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开发和验证用于预测PICC导管相关血流感染在患有血性恶性瘤的患者中的诺米克图

Qian Guo¹, Yuting Liu¹, Xiaoli Zhu¹

¹Department of Family Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310018, China.

BMC infectious diseases

|October 22, 2025

概括

这项研究确定了患有血液恶性瘤的患者与外围插入的中央导管 (PICC-CRBSI) 相关的血液感染的关键风险因素. 为预测PICC-CRBSI风险,开发了一种经过验证的名录,帮助制定有针对性的预防策略.

科学领域:

血液学血液学血液学
传染性疾病传染性疾病
医疗器械医疗器械

背景情况:

周围插入的中央导管 (PICC) 对血液性恶性瘤患者至关重要.
与PICC相关的血液感染 (PICC-CRBSI) 构成一个显著的风险.
预防需要有效的风险分层.

研究的目的:

在血液恶性瘤患者中确定PICC-CRBSI的独立风险因素.
开发和验证用于PICC-CRBSI风险评估的预测名录.
为PICC-CRBSI指导有针对性的预防策略.

主要方法:

在764名患有PICCs的血液性恶性瘤患者的回顾性队列研究中.
拉索回归和SMOTE-NC用于预测器识别.
多变量逻辑回归用于构建和验证名图,由AUC和DCA评估.

主要成果:

PICC-CRBSI发生在6.02%的患者中;格拉姆阴性细菌是主要原因.
确定了独立的风险因素:糖尿病史,年龄,PICC持续时间,插入尝试,导管直径/静脉,ANC,ALC和D-二次体.
命名图表显示了良好的校准和区分 (AUC 0.883培训,0.822验证).

结论:

一个经过验证的诺姆图有效地整合了PICC-CRBSI风险分层的患者,导管和生物标记因子.
这种工具可以个性化对血液恶性瘤的风险评估.
该名录可能有助于实施有针对性的预防战略.

关键词:

血液性恶性瘤血液性恶性瘤拉索回归法 (Lasso Regression) 是一种回归法.诺莫格拉姆斯 (Nomograms) 是一个名字.这是PICC-CRBSI的合作伙伴关系.风险预测模型的风险预测模型.在SMOTE中使用.

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