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

OBJECTIVE: To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB. DESIGN: Systematic review. DATA SOURCES: PubMed, Embase, Web of Science and Google Scholar were searched for studies published from...

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Autores principales: Peetluk, Lauren S., Ridolfi, Felipe M., Rebeiro, Peter F., Liu, Dandan, Rolla, Valeria C, Sterling, Timothy R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929865/
https://www.ncbi.nlm.nih.gov/pubmed/33653759
http://dx.doi.org/10.1136/bmjopen-2020-044687
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author Peetluk, Lauren S.
Ridolfi, Felipe M.
Rebeiro, Peter F.
Liu, Dandan
Rolla, Valeria C
Sterling, Timothy R.
author_facet Peetluk, Lauren S.
Ridolfi, Felipe M.
Rebeiro, Peter F.
Liu, Dandan
Rolla, Valeria C
Sterling, Timothy R.
author_sort Peetluk, Lauren S.
collection PubMed
description OBJECTIVE: To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB. DESIGN: Systematic review. DATA SOURCES: PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020. STUDY SELECTION AND DATA EXTRACTION: Studies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures. RESULTS: 14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis. CONCLUSIONS: TB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models. TRIAL REGISTRATION: The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)
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spelling pubmed-79298652021-03-19 Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults Peetluk, Lauren S. Ridolfi, Felipe M. Rebeiro, Peter F. Liu, Dandan Rolla, Valeria C Sterling, Timothy R. BMJ Open Infectious Diseases OBJECTIVE: To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB. DESIGN: Systematic review. DATA SOURCES: PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020. STUDY SELECTION AND DATA EXTRACTION: Studies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures. RESULTS: 14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis. CONCLUSIONS: TB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models. TRIAL REGISTRATION: The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782) BMJ Publishing Group 2021-03-02 /pmc/articles/PMC7929865/ /pubmed/33653759 http://dx.doi.org/10.1136/bmjopen-2020-044687 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Infectious Diseases
Peetluk, Lauren S.
Ridolfi, Felipe M.
Rebeiro, Peter F.
Liu, Dandan
Rolla, Valeria C
Sterling, Timothy R.
Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
title Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
title_full Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
title_fullStr Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
title_full_unstemmed Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
title_short Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
title_sort systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults
topic Infectious Diseases
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929865/
https://www.ncbi.nlm.nih.gov/pubmed/33653759
http://dx.doi.org/10.1136/bmjopen-2020-044687
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