Preventing antimicrobial resistance and infections in hospitalized neonates in low resource settings

项目摘要/摘要 每年，有250万婴儿在出生后的前四周内死亡，其中近四分之一死于感染原因。 由于以下因素，住进新生儿重症监护病房(NICU)的新生儿特别脆弱 早产、免疫系统不成熟以及需要维持生命的侵入性程序和设备。处于低位 在中等收入国家(LMIC)，越来越多的NICU照顾早产儿和危重病人 新生儿。医疗保健相关血流感染(HA-BSI)在LMIC中更常见，原因是 感染预防和控制(IPC)，由于抗菌素耐药率高而更难治疗 (AMR)。以前在这一背景下的研究主要集中在疫情调查上，并没有充分 描述HA-BSI的风险因素。医疗机构缺乏评估孕产妇和新生儿IPC的有效工具 并制定改进策略。申请者正在进行的前瞻性队列研究的初步数据 在印度浦那的三个NICU中登记了超过6600名新生儿，加强了这个地区HA-BSI的高发病率 设置为每1000个病例日7.6次，以及高AMR率。肺炎克雷伯菌 最常见的BSI病原体，96%对第三代头孢菌素耐药，38%对 碳青霉烯类。在患有BSI的新生儿中，死亡率为22%。在这项研究的框架内，以下是 建议：(1)确定NICU中HA-BSI的可改变的危险因素；(2)开发预测模型 碳青霉烯耐药菌(CRO)感染；以及(3)开发和试验一种新的工具来评估IPC 新生儿重症监护室和分娩的实践。识别NICU中HA-BSI的危险因素将促进 制定有针对性的IPC战略。使用决策树算法创建预测模型将有所帮助 确定CRO感染风险最高的婴儿。这样的模式可以支持NICU临床医生选择合适的 怀疑感染时使用抗生素，缩短适当治疗的时间并减少不必要的使用 作为最后手段的抗生素，如粘菌素。开发纳入人的因素的IPC评估工具 工程(HFE)原则将使医疗机构能够优化IPC并降低医院收购的风险 感染和相关死亡率。这项有指导的研究将对申请人进行高级流行病学方面的培训。 IPC方法及其在LMIC中的应用。申请人是约翰·霍普金斯大学的一名新生儿专家。 在资源有限的情况下进行以患者为中心的研究。她的长期目标是成为新生儿领域的领导者 在低资源环境下进行IPC，并制定干预措施，以减轻全球HA-BSI和相关 死亡率。K23将促进纵向数据分析、预测模型、调查方面的技能发展 开发、HFE和定性数据分析。培训将包括正式的课程作业、监督数据 由一个在传染病、IPC、生物统计学、流行病学、患者方面拥有专业知识的团队进行分析和指导 安全性和HFE。总的来说，K23的活动将提供一条通往独立职业生涯的道路 具有医疗保健流行病学和低资源环境下的IPC专业知识的临床调查员。

Project Summary/Abstract Annually, 2.5 million babies die within the first four weeks of life, nearly a quarter due to infectious causes. Newborns admitted to the Neonatal Intensive Care Unit (NICU) are especially vulnerable, due to such factors as prematurity, an immature immune system, and need for life-sustaining invasive procedures and devices. In low and middle income countries (LMIC), an increasing number of NICUs care for premature and critically ill newborns. Healthcare-associated bloodstream infections (HA-BSI) in LMIC are more common due to inadequate infection prevention and control (IPC) and more difficult to treat due to high rates of antimicrobial resistance (AMR). Previous research in this setting focuses primarily on outbreak investigations and does not adequately describe risk factors for HA-BSI. Healthcare facilities lack effective tools to assess maternal and neonatal IPC and create improvement strategies. Preliminary data from the applicant's ongoing prospective cohort study that has enrolled over 6600 neonates in three NICUs in Pune, India, reinforces the high incidence of HA-BSI in this setting with a rate of 7.6 per 1000 patient-days, as well as high rates of AMR. Among Klebsiella pneumoniae isolates, the most common BSI pathogen, 96% are resistant to third-generation cephalosporins and 38% to carbapenems. Among neonates with BSI, mortality is 22%. Within the framework of this study, the following are proposed: (1) To identify modifiable risk factors for HA-BSI in the NICU; (2) To develop a model for predicting infection with carbapenem-resistant organisms (CRO); and (3) To develop and pilot a novel tool to assess IPC practices in the NICU and Labor & Delivery. Identifying risk factors for HA-BSI in the NICU will promote development of targeted IPC strategies. Creation of a prediction model using a decision tree algorithm will help identify babies at highest risk of CRO infections. Such a model can support NICU clinicians in selecting the right antibiotics when infection is suspected, reducing time to appropriate therapy and decreasing unnecessary use of last resort antibiotics such as colistin. Development of an IPC assessment tool that incorporates human factors engineering (HFE) principles will enable healthcare facilities to optimize IPC and reduce risk of hospital-acquired infections and associated mortality. This mentored research will train the applicant in advanced epidemiologic methods and application of IPC in LMIC. The applicant is a neonatologist at Johns Hopkins University committed to patient-oriented research in resource-limited settings. Her long-term goals are to become a leader in neonatal IPC in low resource settings and devise interventions to reduce global burden of HA-BSI and associated mortality. This K23 will facilitate skill development in longitudinal data analysis, prediction models, survey development, HFE, and qualitative data analysis. Training will include formal coursework, supervised data analysis, and mentorship by a team with expertise in infectious diseases, IPC, biostatistics, epidemiology, patient safety, and HFE. Collectively, the activities of this K23 will provide a pathway to an independent career as a clinical investigator with expertise in healthcare epidemiology and IPC in low resource settings.