Arthur Patrick McDeed, Kathleen Van Dyk, Xingtao Zhou, Wanting Zhai, Tim A Ahles, Traci N Bethea, Judith E Carroll, Harvey Jay Cohen, Zev M Nakamura, Kelly E Rentscher, Andrew J Saykin, Brent J Small, James C Root, Heather Jim, Sunita K Patel, Brenna C Mcdonald, Jeanne S Mandelblatt, Jaeil Ahn
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We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment.</p><p><strong>Methods: </strong>We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function.</p><p><strong>Results: </strong>Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score.</p><p><strong>Conclusions: </strong>Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. 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引用次数: 0
摘要
目的:癌症幸存者通常会在接受癌症治疗后出现认知能力下降。由于癌症相关认知功能下降(CRCD)的病因复杂,预测哪些人有可能出现 CRCD 仍是一项临床挑战。我们建立了一个模型来预测乳腺癌幸存者在接受系统治疗后会出现 CRCD:我们使用了 "癌症患者的思考与生活 "研究(Thinking and Living with Cancer study),这是一项正在进行中的大型多站点前瞻性研究,研究对象为老年乳腺癌幸存者,在系统治疗前、系统治疗开始后的 12 个月和 24 个月进行了完整的评估。认知能力通过注意力、处理速度和执行功能(APE)的神经心理学测试进行测量。CRCD的定义是:APE评分从基线到12个月下降≥0.25 SD(在匹配对照组中观察到的从基线到12个月的变化)(短暂性),或持续下降>0.25 SD,持续到24个月。我们使用机器学习方法,通过基线人口统计学、肿瘤特征和治疗、基因型、合并症以及自我报告的身体、社会心理和认知功能来预测 CRCD:32%的幸存者出现了短暂的认知功能下降,其中41%的女性出现了持续性认知功能下降。对 CRCD 的预测效果良好:对短暂性和持续性衰退的预测曲线下面积分别为 0.75 和 0.79。对预测 CRCD 最有参考价值的变量包括载脂蛋白 E4 阳性、肿瘤 HER2 阳性、肥胖、心血管合并症、更多处方药和更高的基线 APE 评分:我们的概念验证工具表明,我们的预测模型可能有助于预测 CRCD 的风险。未来的研究需要验证这种方法在常规实践中预测 CRCD 的有效性。
Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study.
Purpose: Cancer survivors commonly report cognitive declines after cancer therapy. Due to the complex etiology of cancer-related cognitive decline (CRCD), predicting who will be at risk of CRCD remains a clinical challenge. We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment.
Methods: We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function.
Results: Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score.
Conclusions: Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. Future research is needed to validate this approach for predicting CRCD in routine practice settings.