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炎症指数和机器学习算法用于预测尿路整形成功率

Emre Tokuc¹, Mithat Eksi², Ridvan Kayar³

¹Urology Clinic, Haydarpasa Numune SUAM, University of Health Sciences, Istanbul, Türkiye. emretokuc@gmail.com.

Investigative and clinical urology

|May 7, 2024

概括

血液学炎症标志物,特别是泛免疫炎症值 (PIV),可以预测尿道收缩在尿路整形术后的复发. 与传统方法相比,机器学习算法显著提高了预测准确性.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
炎症标记物炎症标记物
医疗信息学医疗信息学

背景情况:

在初级尿路整形术后尿道狭窄的复发仍然是一个临床挑战.
血液炎症标志物越来越多地被探索,因为它们在各种条件下具有预后价值.

研究的目的:

评估血液学炎症标志物的预测能力,以评估尿道狭窄性复发后的尿道狭窄性复发.
将传统统计模型的性能与用于预测复发的机器学习算法进行比较.

主要方法:

分析了一组287名接受初级尿路整形手术的患者.
收集的数据包括患者人口统计学,并发症,血液炎症标志物 (例如PLR,SII,PIV) 和收缩特征.
患者被跟踪了一年,以评估复发情况,使用后勤回归和机器学习进行分析.

主要成果:

在复发和非复发组之间观察到狭窄长度,局部和炎症标志物 (PLR,SII,PIV) 的显著差异.
多变量分析确定了收缩长度和PIV作为复发的重要预测因素.
机器学习算法表现出优异的预测性能 (AUC 0.82),与经典后勤回归 (AUC 0.65) 相比.

结论:

全免疫-炎症值 (PIV) 在预测尿道收缩在尿道整形术后的复发方面表现有前途.
机器学习算法为预测尿路整形结果提供了更高的准确性,可能有助于个性化治疗策略和名ogram开发.

关键词:

人工智能的人工智能是人工智能.生物标志物生物标志物尿道是什么意思尿道是什么意思尿道狭窄症是什么意思

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