods, 2013, 5(17): 4467-4475.
［15］Han H, Zhang L, Zhang Y, et al. Rapid analysis of the in-process extract solutions of compound E Jiao oral liquid using near infrared spectroscopy and partial least-squares regression［J］. Anal Methods, 2013, 5(19): 5272-5278.
［16］于勇. 病原菌耐药与抗菌药物使用的宏观量化关系［J］. 微生物学通报, 2006, 33(4): 186-189.
［17］忻志鸣, 叶根深, 牛洪敏. 252株肺炎克雷伯菌耐药率与抗菌药物使用强度相关性分析［J］. 中国药业, 2014, 23(22): 82-84.
［18］黄健, 李珂佳, 曾海萍, 等. 某院近10年革兰阴性杆菌耐药率与抗菌药物使用相关性分析［J］. 中国新药杂志, 2015, 24(1): 112-117.
［19］蔡惠惠, 王萍, 赵水娣, 等. 我院抗菌药使用与葡萄球菌耐药率变化的相关性分析［J］. 药物流行病学杂志, 2015, 24(1): 28-32.
［20］谢少玲, 李平, 刘永刚, 等. 抗菌药物使用强度与金黄色葡萄球菌耐药率的相关性分析［J］. 解放军药学学报, 2015, 31(2): 167-169.
［21］李园园, 葛卫红, 于锋. 本院2008-2012年ICU病原菌分布特点及耐药率与哌拉西林/他唑巴坦使用强度相关性分析［J］. 中国临床药理学与治疗学, 2014, 19(5): 561-566.
［22］谢多双, 胡荍, 明星, 等. 铜绿假单胞菌耐药率与抗菌药物使用强度的相关性研究［J］. 中国感染控制杂志, 2014, 13(11): 646-649.

[1]	DANG Zilong, LIU Haiyang, ZHANG Yangfan, LIU Jinfeng, ZHANG Tianwei, LI Bin, GUO Hong, CHENG Wenyuan, XU Xiaohui, LI Yile, WEI Yuhui, WU Xin'an. Optimization strategy of antibiotic dosing regimen in intensive care unit patients with augmented renal clearance [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2023, 28(5): 561-571.
[2]	LI Shan, LIU Yixin, REN Feifei, LI Xiangchen, ZHANG Zhiqing. Research progress of natural plant active ingredients in reversing tumor multidrug resistance by down-regulating P-gp [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2023, 28(3): 331-340.
[3]	LIU Jiajia, GAO Yan, ZHANG Yehui, WU Jing. Functions of CRABP2 in tumorigenesis and progression [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2023, 28(1): 86-92.
[4]	DONG Wei, HUANG Xiaoying, XIE Bingbin, TANG Xilan, LI Hongming, QIU Yumei, ZHAO Guowei, LIANG Xinli. Effects of oxypeucedanin on the resistance of breast cancer MCF-7/DOX cells to doxorubicin [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2022, 27(3): 260-266.
[5]	MENG Qiang, LIU Kexin. Research status and prospect of transporter-mediated drug-drug interactions [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2021, 26(8): 876-888.
[6]	YANG Hechun, SHI Daohua . Research progress of small molecule inhibitors that reverse tumor drug resistance by regulating tumor glucose metabolism [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2021, 26(7): 836-840.
[7]	WEI Songchang, YANG Hongqi, ZHANG Hongjuan, SHI Xiaoli, REN Kaiwen. Correlation between the expression of RON protein and CXC chemokine receptor 4 protein and abiraterone resistance in patients with castration-resistant prostate cancer [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2021, 26(4): 414-422.
[8]	JIN Le, JIANG Duochen, CHEN Hongxiao, LIU Su, CHEN Zhaolin, JU Jing. Quercetin reverses adriamycin resistance by activating GSK-3β/β-catenin pathways through GAS5 in breast cancer cells [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2021, 26(12): 1344-1351.
[9]	WANG Ziyuan, SUN Mingyu. Advances in research on long non-coding RNA in drug resistance of colorectal cancer [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2020, 25(7): 796-802.
[10]	YANG Yanlang, LI Yan, WANG Lin. Distribution and drug resistance of bacterial pathogens in the infecting inpatients of department of nephrology [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2020, 25(2): 203-208.
[11]	XU Weiping, ZHOU Weiying. Advances in the sensitization mechanism of chemotherapeutic drugs for bladder cancer [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2019, 24(9): 1060-1064.
[12]	WANG Xiaoli, LU Shuping, DAI Jiale. Role of MSX2 in melanoma cells for trametinib resistance [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2019, 24(10): 1147-1154.
[13]	FU Peishu, SUN Fengjun, FENG Wei, XIONG Lirong, LI Xiaoyu, XIA Peiyuan. Progress in drug therapy for multidrug-resistant Acinetobacter baumannii infection [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2019, 24(10): 1181-1187.
[14]	LI Zhaoxia，WANG Rui，LI Aimin，LIU Youning. Study of the primary target and mutation position in nemonoxacin-resistant mutants of Staphlococcus aureus [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2018, 23(6): 667-671.
[15]	YE Haizhu, CHEN Yajuan, ZHAO Wenying, LIU Xiaoping. Different expression of FoxO3/Keap1/Nrf2 pathway in tumor cells and drug resistant tumor cells [J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2017, 22(8): 859-865.