Leveraging machine learning to improve risk prediction for chemotherapy inducedneuropathy

利用机器学习改善化疗引起的神经病变的风险预测

• 批准号: 10364532
• 负责人: Alyce Sophia Adams
• 金额: $ 68.96万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10364532
• 关键词: Address; Adjuvant Chemotherapy; Adult; Affect; Age; Awareness; Biometry; Breast; Cancer Survivorship; Caring; Characteristics; Chemotherapy-induced peripheral neuropathy; Chronic; Clinical; Clinical Data; Clinical Trials; Colorectal Cancer; Community Health; Complex; Computer software; Decision Making; Development; Diagnosis; Dose; Dose-Limiting; Electronic Health Record; Goals; Health Services Research; Impairment; Individual; Interview; Journals; Lead; Life; Limb structure; Machine Learning; Malignant Neoplasms; Mental Depression; Methods; Modeling; Motor; Nature; Neuropathy; Numbness; Obesity; Oncology; Outcome; Pain; Patient Care; Patient Preferences; Patients; Peer Review; Peripheral Nervous System Diseases; Pharmacotherapy; Platinum; Prevention; Provider; Publications; Quality of life; Race; Reporting; Risk; Risk Estimate; Risk Factors; Sample Size; Savings; Statistical Models; Symptoms; Testing; Thinking; Time; Translations; Treatment Protocols; Vinca Alkaloids; analog; associated symptom; cancer care; cancer epidemiology; cancer invasiveness; cancer therapy; cancer type; care systems; chemotherapy; chemotherapy induced neuropathy; clinical decision-making; community based practice; comorbidity; disability; experience; experimental study; fall risk; follow-up; health care settings; high risk; improved; machine learning method; mathematical ability; medication safety; neurotoxicity; older patient; predictive modeling; risk prediction; side effect; survivorship; taxane; tool; treatment choice; treatment duration

Project Summary/Abstract Chemotherapy-induced peripheral neuropathy (CIPN) affects more than two-thirds of adults with invasive cancer who receive select adjuvant chemotherapies (e.g., taxanes, platinum analogs). Severe CIPN symptoms can lead to chemotherapy dose reductions, treatment delays, or changes in treatment regimens; thereby affecting the potential curative effects of chemotherapy. For some patients, CIPN symptoms can persist over time, contributing to lower quality of life. Little is known about risk factors for CIPN. Chemotoxicity risk scores have been developed and evaluated for use among elderly patients receiving chemotherapy. However, these tools generally report moderate predictive accuracy (60%-70%), small sample sizes, and short-term follow up. We are aware of no publicly available, validated risk models to assess risk of severe and chronic CIPN among diverse patients at risk for this potentially disabling side effect. The goal of this proposal is to identify patients at risk for CIPN and to understand how patients and provider interpret and use CIPN risk information in clinical decision-making. Focusing on more than 8,500 insured adults (18+) diagnosed with invasive, stage I-III breast and II-IIIA colorectal cancers (2013-2021) who received adjuvant chemotherapy treatment with known risk for CIPN, we will develop and validate predictive models to quantify the risk of severe CIPN and incident chronic CIPN and assess how CIPN risk information might be used to inform clinical decision-making about cancer treatment and survivorship care planning. We hypothesize that CIPN risk is a high priority for patients in thinking about treatment choice and survivorship care planning. In addition, we hypothesize that the relative importance of CIPN risk for patient and provider decision-making will vary by patient characteristics (e.g., age, cancer stage). We anticipate that the risk of severe and chronic CIPN can be predicted with a high degree of accuracy using electronic health records and machine learning methods. The study team has significant and complementary expertise in health services research, biostatistics and predictive modeling, oncology practice, cancer epidemiology, pharmacotherapy, drug safety and the patient care experience. To our knowledge, this will be one of the first studies to develop and validate a CIPN predictive model that can be used by oncology teams to inform treatment and care planning decisions and improve patient-valued outcomes. Translation and replication of the findings will be catalyzed through publication in peer-reviewed journals and the development and distribution of free software to facilitate testing and adaptation of the resulting risk models across diverse systems of care.

项目总结/摘要 化疗引起的周围神经病变（CIPN）影响超过三分之二的成年人， 接受选择的辅助化疗的侵袭性癌症（例如，紫杉烷、铂类似物）。 严重的CIPN症状可能导致化疗剂量减少，治疗延迟，或 治疗方案的改变;从而影响化疗的潜在疗效。 对于一些患者，CIPN症状可能会随着时间的推移而持续存在，导致生活质量降低。 对CIPN的危险因素知之甚少。化学毒性风险评分已经制定 并评估其在接受化疗的老年患者中的使用。然而，这些工具 一般报告中等预测准确性（60%-70%），小样本量，短期 跟进我们知道没有公开可用的、经过验证的风险模型来评估严重 和慢性CIPN在不同的患者中存在这种潜在的致残副作用的风险。 该提案的目标是识别有CIPN风险的患者，并了解如何 患者和提供者在临床决策中解释和使用CIPN风险信息。 专注于8，500多名被诊断患有侵入性I-III期乳腺癌的投保成年人（18岁以上） 和II-IIIA结直肠癌（2013-2021年），接受辅助化疗， CIPN的已知风险，我们将开发和验证预测模型，以量化 严重CIPN和偶发慢性CIPN，并评估CIPN风险信息如何用于 为癌症治疗和生存护理计划的临床决策提供信息。 我们假设CIPN风险是患者考虑治疗的优先考虑因素 选择和生存护理计划。此外，我们假设， 患者和提供者决策的CIPN风险将因患者特征而异（例如， 年龄、癌症阶段）。我们预计，严重和慢性CIPN的风险可以预测 使用电子健康记录和机器学习方法进行高度准确的评估。 该研究小组在卫生服务研究方面具有重要和互补的专业知识， 生物统计学和预测建模，肿瘤学实践，癌症流行病学， 药物治疗、药物安全性和患者护理经验。据我们所知，这将是 开发和验证CIPN预测模型的首批研究之一， 肿瘤学团队为治疗和护理计划决策提供信息， 结果。研究结果的翻译和复制将通过在 同行评审的期刊和自由软件的开发和分发，以方便测试 并在不同的护理系统中调整由此产生的风险模型。