Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
JAMIA Open Pub Date : 2024-01-19 eCollection Date: 2024-04-01 DOI:10.1093/jamiaopen/ooae006
Ilia Rattsev, Vered Stearns, Amanda L Blackford, Daniel L Hertz, Karen L Smith, James M Rae, Casey Overby Taylor
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Abstract

Objectives: Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time.

Materials and methods: Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication.

Results: The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up.

Discussion and conclusion: Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

纳入突发症状和遗传协变量可提高对芳香化酶抑制剂停药的预测。
目的:在服用芳香化酶抑制剂(AIs)的乳腺癌患者中,过早停药很常见。虽然已经确定了几种预测因素,但如何同时考虑个人的多种风险因素尚不清楚。我们试图开发一种用于预测 AI 停药的工具,并探索风险因素的预测价值如何随时间而变化:我们使用生存机器学习来预测 181 名加入前瞻性队列的女性停用人工合成药物的时间。通过随时间变化的曲线下面积(AUC)、c-指数和综合布赖尔评分对模型进行评估。特征重要性分析通过夏普利加法解释(SHAP)进行,其预测价值的时间依赖性则通过时间依赖性 AUC 进行分析。为风险交流构建了个性化生存曲线:表现最好的模型包含了遗传风险因素和患者报告结果的变化,平均随时间变化的AUC为0.66,6个月和12个月截止时的AUC分别为0.72和0.67。最重要的特征包括 ESR1 变异和突发症状。遗传风险因素的预测价值在治疗的第一年最高。身体功能下降是随访时最强的独立预测因素:讨论和结论:纳入基因组和 3 个月随访数据提高了模型识别人工智能停药风险个体的能力。遗传风险因素对预测早期停药者尤为重要。这项研究深入揭示了人工智能停药的复杂性,并强调了将遗传风险因素和突发症状纳入预测模型的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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