Literature DB >> 28284255

A systematic review of prediction models for prevalent pulmonary tuberculosis in adults.

S S Van Wyk1, H H Lin2, M M Claassens1.   

Abstract

A systematic review was conducted to describe the quality and characteristics of prediction models for prevalent pulmonary tuberculosis (PTB) in adults at routine TB care settings. A prediction model was defined as the combination of two or more clinical predictors designed to estimate the probability of having TB. Studies using culture-confirmed PTB as reference standard were included. Models for in-patients, children or specific patient populations were excluded. PubMed, Scopus and the Cochrane Library and abstracts from the International Union Against Tuberculosis and Lung Disease, American Thoracic Society and European Respiratory Society conferences were searched. The CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist was used for data extraction and quality assessment. From 13 671 identified records, six were included for data extraction; three assessed smear-negative, culture-positive PTB as outcome and three focused on human immunodeficiency virus infected individuals only. Reporting of model development, performance and evaluation was poor. In four studies, predictive performance was evaluated using the development data set (apparent performance), one study did an internal validation and one study did an external validation. Results were not pooled due to heterogeneity. Existing prediction models for estimating prevalent PTB in adults at primary care level are poorly reported and validated and are not useful for TB screening. The World Health Organization symptom screen is recommended.

Entities:  

Mesh:

Year:  2017        PMID: 28284255     DOI: 10.5588/ijtld.16.0059

Source DB:  PubMed          Journal:  Int J Tuberc Lung Dis        ISSN: 1027-3719            Impact factor:   2.373


  11 in total

1.  Study of the association of interferon-γ gene polymorphisms and Th1/Th2 balance in tuberculosis susceptibility.

Authors:  Qiuping Wu; Yuanjiang Huang; Yun Zhou; Guizhong Zhou; Haifeng Wu; Jing He
Journal:  Am J Transl Res       Date:  2021-05-15       Impact factor: 4.060

2.  A comparison of the yield and relative cost of active tuberculosis case-finding algorithms in Zimbabwe.

Authors:  S M Machekera; E Wilkinson; S G Hinderaker; M Mabhala; C Zishiri; R T Ncube; C Timire; K C Takarinda; T Sengai; C Sandy
Journal:  Public Health Action       Date:  2019-06-21

Review 3.  Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults.

Authors:  Lauren S Peetluk; Felipe M Ridolfi; Peter F Rebeiro; Dandan Liu; Valeria C Rolla; Timothy R Sterling
Journal:  BMJ Open       Date:  2021-03-02       Impact factor: 2.692

4.  Development and validation of a prediction model for active tuberculosis case finding among HIV-negative/unknown populations.

Authors:  Yun-Ju Shih; Helen Ayles; Knut Lönnroth; Mareli Claassens; Hsien-Ho Lin
Journal:  Sci Rep       Date:  2019-04-16       Impact factor: 4.379

5.  Risk score for predicting mortality including urine lipoarabinomannan detection in hospital inpatients with HIV-associated tuberculosis in sub-Saharan Africa: Derivation and external validation cohort study.

Authors:  Ankur Gupta-Wright; Elizabeth L Corbett; Douglas Wilson; Joep J van Oosterhout; Keertan Dheda; Helena Huerga; Jonny Peter; Maryline Bonnet; Melanie Alufandika-Moyo; Daniel Grint; Stephen D Lawn; Katherine Fielding
Journal:  PLoS Med       Date:  2019-04-05       Impact factor: 11.069

6.  Models for estimating and projecting global, regional and national prevalence and disease burden of asthma: a systematic review.

Authors:  Mohammad Romel Bhuia; Md Atiqul Islam; Bright I Nwaru; Christopher J Weir; Aziz Sheikh
Journal:  J Glob Health       Date:  2020-12-30       Impact factor: 4.413

7.  Sensitivity and specificity of tuberculosis signs and symptoms screening and adjunct role of social pathology characteristics in predicting bacteriologically confirmed tuberculosis in Myanmar.

Authors:  Kyaw Ko Ko Htet; Virasakdi Chongsuvivatwong; Si Thu Aung
Journal:  Trop Med Health       Date:  2021-01-07

8.  Novel Long Non-coding RNA and LASSO Prediction Model to Better Identify Pulmonary Tuberculosis: A Case-Control Study in China.

Authors:  Zirui Meng; Minjin Wang; Shuo Guo; Yanbing Zhou; Mengyuan Lyu; Xuejiao Hu; Hao Bai; Qian Wu; Chuanmin Tao; Binwu Ying
Journal:  Front Mol Biosci       Date:  2021-05-25

9.  Two Clinical Prediction Tools to Improve Tuberculosis Contact Investigation.

Authors:  Ruoran Li; Francesco Nordio; Chuan-Chin Huang; Carmen Contreras; Roger Calderon; Rosa Yataco; Jerome T Galea; Zibiao Zhang; Mercedes C Becerra; Leonid Lecca; Megan B Murray
Journal:  Clin Infect Dis       Date:  2020-11-05       Impact factor: 9.079

10.  A clinical score for identifying active tuberculosis while awaiting microbiological results: Development and validation of a multivariable prediction model in sub-Saharan Africa.

Authors:  Yeonsoo Baik; Hannah M Rickman; Colleen F Hanrahan; Lesego Mmolawa; Peter J Kitonsa; Tsundzukana Sewelana; Annet Nalutaaya; Emily A Kendall; Limakatso Lebina; Neil Martinson; Achilles Katamba; David W Dowdy
Journal:  PLoS Med       Date:  2020-11-10       Impact factor: 11.069

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.