一种使用机器学习的新型皮肤穿透后利切除术败血症预测模型
Rong Shen1, Shaoxiong Ming1, Wei Qian2
1Department of Urology, Shanghai Changhai Hospital, No.168 Changhai Rd, Shanghai, 200433, China.
BMC urology
|February 2, 2024
概括
这项研究开发了一种机器学习模型,用于预测皮肤穿神经切除术 (PCNL) 后的败血症. 该模型识别了高风险患者,使早期干预能够减少败血症发病率.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
背景情况:
- 穿皮骨切除术 (PCNL) 是对结石的一种常见手术.
- 败血症是PCNL后的严重并发症,需要主动识别风险.
- 机器学习为开发外科手术结果预测模型提供了潜力.
研究的目的:
- 创建一个基于机器学习的预测模型,用于PCNL后的败血症.
- 为了识别PCNL后患有败血症的高风险患者.
- 促进泌尿科医生的早期诊断和干预.
主要方法:
- 对694名接受PCNL治疗的患者进行了回顾性分析.
- 使用22个手术前和手术内参数开发机器学习模型.
- 100倍的蒙特卡洛交叉验证,80%的培训和20%的验证.
主要成果:
- 在694名患者中,有45名患者发生败血症.
- 预测模型表现出强的性能:AUC=0.89,敏感度为87.8%,特异性为86.9%,准确度为87.4%.
- 关键预测因素包括手术前尿液培养,性别,抗生素使用,白细胞计数和石头特征.
结论:
- 开发的预测模型是有效估计PCNL后的败血症风险.
- 以这种模式为指导的早期干预可能会减少败血症发病率.
- 该工具支持泌尿科医生管理患有术后败血症风险的患者.
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