Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis
Issued Date
2024-09-01
Resource Type
ISSN
00029637
eISSN
14761645
Scopus ID
2-s2.0-85203253090
Pubmed ID
39013385
Journal Title
American Journal of Tropical Medicine and Hygiene
Volume
111
Issue
3
Start Page
546
End Page
553
Rights Holder(s)
SCOPUS
Bibliographic Citation
American Journal of Tropical Medicine and Hygiene Vol.111 No.3 (2024) , 546-553
Suggested Citation
Stadelman-Behar A.M., Tiffin N., Ellis J., Creswell F.V., Ssebambulidde K., Nuwagira E., Richards L., Lutje V., Hristea A., Jipa R.E., Vidal J.E., Azevedo R.G.S., de Almeida S.M., Kussen G.B., Nogueira K., Gualberto F.A.S., Metcalf T., Heemskerk A.D., Dendane T., Khalid A., Zeggwagh A.A., Bateman K., Siebert U., Rochau U., van Laarhoven A., van Crevel R., Ganiem A.R., Dian S., Jarvis J., Donovan J., Thuong T.N.T., Thwaites G.E., Bahr N.C., Meya D.B., Boulware D.R., Boyles T.H. Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis. American Journal of Tropical Medicine and Hygiene Vol.111 No.3 (2024) , 546-553. 553. doi:10.4269/ajtmh.23-0789 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101182
Title
Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis
Author(s)
Stadelman-Behar A.M.
Tiffin N.
Ellis J.
Creswell F.V.
Ssebambulidde K.
Nuwagira E.
Richards L.
Lutje V.
Hristea A.
Jipa R.E.
Vidal J.E.
Azevedo R.G.S.
de Almeida S.M.
Kussen G.B.
Nogueira K.
Gualberto F.A.S.
Metcalf T.
Heemskerk A.D.
Dendane T.
Khalid A.
Zeggwagh A.A.
Bateman K.
Siebert U.
Rochau U.
van Laarhoven A.
van Crevel R.
Ganiem A.R.
Dian S.
Jarvis J.
Donovan J.
Thuong T.N.T.
Thwaites G.E.
Bahr N.C.
Meya D.B.
Boulware D.R.
Boyles T.H.
Tiffin N.
Ellis J.
Creswell F.V.
Ssebambulidde K.
Nuwagira E.
Richards L.
Lutje V.
Hristea A.
Jipa R.E.
Vidal J.E.
Azevedo R.G.S.
de Almeida S.M.
Kussen G.B.
Nogueira K.
Gualberto F.A.S.
Metcalf T.
Heemskerk A.D.
Dendane T.
Khalid A.
Zeggwagh A.A.
Bateman K.
Siebert U.
Rochau U.
van Laarhoven A.
van Crevel R.
Ganiem A.R.
Dian S.
Jarvis J.
Donovan J.
Thuong T.N.T.
Thwaites G.E.
Bahr N.C.
Meya D.B.
Boulware D.R.
Boyles T.H.
Author's Affiliation
Faculty of Tropical Medicine, Mahidol University
Oxford University Clinical Research Unit
Botswana Harvard AIDS Institute Partnership
Brighton and Sussex Medical School
University of the Witwatersrand Faculty of Health Sciences
Mbarara University of Science and Technology
School of Medicine, Makerere University College of Health Sciences
Makerere University
Universitas Padjadjaran
Instituto de Infectologia Emilio Ribas
Universitatea de Medicina si Farmacie Carol Davila din Bucuresti
Harvard T.H. Chan School of Public Health
University of the Western Cape
London School of Hygiene & Tropical Medicine
Helen Joseph Hospital
University of Kansas School of Medicine
University of Washington
Universidade Federal do Parana
School of Public Health
Ibn Sina Hospital, Agdal Rabat
Nuffield Department of Medicine
University of Minnesota Medical School
Universidade de São Paulo
Groote Schuur Hospital
Harvard Medical School
Radboud University Medical Center
Amsterdam UMC - University of Amsterdam
University of Cape Town
UMIT TIROL - University for Health Sciences and Technology
MRC/UVRI and LSHTM Uganda Research Unit
Center for Reference and Training in STD/AIDS CRT DST/AIDS
Cochrane Infectious Diseases Group
Oxford University Clinical Research Unit
Botswana Harvard AIDS Institute Partnership
Brighton and Sussex Medical School
University of the Witwatersrand Faculty of Health Sciences
Mbarara University of Science and Technology
School of Medicine, Makerere University College of Health Sciences
Makerere University
Universitas Padjadjaran
Instituto de Infectologia Emilio Ribas
Universitatea de Medicina si Farmacie Carol Davila din Bucuresti
Harvard T.H. Chan School of Public Health
University of the Western Cape
London School of Hygiene & Tropical Medicine
Helen Joseph Hospital
University of Kansas School of Medicine
University of Washington
Universidade Federal do Parana
School of Public Health
Ibn Sina Hospital, Agdal Rabat
Nuffield Department of Medicine
University of Minnesota Medical School
Universidade de São Paulo
Groote Schuur Hospital
Harvard Medical School
Radboud University Medical Center
Amsterdam UMC - University of Amsterdam
University of Cape Town
UMIT TIROL - University for Health Sciences and Technology
MRC/UVRI and LSHTM Uganda Research Unit
Center for Reference and Training in STD/AIDS CRT DST/AIDS
Cochrane Infectious Diseases Group
Corresponding Author(s)
Other Contributor(s)
Abstract
No accurate and rapid diagnostic test exists for tuberculous meningitis (TBM), leading to delayed diagnosis. We leveraged data from multiple studies to improve the predictive performance of diagnostic models across different populations, settings, and subgroups to develop a new predictive tool for TBM diagnosis. We conducted a systematic review to analyze eligible datasets with individual-level participant data (IPD). We imputed missing data and explored three approaches: stepwise logistic regression, classification and regression tree (CART), and random forest regression. We evaluated performance using calibration plots and C-statistics via internal-external cross-validation. We included 3,761 individual participants from 14 studies and nine countries. A total of 1,240 (33%) participants had "definite"(30%) or "probable"(3%) TBM by case definition. Important predictive variables included cerebrospinal fluid (CSF) glucose, blood glucose, CSF white cell count, CSF differential, cryptococcal antigen, HIV status, and fever presence. Internal validation showed that performance varied considerably between IPD datasets with C-statistic values between 0.60 and 0.89. In external validation, CART performed the worst (C = 0.82), and logistic regression and random forest had the same accuracy (C = 0.91). We developed a mobile app for TBM clinical prediction that accounted for heterogeneity and improved diagnostic performance (https://tbmcalc.github.io/tbmcalc). Further external validation is needed.
