A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
2
Issued Date
2023-01-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85168355683
Pubmed ID
37594939
Journal Title
PloS one
Volume
18
Issue
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
PloS one Vol.18 No.8 (2023) , e0290361
Suggested Citation
Lertudomphonwanit T., Somboonprasert C., Lilakhunakon K., Jaovisidha S., Ruangchaijatuporn T., Fuangfa P., Rattanasiri S., Watcharananan S., Chanplakorn P. A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis. PloS one Vol.18 No.8 (2023) , e0290361. doi:10.1371/journal.pone.0290361 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/88879
Title
A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis
Other Contributor(s)
Abstract
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/mm3 (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.