A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis.

Autor: Dong THK; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam. trinhdhk@oucru.org.; King's College London, London, UK. trinhdhk@oucru.org., Donovan J; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; London School of Hygiene and Tropical Medicine, London, UK., Ngoc NM; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; the Hospital of Tropical Diseases, Ho Chi Minh City, Vietnam., Thu DDA; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam., Nghia HDT; the Hospital of Tropical Diseases, Ho Chi Minh City, Vietnam.; Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam., Oanh PKN; the Hospital of Tropical Diseases, Ho Chi Minh City, Vietnam., Phu NH; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; School of Medicine, Vietnam National University, Ho Chi Minh City, Vietnam., Hang VTT; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam., Vinh Chau NV; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; the Hospital of Tropical Diseases, Ho Chi Minh City, Vietnam.; Ho Chi Minh City Department of Health, Ho Chi Minh City, Vietnam., Thuong Thuong NT; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK., Tan LV; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK., Thwaites GE; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK., Geskus RB; Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Jazyk: angličtina
Zdroj: BMC infectious diseases [BMC Infect Dis] 2024 Feb 06; Vol. 24 (1), pp. 163. Date of Electronic Publication: 2024 Feb 06.
DOI: 10.1186/s12879-024-08992-z
Abstrakt: Background: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known.
Methods: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally.
Results: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden's Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively.
Conclusion: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.
(© 2024. The Author(s).)
Databáze: MEDLINE
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