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

• 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
• 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

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 大多数接受过乳腺癌治疗的妇女会经历某种形式的与药物有关的毒性和随后的毒性 与健康相关的生活质量障碍（HRQOL）的损害，在肿瘤学试验中尚不一致地评估了毒性。 尽管治疗副作用的潜力对于患者而言非常重要 关于他们的治疗的选择，毒性常常被低估。评估试验症状时 参与者，患者和提供者并不总是将症状归因于研究药物，这可能导致 最大耐受剂量的错误分类。此外，许多药物毒性，例如神经病， 疲劳和腹泻通常被提供者在试验中低估，因此以患者为中心的评估 可能会导致早期识别可逆副作用。 知识的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 以一种形式将数据呈现给提供者，从而导致早期毒性降低。而较低的数量 鉴于患者预后的报告，等级毒性可能会增加 可以减轻严重的不良事件（SAE）。 我们最近实例化了一名电子患者报告的结果（EPRO）平台，跨I-的26个地点 SPY2我们收集不良事件和谎言信息的质量。 i-spy2是一个自适应平台试验 高风险，早期乳腺癌不断评估新的新辅助乳腺癌的功效 疗法。该提案的总体目的是使用 可解释的机器学习，可用于基础一个框架以重定向处理并避免更多 严重疾病。今天的临床试验中不存在这种方法，可以极大地使患者受益 提供者和临床护理团队通过跟踪否则可能是患者困扰的拐点 错过，但可能需要更直接的干预。该方法将通过计算开发 与提供者的讨论框架，在不同的治疗阶段，例如单一的严重程度 症状确实会影响身体机能（主要结果），或者症状星座预示 整体健康状况显着恶化。该提议的核心假设是 我们正在发展谁会发展可能影响生活质量的慢性疾病和症状 减轻严重反应的事件并改善整体生活质量，尤其是身体上的质量 功能。我们将在一组I-Spy患者和乳房护理中心早期测试我们的方法论 UCSF的参与者。