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

• 批准号: 10215584
• 负责人: Julia Johnson
• 金额: $ 17.09万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 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/10215584
• 关键词: Address; 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; Hospitals; Human; Hygiene; Immune system; Incidence; India; Infection; Infection Control; Infection prevention; Intensive Care; Intervention; Investigation; Klebsiella pneumoniae; Length of Stay; Life; Machine Learning; 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; Research; Research Personnel; Resistance; Resistance to infection; Resort; Resources; Risk; Risk Factors; Sepsis; Site; Supervision; Surveys; Testing; Time; Training; Universities; Work; antibiotic resistant infections; bacterial resistance; base; biobank; carbapenem resistance; career; classification trees; clinical investigation; cohort; critically ill newborn; health care model; healthcare-associated infections; high risk; i(19); improved; improved outcome; infection risk; low and middle-income countries; modifiable risk; mortality; neonatal care; neonatal death; 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

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.

项目摘要/摘要 每年，有250万婴儿在生命的前四个星期内死亡，近四分之一。 由于因素 早产，一种不成熟的免疫系统，并需要维持生命的侵入性程序和设备。低 和中收入国家（LMIC），越来越多的NICUS护理过早和重病 新生儿。 LMIC中与医疗保健相关的血液感染（HA-BBSI）更为常见，因为不足 感染预防和控制（IPC），并且由于抗菌素耐药的速率高而难以治疗 （AMR）。在这种情况下的先前研究主要集中在爆发调查上，但没有充分 描述HA-BSI的风险因素。医疗机构缺乏评估母体和新生儿IPC的有效工具 并创建改进策略。申请人正在进行的前瞻性队列研究的初步数据 在印度浦那的三名NICUS招募了6600多名新生儿，加强了HA-BSI的高发病率 设置为每1000例患者日的7.6率，以及高度的AMR。在肺炎的克雷伯菌中 分离株，最常见的BSI病原体，96％对第三代头孢菌素具有抵抗力，38％至 碳青霉烯。在BSI的新生儿中，死亡率为22％。在本研究的框架内，以下是 提议：（1）确定NICU中HA-BSI的可修改风险因素； （2）开发一个预测模型 耐碳青霉烯的生物（CRO）感染； （3）开发和飞行一种新的工具来评估IPC NICU和人工与交付的实践。确定NICU中HA-BSI的危险因素将促进 制定目标IPC策略。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。这样的模型可以支持NICU临床医生选择权利 怀疑感染时抗生素，减少了适当治疗的时间并减少不必要的使用 最后的度假抗生素，例如colistin。开发结合人为因素的IPC评估工具 工程（HFE）原则将使医疗机构能够优化IPC并降低医院获得的风险 感染和相关死亡率。这项指导的研究将培训高级流行病学的申请人 IPC在LMIC中的方法和应用。申请人是约翰·霍普金斯大学的新生儿学家 在资源有限的设置中以患者为导向的研究。她的长期目标是成为新生儿的领导者 IPC在低资源设置中并设计干预措施，以减少HA-BSI的全球负担 死亡。该K23将促进纵向数据分析，预测模型，调查的技能发展 开发，HFE和定性数据分析。培训将包括正式课程，监督数据 分析和由具有传染病，IPC，生物统计学，流行病学的专业知识的团队的指导 安全和HFE。总的来说，这项K23的活动将为独立职业提供途径 在低资源设置中，具有医疗保健流行病学和IPC专业知识的临床研究者。