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基于临床参数的神经梅毒风险分层预测模型

Clinical parameter-based prediction model for neurosyphilis risk stratification.

作者信息

Yang Yilan, Gu Xin, Zhu Lin, Cheng Yuanyuan, Lu Haikong, Guan Zhifang, Shi Mei, Ni Liyan, Peng Ruirui, Zhao Wei, Wu Juan, Qi Tengfei, Long Fuquan, Chai Zhe, Gong Weiming, Ye Meiping, Zhou Pingyu

机构信息

Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China.

出版信息

Epidemiol Infect. 2024 Jan 15;152:e21. doi: 10.1017/S0950268824000074.

Abstract

Accurately predicting neurosyphilis prior to a lumbar puncture (LP) is critical for the prompt management of neurosyphilis. However, a valid and reliable model for this purpose is still lacking. This study aimed to develop a nomogram for the accurate identification of neurosyphilis in patients with syphilis. The training cohort included 9,504 syphilis patients who underwent initial neurosyphilis evaluation between 2009 and 2020, while the validation cohort comprised 526 patients whose data were prospectively collected from January 2021 to September 2021. Neurosyphilis was observed in 35.8% (3,400/9,504) of the training cohort and 37.6% (198/526) of the validation cohort. The nomogram incorporated factors such as age, male gender, neurological and psychiatric symptoms, serum RPR, a mucous plaque of the larynx and nose, a history of other STD infections, and co-diabetes. The model exhibited good performance with concordance indexes of 0.84 (95% CI, 0.83-0.85) and 0.82 (95% CI, 0.78-0.86) in the training and validation cohorts, respectively, along with well-fitted calibration curves. This study developed a precise nomogram to predict neurosyphilis risk in syphilis patients, with potential implications for early detection prior to an LP.

摘要

在进行腰椎穿刺(LP)之前准确预测神经梅毒对于神经梅毒的及时治疗至关重要。然而,目前仍缺乏用于此目的的有效且可靠的模型。本研究旨在开发一种列线图,用于准确识别梅毒患者中的神经梅毒。训练队列包括2009年至2020年间接受初始神经梅毒评估的9504例梅毒患者,而验证队列包括2021年1月至2021年9月前瞻性收集数据的526例患者。训练队列中35.8%(3400/9504)的患者和验证队列中37.6%(198/526)的患者被观察到患有神经梅毒。该列线图纳入了年龄、男性性别、神经和精神症状、血清RPR、喉和鼻黏膜斑、其他性传播疾病感染史以及合并糖尿病等因素。该模型在训练队列和验证队列中的一致性指数分别为0.84(95%CI,0.83 - 0.85)和0.82(95%CI,0.78 - 0.86),表现良好,同时校准曲线拟合良好。本研究开发了一种精确的列线图来预测梅毒患者的神经梅毒风险,对在LP之前进行早期检测具有潜在意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cda0/10894895/f9a738f6b109/S0950268824000074_fig1.jpg

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