乳腺癌患者的化疗诱导的恶心和吐:使用分类和回归树 (CART) 的风险因素和预测模型
Bryant Ng1, Yufi Kartika Astari2, Juan Adrian Wiranata3
1Medicine Study Program, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, IDN.
Cureus
|September 4, 2023
概括
乳腺癌患者体重不足至正常的BMI在第一轮治疗期间面临更高的化学疗法诱导的恶心和吐 (CINV) 风险. 一个分类和回归树 (CART) 模型可以帮助识别不太可能患上CINV的患者,帮助个性化护理.
科学领域:
- 在瘤学瘤学.
- 临床药理学 临床药理学
- 生物统计学 生物统计学
背景情况:
- 化疗诱导的恶心和吐 (CINV) 是影响乳腺癌患者治疗经验的显著不良影响.
- 了解早期CINV发病率对于管理预期性恶心和吐至关重要.
- 识别第一周期CINV的风险因素对于主动的患者管理至关重要.
研究的目的:
- 在乳腺癌患者中研究与第一周期CINV相关的风险因素.
- 开发和评估一个分类和回归树 (CART) 模型,用于预测第一周期CINV.
主要方法:
- 一个横截面研究嵌入在137名女性乳腺癌患者接受高度排泄性化疗的前性队列中.
- 使用常见不良事件毒性标准 (CTCAE) 版本4.0.0对CINV的评估.
- 统计分析包括千平方,双变量和多变量逻辑回归以及CART分析.
主要成果:
- 第一个周期中CINV的发病率为43.1%.
- 保险状况和诊断时的阶段与CINV显著相关 (p<0.001).
- 体重不足至正常体重指数 (BMI) 的患者在第一周期中患有CINV的风险增加 (OR = 2.17,p = 0.041).
- 该CART模型确定了诊断阶段,BMI和年龄作为关键预测因素,在识别不会发展CINV的患者时,达到61.3%的准确性和强的特异性 (85.9%).
结论:
- 体重低于正常的BMI是乳腺癌患者第一周期CINV的重要风险因素.
- 开发的CART模型在预测不太可能经历CINV的患者方面具有实用性,促进了个性化护理策略.
- 卡特模型提供了一个潜在的简单和有效的工具,用于个性化对第一周期CINV患者的管理.
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