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相关概念视频

Acute Pyelonephritis II: Diagnostic Studies and Management01:28

Acute Pyelonephritis II: Diagnostic Studies and Management

Introduction:For diagnosing acute pyelonephritis, a comprehensive patient history is collected to identify symptoms such as dysuria, frequent or urgent urination, flank pain, or costovertebral angle (CVA) tenderness that may suggest a kidney infection.Physical ExaminationDuring the physical examination, CVA tenderness is assessed. This involves gentle percussion over the costovertebral angle, where tenderness often indicates a kidney infection.Diagnostic TestsUrinalysis: Used to identify white...
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相关实验视频

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Standardized Colon Ascendens Stent Peritonitis in Rats - a Simple, Feasible Animal Model to Induce Septic Acute Kidney Injury
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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
PubMed
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.

关键词:
早期干预是早期的干预.机器学习是机器学习.通过皮肤进行神经质切除术.败血症 这是一种败血症.尿路结石 尿路结石

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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.