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A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis

BACKGROUND: Microbiological diagnosis of tuberculous spondylodiscitis (TS) and pyogenic spontaneous spondylodiscitis (PS) is sometime difficult. This study aimed to identify the predictive factors for differentiating TS from PS using clinical characteristics, radiologic findings, and biomarkers, and...

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Autores principales: Lertudomphonwanit, Thamrong, Somboonprasert, Chirtwut, Lilakhunakon, Kittiphon, Jaovisidha, Suphaneewan, Ruangchaijatuporn, Thumanoon, Fuangfa, Praman, Rattanasiri, Sasivimol, Watcharananan, Siriorn, Chanplakorn, Pongsthorn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437852/
https://www.ncbi.nlm.nih.gov/pubmed/37594939
http://dx.doi.org/10.1371/journal.pone.0290361
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author Lertudomphonwanit, Thamrong
Somboonprasert, Chirtwut
Lilakhunakon, Kittiphon
Jaovisidha, Suphaneewan
Ruangchaijatuporn, Thumanoon
Fuangfa, Praman
Rattanasiri, Sasivimol
Watcharananan, Siriorn
Chanplakorn, Pongsthorn
author_facet Lertudomphonwanit, Thamrong
Somboonprasert, Chirtwut
Lilakhunakon, Kittiphon
Jaovisidha, Suphaneewan
Ruangchaijatuporn, Thumanoon
Fuangfa, Praman
Rattanasiri, Sasivimol
Watcharananan, Siriorn
Chanplakorn, Pongsthorn
author_sort Lertudomphonwanit, Thamrong
collection PubMed
description BACKGROUND: Microbiological diagnosis of tuberculous spondylodiscitis (TS) and pyogenic spontaneous spondylodiscitis (PS) is sometime difficult. This study aimed to identify the predictive factors for differentiating TS from PS using clinical characteristics, radiologic findings, and biomarkers, and to develop scoring system by using predictive factors to stratify the probability of TS. METHODS: A retrospective single-center study. Demographics, clinical characteristics, laboratory findings and radiographic findings of patients, confirmed causative pathogens of PS or TS, were assessed for independent factors that associated with TS. The coefficients and odds ratio (OR) of the final model were estimated and used to construct the scoring scheme to identify patients with TS. RESULTS: There were 73 patients (51.8%) with TS and 68 patients (48.2%) with PS. TS was more frequently associated with younger age, history of tuberculous infection, longer duration of symptoms, no fever, thoracic spine involvement, ≥3 vertebrae involvement, presence of paraspinal abscess in magnetic-resonance-image (MRI), well-defined thin wall abscess, anterior subligamentous abscess, and lower biomarker levels included white blood cell (WBC) counts, erythrocyte-sedimentation-rate (ESR), neutrophil fraction, and C-reactive protein (all p < 0.05). Multivariate logistic regression analysis revealed significant predictors of TS included WBC ≤9,700/mm(3) (odds ratio [OR] 13.11, 95% confidence interval [CI] 4.23–40.61), neutrophil fraction ≤78% (OR 4.93, 95% CI 1.59–15.30), ESR ≤92 mm/hr (OR 4.07, 95% CI 1.24–13.36) and presence of paraspinal abscess in MRI (OR 10.25, 95% CI 3.17–33.13), with an area under the curve of 0.921. The scoring system stratified the probability of TS into three categories: low, moderate, and high with a TS prevalence of 8.1%, 29.6%, and 82.2%, respectively. CONCLUSIONS: This prediction model incorporating WBC, neutrophil fraction counts, ESR and presence of paraspinal abscess accurately predicted the causative pathogens. The scoring scheme with combination of these biomarkers and radiologic features can be useful to differentiate TS from PS.
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spelling pubmed-104378522023-08-19 A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis Lertudomphonwanit, Thamrong Somboonprasert, Chirtwut Lilakhunakon, Kittiphon Jaovisidha, Suphaneewan Ruangchaijatuporn, Thumanoon Fuangfa, Praman Rattanasiri, Sasivimol Watcharananan, Siriorn Chanplakorn, Pongsthorn PLoS One Research Article BACKGROUND: Microbiological diagnosis of tuberculous spondylodiscitis (TS) and pyogenic spontaneous spondylodiscitis (PS) is sometime difficult. This study aimed to identify the predictive factors for differentiating TS from PS using clinical characteristics, radiologic findings, and biomarkers, and to develop scoring system by using predictive factors to stratify the probability of TS. METHODS: A retrospective single-center study. Demographics, clinical characteristics, laboratory findings and radiographic findings of patients, confirmed causative pathogens of PS or TS, were assessed for independent factors that associated with TS. The coefficients and odds ratio (OR) of the final model were estimated and used to construct the scoring scheme to identify patients with TS. RESULTS: There were 73 patients (51.8%) with TS and 68 patients (48.2%) with PS. TS was more frequently associated with younger age, history of tuberculous infection, longer duration of symptoms, no fever, thoracic spine involvement, ≥3 vertebrae involvement, presence of paraspinal abscess in magnetic-resonance-image (MRI), well-defined thin wall abscess, anterior subligamentous abscess, and lower biomarker levels included white blood cell (WBC) counts, erythrocyte-sedimentation-rate (ESR), neutrophil fraction, and C-reactive protein (all p < 0.05). Multivariate logistic regression analysis revealed significant predictors of TS included WBC ≤9,700/mm(3) (odds ratio [OR] 13.11, 95% confidence interval [CI] 4.23–40.61), neutrophil fraction ≤78% (OR 4.93, 95% CI 1.59–15.30), ESR ≤92 mm/hr (OR 4.07, 95% CI 1.24–13.36) and presence of paraspinal abscess in MRI (OR 10.25, 95% CI 3.17–33.13), with an area under the curve of 0.921. The scoring system stratified the probability of TS into three categories: low, moderate, and high with a TS prevalence of 8.1%, 29.6%, and 82.2%, respectively. CONCLUSIONS: This prediction model incorporating WBC, neutrophil fraction counts, ESR and presence of paraspinal abscess accurately predicted the causative pathogens. The scoring scheme with combination of these biomarkers and radiologic features can be useful to differentiate TS from PS. Public Library of Science 2023-08-18 /pmc/articles/PMC10437852/ /pubmed/37594939 http://dx.doi.org/10.1371/journal.pone.0290361 Text en © 2023 Lertudomphonwanit et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lertudomphonwanit, Thamrong
Somboonprasert, Chirtwut
Lilakhunakon, Kittiphon
Jaovisidha, Suphaneewan
Ruangchaijatuporn, Thumanoon
Fuangfa, Praman
Rattanasiri, Sasivimol
Watcharananan, Siriorn
Chanplakorn, Pongsthorn
A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
title A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
title_full A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
title_fullStr A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
title_full_unstemmed A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
title_short A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
title_sort clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437852/
https://www.ncbi.nlm.nih.gov/pubmed/37594939
http://dx.doi.org/10.1371/journal.pone.0290361
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