Tuberculostearic acid incorporated predictive model contributes to the clinical diagnosis of tuberculous meningitis

Tsz Hei Fong; Wangpan Shi; Guohui Ruan; Siyi Li; Guanghui Liu; Leyun Yang; Kaibin Wu; Jingxian Fan; Chung Lam Ng; Yafang Hu; Haishan Jiang

doi:10.1016/j.isci.2023.107858

Tuberculostearic acid incorporated predictive model contributes to the clinical diagnosis of tuberculous meningitis

iScience. 2023 Sep 9;26(10):107858. doi: 10.1016/j.isci.2023.107858. eCollection 2023 Oct 20.

Authors

Affiliations

¹ Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
² The First Clinical Medical School, Southern Medical University, Guangzhou 510515, China.
³ School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
⁴ Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC 20057-1484, USA.

Abstract

The conventional confirmation tests of tuberculous meningitis (TBM) are usually low in sensitivity, leading to high TBM mortality. Hence, sensitive methods for indicating the presence of bacilli are required. Tuberculostearic acid (TBSA), a constituent from Mycobacterium tuberculosis had been evaluated as a promising marker, but fails to demonstrate consistent results for definite TBM. This study retrospectively reviewed medical records of 113 TBM suspects, constructing a TBSA-combined scoring system based on multiple factors, which show sensitivity and specificity of 0.8148 and 0.8814, respectively, and the area under the receiver operating characteristic curve of 0.9010. Multivariate analyses revealed four co-predictive factors strongly associated with TBSA: extra-neural tuberculosis, basal meningeal enhancement, CSF glucose/Serum glucose <0.595, and coinfection in CNS (Total). The subsequent machine learning-based validation showed correspondent importance to factors in the TBSA model. This study demonstrates a simple scoring system to facilitate TBM prediction, yield reliable diagnoses and allow timely treatment initiation.

Keywords: Clinical neuroscience; Medical biochemistry; Medical informatics.