International Journal of Pediatrics Research
International Journal of Pediatrics Research. 2024; 4: (2) ; 10.12208/j.ijped.20240012 .
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重庆医科大学 重庆
*通讯作者： 周文浩,单位：重庆医科大学 重庆；
抗生素耐药性已成为全球公共卫生的重大威胁，而儿科患者由于免疫系统发育不完善、药物代谢特点特殊，其抗生素合理使用与耐药管理面临严峻挑战。本研究旨在系统分析儿科抗生素使用现状，评估耐药菌流行趋势，并探讨有效的抗菌药物管理策略（Antimicrobial Stewardship Program, ASP），以优化临床实践并减缓耐药性发展。研究采用多中心回顾性分析方法，整合来自不同医疗机构的临床数据，结合人工智能（AI）辅助决策系统，评估抗生素处方合理性及耐药菌感染的危险因素。结果显示，实施ASP可显著降低儿科患者抗生素使用强度（DDD值下降18.7%）和耐药率（如肺炎克雷伯菌对第三代头孢菌素的耐药率降低23.1%）。此外，AI临床决策支持系统（如KINBIOTICS）的应用可提高抗生素选择的准确性，减少不必要的广谱抗生素使用（OR=0.62, 95%CI 0.51-0.75）。研究还发现，重症监护病房（PICU）患儿是多重耐药菌（如CRE、MRSA）感染的高危人群（P<0.01），而规范化的感染防控措施可降低医院获得性耐药菌感染风险达34.5%。本研究的创新点在于结合最新临床数据（2023-2024年）和智能决策技术，提出基于循证医学的儿科抗生素管理优化策略。研究结果提示，未来应加强多学科协作的ASP项目，推广AI辅助决策系统，并针对高危患儿实施精准化感染防控措施，以有效遏制耐药菌的传播。
Rational Use of Antibiotics and Resistance Management Strategies in Pediatrics Antibiotic resistance has become a major global public health threat, and pediatric patients face significant challenges in antibiotic stewardship due to their immature immune systems and unique pharmacokinetics. This study aimed to analyze the current status of antibiotic use in pediatrics, assess the trends of resistant pathogens, and explore effective antimicrobial stewardship strategies to optimize clinical practice and mitigate resistance development.Using a multicenter retrospective analysis combined with AI-based clinical decision support systems (CDSS), we evaluated antibiotic prescribing patterns and risk factors for resistant infections. The results demonstrated that implementing an Antimicrobial Stewardship Program (ASP) significantly reduced antibiotic consumption (18.7% decrease in DDD) and resistance rates (e.g., a 23.1% decline in third-generation cephalosporin resistance in Klebsiella pneumoniae). AI-assisted decision systems (e.g., KINBIOTICS) improved antibiotic selection accuracy and reduced unnecessary broad-spectrum use (OR=0.62, 95%CI 0.51-0.75). Furthermore, pediatric intensive care unit (PICU) patients were at higher risk for multidrug-resistant infections (e.g., CRE, MRSA; P<0.01), while standardized infection control measures reduced hospital-acquired resistant infections by 34.5%.This study integrates the latest clinical data (2023-2024) and AI-driven approaches to propose evidence-based optimization strategies for pediatric antibiotic management. The findings highlight the need for multidisciplinary ASP initiatives, AI-assisted decision-making, and targeted infection prevention measures in high-risk populations to curb the spread of resistant pathogens.
[1] Darby E M, Trampari E, Siasat P, et al. Molecular mechanisms of antibiotic resistance revisited[J]. Nature Reviews Microbiology, 2023, 21(5): 280-295.
[2] Romandini A, Pani A, Schenardi P A, et al. Antibiotic resistance in pediatric infections: global emerging threats, predicting the near future[J]. Antibiotics, 2021, 10(4): 393.
[3] Gerber J S, Jackson M A, Tamma P D, et al. Antibiotic stewardship in pediatrics[J]. Pediatrics, 2021, 147(1): e2020040295.
[4] Sulis G, Sayood S, Gandra S. Antimicrobial resistance in low-and middle-income countries: current status and future directions[J]. Expert review of anti-infective therapy, 2022, 20(2): 147-160.
[5] 张海丽.儿科门诊抗生素不合理用药的研究分析[J].北方药学, 2022(001):019.
[6] Santella B, Serretiello E, De Filippis A, et al. Lower respiratory tract pathogens and their antimicrobial susceptibility pattern: a 5-year study[J]. Antibiotics, 2021, 10(7): 851.
[7] Shen H, FU P, Liu J, et al. The characteristics and changes of bacterial infection and drug resistance in pediatric intensive care unit from 2016 to 2020[J]. Chinese Pediatric Emergency Medicine, 2023: 508-514.
[8] Zhang X, Li Y, Tao Y, et al. Epidemiology and drug resistance of neonatal bloodstream infection pathogens in East China children’s Medical Center from 2016 to 2020[J]. Frontiers in Microbiology, 2022, 13: 820577.
[9] Zhu M, Wang L, Zhuge Z, et al. Risk factors associated with multi-drug resistance in neonatal sepsis caused by Escherichia coli[J]. Infection and Drug Resistance, 2023: 2097-2106.
[10] Markham J L, Burns A, Hall M, et al. Outcomes associated with initial narrow‐spectrum versus broad‐spectrum antibiotics in children hospitalized with urinary tract infections[J]. Journal of hospital medicine, 2024, 19(9): 777-786.
[11] Probst V, Islamovic F, Mirza A. Antimicrobial stewardship program in pediatric medicine[J]. Pediatric investigation, 2021, 5(03): 229-238.
[12] Fu P, Xu H, Jing C, et al. Bacterial epidemiology and antimicrobial resistance profiles in children reported by the ISPED program in China, 2016 to 2020[J]. Microbiology spectrum, 2021, 9(3): e00283-21.
[13] 王双双,李亚玲,陈丽琴,等.儿童重症监护病区CRE感染的危险因素及耐药情况分析[J].昆明医科大学学报,2023,44(09):49-54.
[14] 蔡叶琴,丁娟,周宁,等.医院感染多重耐药菌的监测及防控措施[J].当代医学,2022,28(16):146-148.
[15] 马娟,王翠,苍金荣,等.2016～2020年陕西省临床肺炎克雷伯菌分布及耐药性变迁[J].现代检验医学杂志, 2021, 36(06):132-135+161.
[16] 陈宏杰,欧晓媚.第3代头孢菌素类抗生素临床应用管理分析[J].中国处方药,2020,18(12):72-73.
[17] 高程凤,孙蕊.新生儿败血症致病菌分布及耐药性分析[J].医学信息,2018,31(17):116-118.
[18] 田茂强,龙丽华,任洋,等.88例小儿多重耐药菌临床特点、治疗策略及转归分析[J].中国中西医结合儿科学, 2018, 10(04):349-351.
[19] 岳冬梅,王忻,李崇,等.儿科多重耐药肺炎克雷伯菌的检测及其耐药性分析[J].实用药物与临床,2018,21(06):705-708. DOI: 10.14053/j.cnki.ppcr.201806025.
[20] 张秋萍,叶飞娣,张仲明,等.产超广谱β-内酰胺酶阴沟肠杆菌感染的医院内分布及危险因素分析[J].热带医学杂志,2017,17(11):1527-1531.
[21] 耿凤霞.抗生素管理对抗生素使用强度及细菌耐药性的影响[J].世界最新医学信息文摘,2017,17(40):110-111.
[22] 张阵,诸宏伟,陈娟娟,等.儿童重症监护病房呼吸机相关性肺炎病原菌分布及耐药性分析[J].中国微生态学杂志,2016,28(11):1289-1291.
DOI:10.13381/j.cnki.cjm.201611013.
[23] 段佳佳,朱震宏,季萍.2009—2013年临床常见非发酵菌的分布及耐药性分析[J].中国抗生素杂志, 2015, 40(04): 290-294.DOI:10.13461/j.cnki.cja.005530.
[24] 汤小英,熊小雨,刘成军.儿童泛耐药鲍曼不动杆菌医院获得性肺炎的危险因素分析及防治措施[J].儿科药学杂志,2014,20(06):40-43.
DOI:10.13407/j.cnki.jpp.1672-108x.2014.06.003.
[25] 王蔚,周愚,苏真娇,等.多重耐药大肠埃希氏菌株感染分布及耐药特点分析[J].现代医院,2014,14(03):72-74.
[26] 游学华,王智华,李静梅.某三甲医院切口分泌物细菌分布及其耐药性分析[J].安徽医药,2013,17(11):1927-1929.
[27] 吕婕,陈峰,田青,等.成人和儿科ICU医院感染特点分析及控制策略[J].现代预防医学,2013,40(13):2455-2457.
[28] 火鸿敏,沈忠芳,陈斌.上海市闵行区某基层社区医院抗生素使用现状[J].职业与健康,2013,29(09):1126-1127.DOI:10.13329/j.cnki.zyyjk.2013.09.051.
[29] 张海萍.儿科多重耐药菌感染病人的管理[J].全科护理,2013,11(09):830-831.
[30] 王智华,林庆喜,李静梅,等.我院常见致病菌耐药率与抗菌药物使用现状分析[J].临床军医杂志,2013,41(01):88-90.