Tom Boyles1,2, Anna Stadelman3, Jayne P Ellis4, Fiona V Cresswell5,6, Vittoria Lutje7, Sean Wasserman8, Nicki Tiffin8,9, Robert Wilkinson8,10,11. 1. Wits Reproductive Health and HIV Institute, University of the Witwatersrand, Johannesburg, Gauteng, 2001, South Africa. 2. Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK. 3. School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA. 4. Hospital for Tropical Diseases, University College London Hospitals NHS Foundation Trust, London, UK. 5. Clinical Research Department, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK. 6. Research Department, Infectious Diseases Institute, Kampala, Uganda. 7. Cochrane Infectious Diseases Group, University of Liverpool, Liverpool, UK. 8. Wellcome Centre for Infectious Disease Research in Africa, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, South Africa. 9. Division of Computational Biology, Integrative Biomedical Sciences, University of Cape Town, University of Cape, South Africa. 10. Department of Medicine, Imperial College London, London, UK. 11. The Francis Crick Institute, London, UK.
Abstract
Background: Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor outcome, is caused in part by lack of availability of diagnostic tests that are both rapid and accurate. Several attempts have been made to develop clinical scoring systems to fill this gap, but none have performed sufficiently well to be broadly implemented. We aim to identify and validate a set of clinical predictors that accurately classify TBM using individual patient data (IPD) from published studies. Methods: We will perform a systematic review and obtain IPD from studies published from the year 1990 which undertook diagnostic testing for TBM in adolescents or adults using at least one of, microscopy for acid-fast bacilli, commercial nucleic acid amplification test for Mycobacterium tuberculosis or mycobacterial culture of cerebrospinal fluid. Clinical data that have previously been shown to be associated with TBM, and can inform the final diagnosis, will be requested. The data-set will be divided into training and test/validation data-sets for model building. A predictive logistic model will be built using a training set with patients with definite TBM and no TBM. Should it be warranted, factor analysis may be employed, depending on evidence for multicollinearity or the case for including latent variables in the model. Discussion: We will systematically identify and extract key clinical parameters associated with TBM from published studies and use a 'big data' approach to develop and validate a clinical prediction model with enhanced generalisability. The final model will be made available through a smartphone application. Further work will be external validation of the model and test of efficacy in a randomised controlled trial. Copyright:
Background: Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor outcome, is caused in part by lack of availability of diagnostic tests that are both rapid and accurate. Several attempts have been made to develop clinical scoring systems to fill this gap, but none have performed sufficiently well to be broadly implemented. We aim to identify and validate a set of clinical predictors that accurately classify TBM using individual patient data (IPD) from published studies. Methods: We will perform a systematic review and obtain IPD from studies published from the year 1990 which undertook diagnostic testing for TBM in adolescents or adults using at least one of, microscopy for acid-fast bacilli, commercial nucleic acid amplification test for Mycobacterium tuberculosis or mycobacterial culture of cerebrospinal fluid. Clinical data that have previously been shown to be associated with TBM, and can inform the final diagnosis, will be requested. The data-set will be divided into training and test/validation data-sets for model building. A predictive logistic model will be built using a training set with patients with definite TBM and no TBM. Should it be warranted, factor analysis may be employed, depending on evidence for multicollinearity or the case for including latent variables in the model. Discussion: We will systematically identify and extract key clinical parameters associated with TBM from published studies and use a 'big data' approach to develop and validate a clinical prediction model with enhanced generalisability. The final model will be made available through a smartphone application. Further work will be external validation of the model and test of efficacy in a randomised controlled trial. Copyright:
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