Analyzing Patient-Level Data in a Breast Cancer Clinical Trial

分析乳腺癌临床试验中的患者水平数据

基本信息

批准号：
10720278
负责人：
Amrita Basu Somani
金额：
$ 36.94万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
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：
2023-07-19 至 2028-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10720278
关键词：
Adverse event Affect Algorithms Breast Breast Cancer therapy Caring Chronic Clinical Clinical Trials Cues Data Data Reporting Data Set Deterioration Development Diarrhea Distress Drug toxicity Early Intervention Event Fatigue Future Health Impairment Individual Intervention Knowledge Machine Learning Maximum Tolerated Dose Measures Methodology Methods Modeling Monitor Neoadjuvant Therapy Neuropathy Participant Patient Outcomes Assessments Patients Pharmaceutical Preparations Physical Function Provider Quality of life Reporting Research Resources Risk Serious Adverse Event Severities Site Supportive care Surveys Symptoms Testing Time Toxic effect Treatment Side Effects Visualization Woman active method arm cancer clinical trial clinical care cohort computer framework efficacy evaluation electronic patient reported outcomes experience follow-up health related quality of life high risk improved innovation malignant breast neoplasm oncology trial patient oriented personalized care prediction algorithm predictive modeling primary outcome prospective side effect symptom cluster symptom management treatment effect trial comparing

项目摘要

ABSTRACT Most women treated for breast cancer will experience some form of drug-related toxicity and subsequent impairments in Health-related Quality of Life (HRQOL), yet toxicity is assessed inconsistently in oncology trials. Although the potential for side effects of treatments is of great importance to patients in making informed choices about their treatment, the toxicities are often under-reported. When assessing symptoms of trial participants, patients and providers do not always attribute symptoms to the study drug, which can result in misclassification of the maximum tolerated dose. Furthermore, many drug toxicities such as neuropathy, fatigue and diarrhea are often underreported by providers in trials, and thus a patient-centered assessment may lead to earlier recognition of reversable side effects. A major gap in knowledge is how to analyze and utilize patient level toxicity data in real time, and how to present the data to providers in a format that can result in early toxicity mitigation. While the number of lower- grade toxicities may increase given the reporting of patient outcomes, acting on these lower grade toxicities can mitigate serious adverse events (SAEs). We have recently instantiated an electronic patient reported outcomes (ePRO) platform across 26 sites in I- SPY2 where we collect adverse events and quality of lie information. I-SPY2 is an adaptive platform trial for high risk, early-stage breast cancer that continuously evaluates the efficacy of new neoadjuvant breast cancer therapies. The overall objective of this proposal is to refine and implement new methodology using interpretable machine learning that can be used to underpin a framework to redirect treatment and avoid more serious illnesses. Such methodology does not exist in clinical trials today and can hugely benefit patients, their providers and the clinical care team by tracking the inflection points of patient distress that could otherwise be missed but may require more immediate intervention. The methods will be developed through a computational framework in discussion with providers, at different stages of treatment, such as when the severity of a single symptom really impacts physical functioning (primary outcome), or when constellation of symptoms herald a significant deterioration in overall health. The central hypothesis of this proposal is that the methodology that we are developing on who will develop chronic conditions and symptoms that may affect quality of life will mitigate the event of a serious adverse reaction and improve overall quality of life, particularly physical functioning. We will test our methodology in a group of I-SPY patients and Breast Care Center early-stage participants at UCSF.

抽象的大多数接受乳腺癌治疗的女性都会经历某种形式的药物相关毒性以及随后的症状健康相关生活质量（HRQOL）受损，但肿瘤学试验中对毒性的评估不一致。尽管治疗的潜在副作用对于患者了解情况非常重要在选择治疗方法时，毒性往往被低估。评估试验症状时参与者、患者和提供者并不总是将症状归咎于研究药物，这可能会导致最大耐受剂量的错误分类。此外，许多药物毒性，如神经病、在试验中，提供者经常低估疲劳和腹泻，因此以患者为中心的评估可能会导致更早认识到可逆的副作用。知识上的一个主要差距是如何实时分析和利用患者水平的毒性数据，以及如何以可以早期减轻毒性的格式向提供者提供数据。虽然数量较低鉴于患者结果的报告，对这些较低级别的毒性采取行动，级别毒性可能会增加可以减轻严重不良事件 (SAE)。我们最近在 I-26 的 26 个站点实例化了电子患者报告结果 (ePRO) 平台 SPY2，我们收集不良事件和谎言质量信息。 I-SPY2 是一个自适应平台试验高风险、早期乳腺癌，持续评估新辅助乳腺癌的疗效疗法。该提案的总体目标是使用以下方法完善和实施新方法可解释的机器学习可用于支撑一个框架，以重定向治疗并避免更多严重的疾病。这种方法在当今的临床试验中并不存在，但可以极大地造福于患者及其患者通过跟踪患者痛苦的拐点，否则可能会影响医疗服务提供者和临床护理团队错过了，但可能需要更立即的干预。这些方法将通过计算来开发在治疗的不同阶段，例如当单一疾病的严重程度时，与提供者讨论框架症状确实影响身体机能（主要结果），或者当一系列症状预示着整体健康状况显着恶化。该提案的中心假设是方法论我们正在研究谁会患上可能影响生活质量的慢性病和症状减轻严重不良反应的发生并提高整体生活质量，特别是身体素质发挥作用。我们将在一组 I-SPY 患者和早期乳腺护理中心中测试我们的方法加州大学旧金山分校的参与者。