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在创伤性脑损伤患者中急性损伤的分类和回归树预测模型
Ruoran Wang1, Jing Zhang1, Min He2
1Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
Therapeutics and clinical risk management
|February 27, 2024
概括
决策树模型有效预测创伤性脑损伤 (TBI) 患者的急性损伤 (AKI). 这种工具有助于医生识别高风险个体,及时进行干预.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 神经学 神经学
- 关键护理医学 关键护理医学
背景情况:
- 急性损伤 (AKI) 是患有创伤性脑损伤 (TBI) 的住院患者的常见并发症.
- 在TBI患者中的AKI与增加的发病率和死亡率有关.
- 需要预测模型来识别高风险的TBI患者.
研究的目的:
- 开发一种基于决策树的可视和方便的模型,用于预测TBI患者的AKI.
- 将决策树模型的预测精度与LASSO回归进行比较.
主要方法:
- 分析了376名TBI患者的队列.
- 数据包括人口统计,生命体征,实验室结果和治疗方法.
- AKI预测模型是使用分类和回归树 (CART) 和LASSO回归构建的.
- 使用接收器操作特征 (ROC) 曲线和曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 在TBI患者中,AKI的发生率为10.4%.
- 拉索回归确定了葡萄糖,血清肌素,囊素C,血清尿酸和新鲜冷血作为预测因素.
- 决策树模型突出显示了葡萄糖,血清尿酸和cystatin C.
- 决策树模型的AUC值为0.892,略高于LASSO模型的0.854.
结论:
- 决策树模型是预测TBI患者AKI的一个有价值的工具.
- 基于树的视觉流程图有助于临床医生识别有风险的患者.
- 早期识别有助于制定TBI中AKI的适当治疗策略.
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