Development and Assessment of Decision Supporting System for Renal studies

肾脏研究决策支持系统的开发和评估

• 批准号: 9765306
• 负责人: AMITA K. MANATUNGA
• 金额: $ 34.77万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765306
• 关键词: Address; Affect; Age; American; Area; Bladder; Blood; Caring; Categories; Chronic Kidney Failure; Clinical; Clinical Data; Complex; Coupled; Data; Decision Support Systems; Development; Diagnosis; Diagnostic; Discipline of Nuclear Medicine; Disease; Diuretics; Drainage procedure; Feasibility Studies; Goals; Hospitals; Image; Image Analysis; Interobserver Variability; Joints; Kidney; Kidney Diseases; Medicare; Methodology; Methods; Modeling; Monitor; Nephrology; Obstruction; Patients; Pelvis; Performance; Photons; Physicians; Pilot Projects; Play; Populations at Risk; Radioactive Tracers; Radiology Specialty; Research Design; Role; Sample Size; Sampling; Scanning; Site; Statistical Methods; Stress; Structure; System; Time; Tracer; Training; Travel; United States; Ureter; Validation; automated image analysis; base; clinically relevant; cost; data acquisition; data integration; diagnostic accuracy; experience; imaging study; improved; interest; intravenous injection; kidney imaging; knowledge base; nephrogenesis; pressure; radiologist; sex; software systems; treatment response; user-friendly

Project Summary/Abstract The pressures imposed by a rapidly expanding knowledge base, gaps in training, limited experience and escalating time constraints create a dilemma for diagnostic radiologists and their patients. How can radiologists consistently provide quality diagnostic interpretations? How can radiologists reduce intra- and interobserver variability in interpretation such that the diagnosis is less dependent on the interpreting radiologist and more accurately reflects the presence or absence of the underlying disease? Our long-term objectives are (1) to develop a general statistical methodology for the development and implementation of a decision supporting system (DSS) to help physicians to make informed decisions in radiologic diagnosis and to reduce intra- and interobserver variability and (2) to develop a new general statistical inferential framework that can determine if the performance of our DSS is equivalent to that of an expert or a panel of experts. Our immediate goal and proof of concept is motivated by the need to develop a DSS to improve the care of nephrology patients referred for a nuclear medicine renal scans, an area where many radiologists lack both training and experience. A renal scan is obtained by injecting a radioactive tracer, 99mTc MAG3 and sequentially imaging that tracer over a 20-30 min period as it is removed from the blood by the kidneys and passes down the ureters into the bladder. When obstruction is suspected, the patient often receives a potent diuretic and sequential images over the kidney are obtained for an additional 20 min. Radiologists typically use a few specific points on the kidney time activity curves (renogram) to assist in interpretation of the study. We propose to integrate clinical data with automated image analysis to provide a comprehensive interpretation of MAG3 renal scans in a structured format. Rather than using a few isolated features on the renogram, we propose to develop a latent class modeling approach for predicting kidney obstruction that jointly models renogram curve data (functional data [13,49]) resulting from renal images and expert ratings as well as other relevant clinical variables (Aim 1). Extensions will be developed for handling missing data that are present in this type of studies. In order to evaluate the newly developed DSS, we propose to develop a new general statistical inferential framework that can determine if the performance of our DSS is equivalent to that of an expert or a panel of experts. The methodology is developed for both categorical and continuous ratings of the disease status (Aim 2). We plan to validate the DSS with an independent data sample (Aim 3). [We plan to conduct two pilot studies with (a) nuclear medicine residents and (b) radiology residents to determine the feasibility of applying DSS to clinical setting under Aim 4]. While intended to be of direct benefit to the interpretation of renal scans, the DSS and statistical methodology to be developed address common and fundamental issues in image interpretation, especially where the integration of data is needed to recover the information about the disease.

项目摘要/摘要 迅速扩大的知识库，培训差距，有限的经验所施加的压力 随着时间的限制，诊断放射科医生及其患者造成了困境。怎么可能 放射科医生始终提供质量诊断解释？放射学家如何减少内部和 解释中观察者间的变异性使诊断较少依赖解释 放射科医生，更准确地反映了潜在疾病的存在或不存在？我们的长期 目标是（1）开发一种一般统计方法来开发和实施 决策支持系统（DSS），以帮助医生在放射学诊断中做出明智的决定和 降低观察者内和间的变异性，（2）开发一个新的一般统计推断框架 可以确定我们的DSS的性能是否等于专家或专家小组的表现。 我们的直接目标和概念证明是由开发DSS来改善DSS的动机 护理肾脏病患者转介核医学肾脏扫描，这是许多放射科医生缺乏的领域 培训和经验。通过注射放射性示踪剂，99mtc mag3和 依次对示踪剂进行依次成像，因为肾脏将其从血液中清除，并 将输液器沿着膀胱传递到膀胱。怀疑障碍时，患者经常会收到有效的 肾脏上的利尿剂和顺序图像再获得20分钟。放射科医生通常使用 肾脏时间活动曲线（肾脏图）的一些特定点有助于解释研究。我们 建议将临床数据与自动图像分析相结合，以提供对 MAG3肾脏扫描以结构化格式。我们没有在肾脏图上使用一些孤立的功能，而是我们 建议开发一种预测肾脏阻塞的潜在类建模方法 肾脏曲线数据（功能数据[13,49]）由肾脏图像和专家评级以及其他 相关的临床变量（AIM 1）。将开发扩展程序，以处理存在的丢失数据 这种类型的研究。为了评估新开发的DSS，我们建议开发新的将军 统计推论框架可以确定我们的DSS的性能是否等于 专家或专家小组。该方法是针对分类和连续评级开发的 疾病状况（目标2）。我们计划使用独立的数据样本验证DSS（AIM 3）。 [我们计划 对（a）核医学居民和（b）放射学居民进行两项试验研究，以确定 在AIM 4下将DSS应用于临床环境的可行性]。虽然打算直接利益 肾脏扫描，开发的DSS和统计方法的解释 图像解释中的基本问题，尤其是在需要数据集成以恢复的情况下 有关疾病的信息。