Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapy

对多模式机器学习算法进行多机构验证，以预测和减少癌症治疗期间的急性护理

• 批准号: 10587221
• 负责人: Julian Clint Hong
• 金额: $ 38.91万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587221
• 关键词: Accident and Emergency department; Address; Affect; Apple; Artificial Intelligence; Asian; Black race; Boston; COVID-19 pandemic; California; Caring; Chemotherapy and/or radiation; Clinical; Clinical Data; Clinical Trials; Community Practice; Complement; Computer software; Data; Data Sources; Devices; Disease; Early identification; Ecosystem; Electronic Health Record; Emergency department visit; Ensure; Equity; Evaluation; Future; Geography; Goals; Health; Healthcare; Healthcare Systems; Hospital Departments; Hospitalization; Hospitals; Informatics; Institution; Intervention; Israel; Machine Learning; Malignant Neoplasms; Medical center; Medicare; Modeling; Monitor; Native Americans; Non-Small-Cell Lung Carcinoma; Oncology; Outcome; Outpatients; Patient Care; Patients; Performance; Procedures; Public Health; Quality of life; Race; Radiation therapy; Randomized; Randomized, Controlled Trials; Research; Resources; San Francisco; Supportive care; System; Systemic Therapy; Treatment outcome; Underserved Population; United States; United States Centers for Medicare and Medicaid Services; Universities; Validation; Washington; Work; acute care; cancer care; cancer therapy; chemoradiation; chemotherapy; clinical practice; cohort; comorbidity; cost; data harmonization; fitbit; handheld mobile device; health care delivery; health care settings; health data; health equality; high risk; improved; machine learning algorithm; machine learning model; machine learning prediction; machine learning prediction algorithm; multimodality; patient population; personalized predictions; portability; practice setting; predictive modeling; prospective; randomized, controlled study; remote health care; rural Americans; socioeconomics; success; symptom management; symptom treatment; symptomatic improvement; tool; wearable device

PROJECT ABSTRACT An estimated 650,000 patients with cancer receive systemic therapy or radiation therapy (RT) annually in the United States. Many of these patients undergoing outpatient cancer therapy will require acute care with an emergency department visit or hospital admission due to symptoms from treatment, disease, or comorbidities. This can impact cancer outcomes, patient treatment decisions, and costs to patients and the healthcare system. While there has been much enthusiasm for artificial intelligence and machine learning (ML) to improve healthcare delivery, high quality prospective data are lacking, especially across diverse clinical practice settings. We previously completed one of the first randomized controlled studies in healthcare ML, demonstrating that ML based on EHR data can accurately generate personalized predictions and guide supportive interventions to decrease acute care requirements and costs in patients undergoing RT and chemoradiotherapy (CRT) (NCT04277650). We have also developed a ML model for predicting hospitalizations based on prospective clinical trials of daily step counts collected in patients undergoing CRT. The research objective of this application is to leverage a geographically, racially, socioeconomically, and technically diverse network of healthcare settings and patients to assess and maximize how accurately and equitably these approaches generalize. Our team includes the University of California, San Francisco (UCSF), Duke University, Beth Israel Deaconess Medical Center, Essentia Health in Duluth, MN and Ashland, WI, Washington Hospital in Fremont, CA, Duke Regional Hospital in Durham, NC, and Duke Raleigh Hospital in Raleigh, NC. Specifically, we seek to: (1) prospectively evaluate the validity of an EHR-based acute care prediction ML algorithm across our network and establish a framework for equity, generalizability, and portability and (2) validate our existing patient-generated health data (PGHD; step count) models that predict hospitalization during CRT at a second institution and integrate with our EHR-based ML algorithm to enhance prediction of acute care needs. We hypothesize that our approaches will be accurate across institutions though require adjustments for both generalizability and fairness, and that EHR- and PGHD-based approaches will offer complementary predictive performance. The long-term goal is to develop informatics-based tools that can be broadly and equitably deployed to improve the delivery of cancer care and subsequent treatment outcomes. This research will generate data regarding the generalizability and fairness of EHR- and PGHD-based approaches and a platform for a future multi-institutional randomized controlled trial.

项目摘要 据估计，每年有65万癌症患者接受全身治疗或放射治疗（RT）。 美国的许多接受门诊癌症治疗的患者需要急性护理， 因治疗、疾病或合并症引起的症状而急诊或住院。 这可能会影响癌症结果、患者治疗决策以及患者和医疗保健的成本。 系统虽然人们对人工智能和机器学习（ML）有很大的热情， 医疗保健服务，缺乏高质量的前瞻性数据，特别是在不同的临床实践中 设置. 我们之前完成了医疗ML的首批随机对照研究之一，证明 基于EHR数据的ML可以准确地生成个性化预测，并指导支持性干预， 减少接受RT和放化疗（CRT）的患者的急性护理需求和成本 （NCT04277650）。我们还开发了一个ML模型，用于根据预期的 在接受CRT的患者中收集的每日步数的临床试验。本研究的目的是 应用程序是利用地理上，种族，社会经济和技术上多样化的网络， 医疗机构和患者评估和最大限度地提高这些方法的准确性和公平性， 概括。我们的团队包括加州大学，旧金山弗朗西斯科（UCSF），杜克大学，贝斯以色列 女执事医疗中心，明尼苏达州杜卢斯和威斯康星州阿什兰的迪肯健康中心，弗里蒙特的华盛顿医院， 加利福尼亚州达勒姆的杜克地区医院和北卡罗来纳州罗利的杜克罗利医院。具体来说，我们寻求 （1）前瞻性评估基于EHR的急性护理预测ML算法在我们的 网络和建立一个框架的公平性，普遍性，和便携性和（2）验证我们现有的 患者生成的健康数据（PGHD;步数）模型，预测CRT期间第二次住院治疗 并与我们基于EHR的ML算法相结合，以增强对急性护理需求的预测。我们 假设我们方法在机构间是准确的，尽管需要对两者进行调整 普遍性和公平性，以及EHR和PGHD为基础的方法将提供互补的预测 性能 长期目标是开发基于信息的工具，这些工具可以广泛和公平地部署， 改善癌症护理的提供和后续治疗结果。这项研究将产生数据 关于EHR和PGHD为基础的方法和未来的平台的普遍性和公平性 多机构随机对照试验。