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

预防资源匮乏地区住院新生儿的抗菌药物耐药性和感染

基本信息

批准号：
10652977
负责人：
Julia Johnson
金额：
$ 17.17万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-13 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10652977
关键词：
Address Admission activity Algorithms Antibiotic Resistance Antibiotics Antimicrobial Resistance Assessment tool Bacterial Infections Biometry Birth Blood Carbapenems Caring Cephalosporins Characteristics Clinical Clinical Investigator Cohort Studies Colistin Communicable Diseases Communities Data Data Analyses Decision Making Decision Trees Development Devices Disease Outbreaks Engineering Enrollment Epidemiologic Methods Epidemiology Future Generations Goals Gram-Negative Bacteria Hand Health care facility Healthcare Hospitalization Human Hygiene Immune system Incidence India Infection Infection Control Infection prevention Intensive Care Intervention Investigation Klebsiella pneumoniae Length of Stay Life Measures Mechanical ventilation Mentors Mentorship Methodology Methods Modeling Morbidity - disease rate Neonatal Neonatal Intensive Care Units Neonatal Mortality Newborn Infant Nosocomial Infections Organism Pathway interactions Patients Pneumonia Positioning Attribute Prevalence Principal Investigator Procedures Prospective cohort Prospective, cohort study Recommendation Rectum Research Research Personnel Resistance Resistance to infection Resource-limited setting Risk Factors Risk Reduction Sepsis Site Surveys Testing Time Training Universities Work antibiotic resistant infections bacterial resistance biobank carbapenem resistance career classification trees clinical investigation cohort critically ill newborn health care model healthcare-associated infections high risk hospital care improved improved outcome infection risk low and middle-income countries machine learning model modifiable risk mortality neonatal care neonatal death neonatal health neonatal infection neonatal sepsis neonate novel pathogen patient oriented research patient safety predictive modeling premature prevent prospective rectal regression trees skill acquisition skills tertiary care tool treatment strategy

项目摘要

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.

项目概要/摘要每年，有 250 万婴儿在出生后的前 4 周内死亡，其中近四分之一死于感染原因。由于以下因素，入住新生儿重症监护室 (NICU) 的新生儿特别容易受到伤害：早产、免疫系统不成熟以及需要维持生命的侵入性手术和设备。在低和中等收入国家 (LMIC)，越来越多的新生儿重症监护室 (NICU) 为早产儿和重症患者提供护理新生儿。由于医疗保健相关血流感染（HA-BSI）在中低收入国家中更为常见，原因是缺乏足够的感染预防和控制 (IPC)，并且由于抗菌药物耐药率高而更难以治疗（AMR）。以前在这种情况下的研究主要集中在疫情调查上，并没有充分描述 HA-BSI 的危险因素。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。申请人正在进行的前瞻性队列研究的初步数据表明已在印度浦那的三个 NICU 入组了 6600 多名新生儿，这进一步证实了该地区 HA-BSI 的高发病率每 1000 个患者日的比率为 7.6，AMR 比率也很高。肺炎克雷伯菌中分离株是最常见的 BSI 病原体，96% 对第三代头孢菌素耐药，38% 对第三代头孢菌素耐药。碳青霉烯类。患有 BSI 的新生儿的死亡率为 22%。在本研究的框架内，以下内容是建议：(1) 确定 NICU 中发生 HA-BSI 的可改变危险因素； (2) 建立预测模型碳青霉烯类耐药菌（CRO）感染； (3) 开发并试点评估 IPC 的新工具新生儿重症监护病房 (NICU) 和分娩领域的实践。确定 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识别 CRO 感染风险最高的婴儿。这样的模型可以支持 NICU 临床医生选择正确的怀疑感染时使用抗生素，减少适当治疗的时间并减少不必要的使用最后的抗生素，如粘菌素。开发纳入人为因素的 IPC 评估工具工程 (HFE) 原则将使医疗机构能够优化 IPC 并降低医院获得性感染的风险感染和相关死亡率。这项指导性研究将为申请人提供高级流行病学方面的培训 IPC方法及其在中低收入国家中的应用申请人是约翰·霍普金斯大学的新生儿科医生在资源有限的环境中以患者为导向的研究。她的长期目标是成为新生儿领域的领导者在资源匮乏的环境中进行 IPC 并制定干预措施，以减轻 HA-BSI 和相关的全球负担死亡。该 K23 将促进纵向数据分析、预测模型、调查等方面的技能发展开发、HFE 和定性数据分析。培训将包括正式课程、监督数据由具有传染病、IPC、生物统计学、流行病学、患者专业知识的团队进行分析和指导安全和 HFE。总的来说，K23 的活动将为作为独立职业者提供一条途径具有医疗保健流行病学和资源匮乏地区 IPC 方面专业知识的临床研究者。