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Clinical parameter-based prediction model for neurosyphilis risk stratification

Published online by Cambridge University Press:  15 January 2024

Yilan Yang
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Xin Gu
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Lin Zhu
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Yuanyuan Cheng
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Haikong Lu
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Zhifang Guan
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Mei Shi
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Liyan Ni
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Ruirui Peng
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Wei Zhao
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Juan Wu
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Tengfei Qi
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Fuquan Long
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Zhe Chai
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Weiming Gong
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Meiping Ye
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
Pingyu Zhou*
Affiliation:
Institute of Sexually Transmitted Disease, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
*
Corresponding author: Pingyu Zhou; Email: zpyls@yahoo.com
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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.

Information

Type
Original Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press
Figure 0

Table 1. Baseline demographic and clinical characteristics of all patients

Figure 1

Table 2. Baseline demographic and clinical characteristics of the training and validation cohorts

Figure 2

Table 3. Univariate logistic regression analysis of correlated factors for neurosyphilis patients

Figure 3

Table 4. Multivariate logistic regression analysis of correlated factors for neurosyphilis patients

Figure 4

Figure 1. Development and performance of the nomogram to predict the risk of neurosyphilis. (a) Nomogram based on clinical factors; (b,c) calibration plot of the nomogram in the training (b) and validation cohorts (c). The 45° line in the plot indicates a perfect calibration that the predictive capability of the model perfectly matches the actual risk of neurosyphilis. The dotted line represents the performance of the nomogram, while the solid line corrects for any bias in the nomogram.

Figure 5

Table 5. Accuracy of the prediction score of the nomogram for estimating the risk of neurosyphilis

Figure 6

Figure 2. ROC curve of the nomogram in the training (a) and validation cohorts (b).

Figure 7

Figure 3. Decision curve analysis of the nomogram in the training (a) and validation cohorts (b). The x-axis denotes the threshold probability, while the y-axis denotes the net benefit. The green line represents that all syphilis patients developed neurosyphilis, the blue line represents that no syphilis patients developed neurosyphilis, and the red line represents the nomogram to predict neurosyphilis in patients with syphilis.

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