Preventing rapid decline in CF: statistical research career commitment

防止 CF 快速下降：统计研究职业承诺

• 批准号: 9116939
• 负责人: Rhonda Szczesniak
• 金额: $ 16.07万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9116939
• 关键词: Address; Adolescence; Age; Algorithms; Alzheimer' s Disease; Area; Award; Biological Markers; Biomedical Research; Biometry; Caring; Cause of Death; Clinic; Clinical; Clinical Management; Clinical Research; Clinical Trials; Complex; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; Data; Data Analyses; Decision Analysis; Decision Making; Detection; Development Plans; Disease; Disease Marker; Disease Progression; Doctor of Philosophy; Early Diagnosis; Early treatment; Environment; Epidemiology; Event; Faculty; Figs - dietary; Foundations; Future; Gender; Goals; Hand; Health; Healthcare; Individual; Intervention; Leadership; Light; Lung; Lung diseases; Measures; Medical; Medical center; Medicine; Mentors; Methods; Microbiology; Mission; Modeling; Monitor; Mutation; Non-linear Models; Outcome; Outcomes Research; Patient-Focused Outcomes; Patients; Pediatric Hospitals; Phenotype; Population; Positioning Attribute; Prevention; Prospective Studies; Provider; Public Health; Pulmonary Cystic Fibrosis; Pulmonary function tests; Pulmonology; Registries; Research; Research Activity; Research Methodology; Research Personnel; Research Training; Resources; Respiratory physiology; Risk; Science; Severities; Sleep Disorders; Staging; Statistical Methods; Synaptic Transmission; System; Teacher Professional Development; Testing; Time; Training; Training Activity; Translating; Translational Research; United States National Institutes of Health; Universities; apprenticeship; base; cardiovascular health; care delivery; career; career development; clinical decision-making; clinical practice; clinically relevant; cystic fibrosis patients; design; efficacy trial; experience; high risk; improved; individual patient; innovation; mid-career faculty; nutrition; patient oriented; patient registry; pediatric department; personalized care; personalized decision; personalized management; personalized medicine; point of care; prediction algorithm; prevent; prognostic; programs; prospective; research and development; research facility; responsible research conduct; skills; support tools; tool; training project; treatment strategy; young adult

 DESCRIPTION (provided by applicant): Candidate: Rhonda Szczesniak, Ph.D., is a trained mathematical statistician. Her overarching career goal is to develop an independent clinical-research career translating statistical innovation for personalized medicine into clinical trials and practice, thereby improving the care and outcomes of patients with cystic fibrosis (CF) and other lung diseases. She is an Associate Professor in the University of Cincinnati (UC) Department of Pediatrics and jointly appointed to the Division of Biostatistics & Epidemiology and the Division of Pulmonary Medicine at Cincinnati Children's Hospital Medical Center (CCHMC). The Candidate's commitment to biomedical research began with developing a functional data analysis method to classify synaptic transmission wave-forms. Her faculty-level pursuit of biomedical research has led to successful projects in data-intensive statistical methods applied to research in lung disease and cardiovascular health. Career Development: To become an independent clinical and translational researcher in quantitative biomedicine, the proposed K25 career development plan will build upon the Candidate's early career development and experience with focus on five key areas: 1) CF lung disease markers and clinical management; 2) pulmonary outcomes research; 3) computational medicine; 4) clinical trials; 5) R01-level grantsmanship. She will expand her understanding in the 1st area through hands-on clinical training in pulmonary function testing, on campus didactic training in disease-specific clinical/translational research, select attendance at presentations on clinical, basic and translational CF projects from local and invited researcher leaders, and participation in a CF biomarker consortium. She will establish expertise in the 2nd area through on-campus didactic training in mixed methods research and participation in a collaborative, patient-centered pulmonary outcomes research lab. She will develop and apply advanced skills in the 3rd area by apprenticing in a computational medicine lab, training in healthcare interface design, and completing on-campus coursework in intelligent data analysis and decision analysis. Her expertise in the 4th area will be gained through local and NIH training. She will acquire academic and professional skills for the 5th area by taking an on-campus course in leadership in clinical/translational research and participating in early-stage CF investigator forums, and faculty development seminars. She will receive training in the responsible conduct of research through formal coursework and CF clinic/lab interactions. Mentors/Environment: The Candidate's sponsor is a well-established CF clinical and translational researcher and trials investigator. Together, they have assembled a strong team to guide her through the proposed training and research activities. Her co-sponsors are established investigators in pulmonary outcomes research and computational medicine. Her on-campus consultants have extensive clinical research and training experience in measuring markers of lung disease and CF care delivery. Her external consultant is an R01- level researcher with accomplishments in the Candidate's statistical field, functional data analysis. Her research and training will utilize rich intellectual and physical resources available through the distinguished UC/CCHMC CF research environment, including the Pulmonary Function Testing Lab; nationally ranked Boomer Esiason Cystic Fibrosis Center; Computational Medicine Center; Office for Clinical/Translational Research; CF Research and Development Program; top-performing CF Clinical/Translational Research Facility. Research: Progressive lung disease is the primary cause of death in individuals with cystic fibrosis (CF). Rapid decline, characterized by accelerated loss of lung function, is a ubiquitous event in the lives of CF patients. Identifying those at highest risk for rapid decline is a signifiant gap in CF care, and offers the opportunity to intervene prior to irreversible lung damage. This gap is exacerbated by the paucity of individualized predictive data on rapid decline and continued use of linear statistical approaches to model nonlinear CF disease progression. Functional data analysis is a statistical method that has been used to characterize nonlinear phenomena and elucidate complex pathophysiological relationships in different disease states. The overall objective of this research, which serves as the next step to achieve the Candidate's overarching career goal, is to utilize a well-maintained, rich CF registry and prospective study data to accurately forecast the onset of rapid decline in individual patients, and to develop a feasible medical-monitoring tool that positively impacts CF point-of-care decision-making. The central hypothesis is that translating intensive patient-level data through functional data analysis into a medical monitoring system and accurately forecasting lung disease progression to help prioritize pulmonary interventions will improve individualized care. Specific aims are to 1 characterize the phenotype of CF patients at increased risk of early, rapid decline; 2) design a decision support tool to moni- tor real-time lung-function decline for personalized clinical management of CF patients; 3) validate a model-based detection algorithm for rapid decline that informs CF point-of-care decisions. With systems to predict rapid decline, better prospective treatment decisions will become possible, resulting in better patient outcomes. Summary: Capitalizing on the unique combination of resources available to the Candidate and her quantitative training, this project will rapidly position her to submit an R01 to conduct a definitive efficacy trial of clinical forecasting systems for personalized CF therapies and establish a pipeline of R01-level research to design and evaluate medical monitoring interventions in other lung diseases and disorders.

 描述（由申请人提供）： 候选人：Rhonda Szczesniak博士是一位受过训练的数学统计学家。她的总体职业目标是开发独立的临床研究职业，将个性化医学的统计创新转化为临床试验和实践，从而改善护理 囊性纤维化（CF）和其他肺部疾病的患者的结局。她是辛辛那提大学（UC）儿科学系的副教授，并共同任命为辛辛那提儿童医院医疗中心（CCHMC）的生物统计学和流行病学系和肺病科。候选人对生物医学研究的承诺从开发一种功能数据分析方法开始，以对合成传输波形进行分类。她对生物医学研究的教师层次追求已导致用于用于肺部疾病和心血管健康研究的数据密集型统计方法的成功项目。 职业发展：成为定量生物医学的独立临床和翻译研究人员，拟议的K25职业发展计划将基于候选人的早期职业发展，并专注于五个关键领域：1）CF肺病标记和临床管理； 2）肺结果研究； 3）计算医学； 4）临床试验； 5）R01级赠款技巧。 她将通过肺功能测试的动手临床培训，在疾病特异性的临床/转化研究中进行肺功能培训，在第一个领域的临床培训扩大自己的了解，并在本地和受邀研究人员的临床，基本和翻译CF项目的演讲中精选出席，并参与CF Biomarkarker Consortium。她将通过混合方法研究和参与以患者为中心的肺结果研究实验室的校园教学培训在第二领域建立专业知识。她将通过在计算医学实验室，医疗界面设计中的培训以及在智能数据分析和决策分析中完成校园课程工作，在第三领域发展和运用高级技能。她在第四区的专业知识将通过本地和NIH培训获得。她将通过参加临床/翻译研究领导力的校园内课程，参加早期CF研究者论坛和教职员工发展半手，通过校园内的校园内校园课程来获得第五领域的学术和专业技能。她将通过正式课程和CF诊所/实验室互动接受负责任的研究培训。 导师/环境：候选人的赞助商是一位公认的CF临床和翻译研究人员和试验研究者。他们共同组建了一个强大的团队，指导她完成拟议的培训和研究活动。她的共同发起人是肺结果研究和计算医学领域的研究人员。她的校园顾问在测量肺部疾病和CF护理分娩的标志方面具有广泛的临床研究和培训经验。她的外部顾问是R01级的研究人员，在候选人统计领域（功能数据分析）中取得了成就。她的研究和培训将利用通过杰出的UC/CCHMC CF研究环境（包括肺功能测试实验室）获得丰富的智力和物理资源；全国排名的Boomer Esiason囊性纤维化中心；计算医学中心；临床/转化研究办公室； CF研发计划；表现最好的CF临床/转化研究机构。 研究：进行性肺部疾病是囊性纤维化（CF）个体死亡的主要原因。在CF患者的生活中，以加速肺功能丧失的迅速下降是无处不在的事件。确定快速下降风险的人是CF护理的显着差距，并提供了在不可逆肺损害之前进行干预的机会。关于快速下降和持续使用线性统计方法来模拟非线性CF疾病进展的个性化预测数据的匮乏，这一差距加剧了。功能数据分析是一种统计方法，用于表征非线性现象并阐明不同疾病状态中的复杂病理生理关系。这项研究的总体目标是实现候选人总体职业目标的下一步，是利用一份良好的，丰富的CF注册表和前瞻性研究数据来准确预测个别患者的快速下降，并开发出可行的医疗监测工具，从而有效地影响CF Pare Poime Poime Poime Poime Make的决定。中心假设是，通过功能数据分析将密集的患者级数据转化为医学监测系统，并准确地预测肺部疾病进展以帮助优先级肺部干预措施，将改善个性化的护理。具体目的是1表征CF患者的表型，其早期迅速下降的风险增加。 2）设计一种决策支持工具，以对CF患者的个性化临床管理进行实时肺部功能下降； 3）验证基于模型的检测算法快速下降，该算法可为CF保健的决策提供信息。随着系统可以预测快速下降的系统，将成为更好的前瞻性治疗决策，从而带来更好的患者预后。 摘要：利用可用于候选人可用的资源的独特组合和她的定量培训，该项目将迅速定位她提交R01，以对临床预测系统进行确切的有效性试验，以实现个性化CF疗法的临床预测系统，并建立R01级研究的管道，以设计和评估其他肺部疾病和疾病的医疗干预措施。