Diagnosis of neurosyphilis in HIV-negative patients with syphilis: development, validation, and clinical utility of a suite of machine learning models

Zou, Huachun; Lu, Zhen; Weng, Wenjia; Yang, Ligang; Yang, Luoyao; Leng, Xinying; Wang, Junfeng; Lin, Yifan; Fu, Leiwen; Zhang, Xiao-Hui; Li, Yuwei; Wang, Liuyuan; Wu, Xinsheng; Zhou, Xinyi; Tian, Tian; Huang, Lixia; Marra, Christina M.; Yang, Bin; Yang, Tian‐Ci; Ke, Wujian

doi:10.1016/j.eclinm.2023.102080

Cited by 4 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, machine learning has been developing rapidly, which has been widely used in predicting human diseases (23,24), recognizing medical images (25,26) cohort (AUC=0.824) and the external validation cohort (AUC=0.74). These results indicated that the model had significant value in accurately evaluating the probability of severe COVID-19 occurring in elderly patients on an individual basis.…”

Section: Discussionmentioning

confidence: 99%

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

Zhuang,

Qi,

Yao

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

ObjectiveDue to the increased likelihood of progression of severe pneumonia, the mortality rate of the elderly infected with coronavirus disease 2019 (COVID-19) is high. However, there is a lack of models based on immunoglobulin G (IgG) subtypes to forecast the severity of COVID-19 in elderly individuals. The objective of this study was to create and verify a new algorithm for distinguishing elderly individuals with severe COVID-19.MethodsIn this study, laboratory data were gathered from 103 individuals who had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using a retrospective analysis. These individuals were split into training (80%) and testing cohort (20%) by using random allocation. Furthermore, 22 COVID-19 elderly patients from the other two centers were divided into an external validation cohort. Differential indicators were analyzed through univariate analysis, and variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression. The severity of elderly patients with COVID-19 was predicted using a combination of five machine learning algorithms. Area under the curve (AUC) was utilized to evaluate the performance of these models. Calibration curves, decision curves analysis (DCA), and Shapley additive explanations (SHAP) plots were utilized to interpret and evaluate the model.ResultsThe logistic regression model was chosen as the best machine learning model with four principal variables that could predict the probability of COVID-19 severity. In the training cohort, the model achieved an AUC of 0.889, while in the testing cohort, it obtained an AUC of 0.824. The calibration curve demonstrated excellent consistency between actual and predicted probabilities. According to the DCA curve, it was evident that the model provided significant clinical advantages. Moreover, the model performed effectively in an external validation group (AUC=0.74).ConclusionThe present study developed a model that can distinguish between severe and non-severe patients of COVID-19 in the elderly, which might assist clinical doctors in evaluating the severity of COVID-19 and reducing the bad outcomes of elderly patients.

show abstract

Section: Discussionmentioning

confidence: 99%