Leveraging machine learning to improve risk prediction for chemotherapy inducedneuropathy

项目摘要/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 接受精选辅助化疗(例如紫杉烷、铂类似物)的浸润性癌症患者。 严重的CIPN症状可导致化疗剂量减少、治疗延误或 治疗方案的变化；从而影响化疗的潜在疗效。 对于一些患者，CIPN症状可能会持续一段时间，导致生活质量下降。 对CIPN的风险因素知之甚少。化学毒性风险评分已经被开发出来 并评估其在接受化疗的老年患者中的使用情况。然而，这些工具 通常报告的预测准确率为中等(60%-70%)、样本量小和短期 继续跟进。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的 和慢性CIPN在不同的患者中，有这种潜在的致残副作用的风险。 这项建议的目标是确定CIPN的风险患者，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。 重点关注8500多名被诊断为I-III期侵袭性乳房的投保成年人(18岁以上) 和II-IIIA结直肠癌(2013-2021年)接受辅助化疗的患者 对于CIPN的已知风险，我们将开发和验证预测模型来量化 严重CIPN和突发慢性CIPN，并评估如何利用CIPN风险信息 向临床决策提供有关癌症治疗和生存护理计划的信息。 我们假设CIPN风险是患者在考虑治疗时的高度优先事项 选择和生存护理计划。此外，我们假设， 患者和提供商决策的CIPN风险将因患者特征而异(例如， 年龄、癌症分期)。我们预计，严重和慢性CIPN的风险是可以预测的 使用电子健康记录和机器学习方法，具有高度的准确性。 研究小组在卫生服务研究方面拥有重要的和互补的专业知识， 生物统计学和预测模型、肿瘤学实践、癌症流行病学、 药物治疗、药物安全和患者护理经验。据我们所知，这将是 首批开发和验证CIPN预测模型的研究之一，该模型可由 肿瘤学团队为治疗和护理计划决策提供信息，并改善患者价值 结果。这些研究结果的翻译和复制将通过在 同行评议的期刊以及促进测试的自由软件的开发和分发 以及在不同的护理系统中调整由此产生的风险模型。

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.