美国退伍军人转移性阉割抗性前列腺癌患者的药物处方政策:因果机器学习方法。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS JMIR Medical Informatics Pub Date : 2024-11-19 DOI:10.2196/59480
Deepika Gopukumar, Nirup Menon, Martin W Schoen
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引用次数: 0

摘要

背景:前列腺癌是导致美国男性死亡的第二大原因。如果在早期发现并治疗,前列腺癌通常是可以治愈的。但是,晚期前列腺癌(如转移性抗性前列腺癌)的死亡风险很高。目前有多种治疗方案,最常见的包括多西他赛、阿比特龙和恩杂鲁胺。多西他赛是一种细胞毒性化疗,而阿比特龙和恩扎鲁胺则是雄激素受体通路抑制剂(ARPI)。由于毒性较低,与多西他赛相比,ARPIs更受青睐。目前还没有研究利用机器学习患者的人口统计学特征、检查结果和合并症来识别异质性治疗规则,从而改善mCRPC患者的生存期:本研究旨在测量处方药物与总生存期(以随访天数的形式表示)相关性的患者层面异质性,并利用患者人口统计学、检验结果和合并症得出一套处方药物规则:我们排除了在开具 ARPI 处方之前或之后使用多西他赛、卡巴西他赛、米托蒽醌和西普利昔单抗的 mCRPC 患者。我们仅纳入了非裔美国人和白人。我们总共分析了2886名在2014年至2017年期间接受过阿比特龙或恩杂鲁胺一线治疗的mCRPC退伍军人,他们的随访将持续到2020年。我们采用因果生存森林进行分析。分析单位为患者。本研究的主要结果是随访天数,表示在一线药物治疗期间的存活时间。估计治疗效果后,构建了处方政策树:在2886名退伍军人中,恩杂鲁胺比阿比特龙的平均生存天数多59.94天(95% CI 35.60-84.28)。这两种药物增加的总生存期因患者人口统计学、检测结果和合并症而异。通过对患者的增强反倾向加权(AIPW)得分进行排序,确定了两个数据驱动的患者亚组。两个亚组的平均 AIPW 得分为 19.36(95% CI -16.93-55.65)和 100.68(95% CI 62.46-138.89)。根据可视化和 t 检验,低分组和高分组的 AIPW 评分具有显著性(P=.003),因此支持异质性。分析结果显示,根据医生在开处方时掌握的几个协变量,为 2 种 ARPI 制定了一套处方规则:这项对2886名退伍军人进行的研究显示了异质性的证据,某些mCRPC患者的生存天数可能会根据处方药物的不同而有所改善。研究结果表明,根据医生在开处方时掌握的患者特征、实验室检查结果和合并症制定处方规则,可以提供个性化的治疗决策,从而提高患者的生存率。
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Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

Background: Prostate cancer is the second leading cause of death among American men. If detected and treated at an early stage, prostate cancer is often curable. However, an advanced stage such as metastatic castration-resistant prostate cancer (mCRPC) has a high risk of mortality. Multiple treatment options exist, the most common included docetaxel, abiraterone, and enzalutamide. Docetaxel is a cytotoxic chemotherapy, whereas abiraterone and enzalutamide are androgen receptor pathway inhibitors (ARPI). ARPIs are preferred over docetaxel due to lower toxicity. No study has used machine learning with patients' demographics, test results, and comorbidities to identify heterogeneous treatment rules that might improve the survival duration of patients with mCRPC.

Objective: This study aimed to measure patient-level heterogeneity in the association of medication prescribed with overall survival duration (in the form of follow-up days) and arrive at a set of medication prescription rules using patient demographics, test results, and comorbidities.

Methods: We excluded patients with mCRPC who were on docetaxel, cabaxitaxel, mitoxantrone, and sipuleucel-T either before or after the prescription of an ARPI. We included only the African American and white populations. In total, 2886 identified veterans treated for mCRPC who were prescribed either abiraterone or enzalutamide as the first line of treatment from 2014 to 2017, with follow-up until 2020, were analyzed. We used causal survival forests for analysis. The unit level of analysis was the patient. The primary outcome of this study was follow-up days indicating survival duration while on the first-line medication. After estimating the treatment effect, a prescription policy tree was constructed.

Results: For 2886 veterans, enzalutamide is associated with an average of 59.94 (95% CI 35.60-84.28) more days of survival than abiraterone. The increase in overall survival duration for the 2 drugs varied across patient demographics, test results, and comorbidities. Two data-driven subgroups of patients were identified by ranking them on their augmented inverse-propensity weighted (AIPW) scores. The average AIPW scores for the 2 subgroups were 19.36 (95% CI -16.93 to 55.65) and 100.68 (95% CI 62.46-138.89). Based on visualization and t test, the AIPW score for low and high subgroups was significant (P=.003), thereby supporting heterogeneity. The analysis resulted in a set of prescription rules for the 2 ARPIs based on a few covariates available to the physicians at the time of prescription.

Conclusions: This study of 2886 veterans showed evidence of heterogeneity and that survival days may be improved for certain patients with mCRPC based on the medication prescribed. Findings suggest that prescription rules based on the patient characteristics, laboratory test results, and comorbidities available to the physician at the time of prescription could improve survival by providing personalized treatment decisions.

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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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