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

• 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
• 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

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.

项目摘要/摘要 候选人：安德鲁·J·里德，医学博士，医学硕士，是该大学的胃肠病专家和卫生服务研究员 他的研究重点是提高胃肠道(GI)癌的诊断。里德博士有 以前接受过生物统计学方面的培训，但不是混合方法或实施科学。这项NCI K08奖将培训 他成为医学预测翻译科学的领导者，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)是包括胃肠道在内的许多疾病的共同征兆 癌症。尽管存在这种重要的联系，但国际开发协会往往得不到充分认识或未得到充分调查，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 可用于改善对胃肠道癌症的诊断。具体地说，可以开发算法来检测 全血细胞计数(CBC)参数的细微变化预测胃肠道癌症。然而，预测 模型很少在临床实践中实施。确定实现以下目标的障碍和促进者 实施模型可以允许更多定制的实施策略，以提高 成功实施。 研究目标：Read博士将(1)改进用于检测胃肠道癌的纵向预测模型 来自退伍军人健康管理局(VA)的实验室数据，这是美国最大的综合医疗系统 美国；(2)确定在临床实践中实施预测模型的障碍和促进者 使用解释性序贯设计的混合方法，结合临床医生调查，然后是半 结构化的临床医生访谈；以及(3)开发和测试预测模型实现的组件 临床环境中的战略，使用实施图。 培训目标：里德博士将在以下方面发展专业知识：(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者；(3)应用 实施映射，以开发和测试一种新的临床预测模型的实施策略。