Improving Diagnosis in Gastrointestinal Cancer: Integrating Prediction Models into Routine Clinical Care

改善胃肠癌的诊断：将预测模型纳入常规临床护理

基本信息

批准号：
10641060
负责人：
Andrew J Read
金额：
$ 27.65万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10641060
关键词：
Address Adopted Adoption Algorithms Attention Award Biometry Blood Vessels Characteristics Chronic Clinical Clinical Trials Colorectal Complete Blood Count Consolidated Framework for Implementation Research Data Data Science Data Scientist Data Set Detection Development Diagnosis Diagnostic Disease Electronic Health Record Electronics Erythrocytes Evaluation Future Gastroenterologist Goals Health Services Healthcare Systems Hemoglobin Hemorrhage Integrated Health Care Systems Interview Investigation Iron Iron deficiency anemia Laboratories Learning Logic Logistic Regressions Machine Learning Malignant neoplasm of esophagus Malignant neoplasm of gastrointestinal tract Maps Medical Methods Michigan Modeling Nature Occult blood screen Patients Pattern Performance Predictive Cancer Model Reminder Systems Research Research Personnel Scientist Small Intestines Specific qualifier value Statistical Algorithm Stomach Structure Surveys System Techniques Testing Theoretical Domains framework Training Translational Research Translations United States Universities Veterans Health Administration Width Work acceptability and feasibility cancer diagnosis career clinical care clinical implementation clinical practice clinical predictive model design digital implementation facilitators implementation framework implementation science implementation strategy improved in silico innovation model development novel prediction algorithm predictive modeling predictive test prospective screening skills tool translational scientist

项目摘要

PROJECT SUMMARY/ABSTRACT Candidate: Andrew J. Read, MD, MS is a gastroenterologist and health services researcher at the University of Michigan with a research focus on improving diagnosis of gastrointestinal (GI) tract cancers. Dr. Read has prior training in biostatistics but not in mixed methods or implementation science. This NCI K08 award will train him to become a leader in the translational science of medical prediction, providing him with the skills to develop tailored implementation strategies for cancer prediction models and test these strategies in future clinical trials. Research Context: Iron deficiency anemia (IDA) is a common sign of many diseases, including GI tract cancers. Despite this important association, IDA is often under-recognized or under-investigated, resulting in delays in diagnosis. Fortunately, the electronic health record (EHR) contains potential diagnostic clues that can be leveraged to improve diagnosis of GI tract cancers. Specifically, algorithms can be developed to detect subtle changes in complete blood count (CBC) parameters to predict GI tract cancers. However, prediction models have rarely been implemented in clinical practice. Identifying the barriers and facilitators to implementing a model can allow for more customized implementation strategies to improve the chances of successful implementation. Research Aims: Dr. Read will (1) Refine a prediction model for detection of GI tract cancers using longitudinal laboratory data from the Veterans Health Administration (VA), the largest integrated healthcare system in the United States; (2) Identify barriers and facilitators to implementation of prediction models in clinical practice using mixed methods with an explanatory sequential design, incorporating a clinician survey followed by semi- structured clinician interviews; and (3) Develop and test components of a prediction model implementation strategy in a clinical setting, using Implementation Mapping. Training Aims: Dr. Read will develop expertise in: (1) Developing advanced longitudinal prediction models using a national dataset; (2) Using and applying mixed methods and implementation science frameworks to identify barriers and facilitators to successful implementation of a prediction model; (3) Applying Implementation Mapping to develop and test an implementation strategy for a novel clinical prediction model.

项目概要/摘要候选人：Andrew J. Read，医学博士、硕士，是该大学的胃肠病学家和健康服务研究员密歇根州的研究重点是改善胃肠道 (GI) 道癌症的诊断。里德博士有之前接受过生物统计学培训，但没有接受过混合方法或实施科学方面的培训。此次NCI K08奖将培训他成为医学预测转化科学的领导者，为他提供了以下技能：为癌症预测模型制定量身定制的实施策略，并在未来测试这些策略临床试验。研究背景：缺铁性贫血（IDA）是许多疾病的常见症状，包括胃肠道疾病癌症。尽管存在这一重要协会，但 IDA 常常未被充分认识或调查不足，导致延误诊断。幸运的是，电子健康记录 (EHR) 包含潜在的诊断线索，可以可用于改善胃肠道癌症的诊断。具体来说，可以开发算法来检测全血细胞计数 (CBC) 参数的细微变化可预测胃肠道癌症。然而，预测模型很少应用于临床实践。识别障碍和促进因素实施模型可以允许更多定制的实施策略，以提高机会成功实施。研究目标：Read 博士将 (1) 完善利用纵向检测胃肠道癌症的预测模型来自退伍军人健康管理局 (VA) 的实验室数据，该管理局是美国最大的综合医疗保健系统美国; (2) 识别预测模型在临床实践中实施的障碍和促进因素使用混合方法和解释性序贯设计，结合临床医生调查，然后进行半结构化临床医生访谈； (3) 开发和测试预测模型实现的组件临床环境中的策略，使用实施映射。培训目标：Read 博士将培养以下方面的专业知识：(1) 开发先进的纵向预测模型使用国家数据集； (2) 使用和应用混合方法和实施科学框架确定成功实施预测模型的障碍和促进因素；（三）申请实施映射，用于开发和测试新型临床预测模型的实施策略。