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.

摘要 大多数接受乳腺癌治疗的女性会经历某种形式的药物相关毒性， 健康相关生活质量（HRQOL）的损害，但在肿瘤学试验中对毒性的评估不一致。 虽然潜在的副作用的治疗是非常重要的病人，使知情的 关于他们的治疗选择，毒性往往被低估。在评估试验症状时 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，如神经病变， 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 可能导致早期认识到可逆的副作用。 知识上的一个主要差距是如何真实的实时分析和利用患者水平的毒性数据，以及如何 向供应商提供数据时采用的格式应能及早减轻毒性。虽然人数较低- 考虑到患者结局的报告，作用于这些较低级别的毒性，级别毒性可能会增加 可缓解严重不良事件（SAE）。 我们最近在I-26个研究中心实例化了电子患者报告结局（ePRO）平台， SPY 2，我们收集不良事件和谎言信息的质量。I-SPY 2是一个自适应平台试验， 高危、早期乳腺癌，持续评估新辅助乳腺癌的疗效 治疗本提案的总体目标是完善和实施新方法， 可解释的机器学习，可用于支持框架，以重新定向治疗并避免更多 严重的疾病。这种方法在今天的临床试验中并不存在，但可以使患者及其家属受益匪浅。 提供者和临床护理团队通过跟踪病人痛苦的拐点，否则可能是 错过了，但可能需要更直接的干预。这些方法将通过计算 在治疗的不同阶段，例如当一个单一的严重程度， 症状确实影响身体功能（主要结局），或者当症状群预示着 整体健康状况严重恶化。这一建议的核心假设是， 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， 缓解严重不良反应事件并改善整体生活质量，特别是身体健康 功能我们将在一组I-SPY患者和乳腺护理中心的早期阶段测试我们的方法 UCSF的参与者。