构建基于机器学习的皮多格雷尔耐药性风险预测模型
Ruo-Ying Wang1, Shui-Di Yan1, Jian-Qi Zeng2
1Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
机器学习使用临床数据预测皮多格勒耐药性风险. 该模型识别了可能抵抗克洛皮多格雷尔的患者,改善了心血管疾病的治疗策略.
科学领域:
- 心脏病学 心脏病学
- 药物基因组学 药物基因组学
- 生物医学信息学 生物医学信息学
背景情况:
- 克洛皮多格勒对于预防动脉循环障碍至关重要,但表现出不可预测的抗血小板功效.
- 克洛皮多格雷尔反应的个体变化需要预测工具,以实现最佳的患者管理.
研究的目的:
- 开发和验证一种机器学习模型,用于预测克洛皮多格雷尔耐药性风险.
- 确定与克洛皮多格勒耐药性相关的关键临床和实验室指标.
主要方法:
- 分析了1592名用克洛皮多格雷尔治疗的心血管疾病患者的队列.
- 拉索和多变量逻辑回归用于从临床,实验室和遗传数据中进行特征选择.
- 评估了后勤回归,LGBM,随机森林和SVC模型,并为最终预测模型选择了随机森林分类器.
主要成果:
- 克洛皮多格雷尔耐药性的关键预测变量包括白细胞计数,血红蛋白,血小板计数,纤维素,甘油三,D-Dimer,平均血小板体积,前热血素时间比,尿酸,糖化血红蛋白和阿波利波蛋白B.
- 随机森林分类器模型实现了0.8730的AUC和0.8033.3的准确性.
- 从2020年到2022年,克洛皮多格雷尔耐药率的年比年显著增加.
结论:
- 开发了一个强大的机器学习模型来预测克洛皮多格雷尔耐药性风险.
- 该模型利用现有的临床数据,可以帮助临床医生做出个性化治疗决策.
- 改善克洛皮多格雷尔耐药性的预测可以为心血管患者提供更有效的治疗策略.
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