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A Comparative Factor Analysis and New Magnetic Resonance Imaging Scoring System for Differentiating Pyogenic Versus Tuberculous Spondylodiscitis

Article information

Neurospine. 2024;21(2):690-700
Publication date (electronic) : 2024 June 30
doi : https://doi.org/10.14245/ns.2448120.060
1Department of Orthopedic Surgery, Maharat Nakhon Ratchasima Hospital, Nakhon Ratchasima, Thailand
2Department of Radiology, Rayong Hospital, Rayong, Thailand
3Department of Medicine, Maharat Nakhon Ratchasima Hospital, Nakhon Ratchasima, Thailand
Corresponding Author Terdpong Tanaviriyachai Department of Orthopedic Surgery, Maharat Nakhon Ratchasima Hospital, Chang Phueak Road, Mueang, Nakhon Ratchasima 30000, Thailand Email: Bomorthokorat@gmail.com
Received 2024 February 1; Revised 2024 April 4; Accepted 2024 April 12.

Abstract

Objective

This study aimed to compare and analyze differences in clinical and magnetic resonance imaging (MRI) findings between tuberculous spondylodiscitis (TbS) and pyogenic spondylodiscitis (PyS), and to develop and validate a simplified multiparameter MRI-based scoring system for differentiating TbS from PyS.

Methods

We compared predisposing factors in 190 patients: 123 with TbS and 67 with PyS, confirmed by laboratory tests, culture, or pathology. Data encompassing patient demographics, clinical characteristics, laboratory results, and MRI findings were collected between 2015 and 2020. Data were analyzed using logistic regression methods, and selected coefficients were transformed into an MRI-based scoring system. Internal validation was performed using bootstrapping method.

Results

Univariate analysis revealed that the significant risk factors associated with TbS included thoracic lesions, vertebral destruction > 50%, intraosseous abscess, thin-walled abscess, well-defined paravertebral abscess, subligamentous spreading, and epidural abscess. Multivariate analysis revealed that only thoracic lesions, absence of epidural phlegmon, subligamentous spreading, intraosseous abscesses, well-defined paravertebral abscesses, epidural abscesses, and absence of facet joint arthritis were independent predictive factors for TbS (all p < 0.05). These potential predictors were used to derive an MRI scoring system. Total scores ≥ 14/29 points significantly predicted the probability of TbS, with a sensitivity of 97.58%, specificity of 92.54%, and an area under the curve of 0.96 (95% confidence interval, 125.40–3,257.95).

Conclusion

This simplified MRI-based scoring system for differentiating TbS from PyS helps guide appropriate treatment when the causative organism is not identified.

INTRODUCTION

Infectious spondylodiscitis (IS) is a septic inflammation of the spine involving vertebral bodies and paraspinal structures [1]. During the progression of the disease, the formation of abscesses or edema can destroy vertebrae or cause neurologic disorders [1,2]. The overall incidence of spinal infection in adults is approximately 2.2 per 100,000 per year, with a slowly increasing trend worldwide in recent years [1,3]. IS is potentially life-threatening, with a mortality rate of 3%–20% [3,4]. Common causes of IS include pyogenic spondylodiscitis (PyS) and tuberculous spondylodiscitis (TbS), which account for 40%–80% and 17%–40% of all IS cases, respectively [3,5]. Insufficient specific signs and symptoms might cause delayed diagnosis and treatment, leading to disastrous consequences [6,7].

It is critical to distinguish between TbS and PyS to provide appropriate treatment. However, the identification of these 2 entities is challenging because of their nonspecific signs and symptoms. Microbiological diagnosis is the gold standard for differentiating between TbS and PyS. However, identifying the microbes is difficult. Previous reports on patients with PyS showed a negative culture rate ranging from 10% to 30%. In contrast, obtaining a positive culture for TbS typically requires 3 weeks, with a success rate ranging between 50% and 70% [8,9]. When microbiological identification is impossible, clinical, laboratory, and magnetic resonance imaging (MRI) findings may aid in identifying a potential causative microorganism. Previous studies have distinguished radiological findings between TbS and PyS [10-17]. However, few studies have developed a scoring system that uses predictive factors to stratify the probability of TbS from PyS [17,18].

In the present study, we aimed to compare and analyze the differences in the clinical, laboratory, and MRI findings between TbS and PyS, and to develop and validate a simplified multiparameter MRI-based scoring system for differentiating between TbS and PyS.

MATERIALS AND METHODS

We retrospectively collected medical records of patients diagnosed with IS admitted to the Maharat Nakhon Ratchasima Hospital between January 2015 and December 2020. Cases with microbiologically and pathologically documented evidence were included in this study. PyS was diagnosed when the etiological organism was identified through percutaneous vertebral biopsy, surgical drainage, or blood culture (a minimum of 2 separate sets). TbS was diagnosed based on pathological samples, tissue cultures, and polymerase chain reaction (PCR) tests. Patients with spondylodiscitis caused by other pathogens (e.g., fungal, or parasitic), unconfirmed spondylodiscitis (if no pathogens were isolated), lack of pretreatment MRI, absence of gadolinium administration during MRI, or lack of T1-weighted or fluid-sensitive sequences were excluded.

Clinical data included age, sex, predisposing factors and/or associated illnesses, onset of the symptoms, fever, Frankel grading, and causative organisms. Laboratory data comprised white blood cell (WBC) count, proportion of neutrophils, C-reactive protein (CRP) levels, erythrocyte sedimentation rate (ESR), and serum alkaline phosphatase (ALP) levels. All MRI examinations followed a standard protocol, including axial and sagittal T1-weighted sequences, axial and sagittal fluid-sensitive sequences, including T2-weighted with fat-saturation (T2w fat-sat) or short tau inversion recovery sequences, and axial and sagittal T1-weighted sequences after gadolinium administration. MRI findings were evaluated by consensus between a 5-year-experienced spine surgeon and a musculoskeletal radiologist. Details of each finding were evaluated and are described in Table 1. The infection in the thoracolumbar region is classified as a thoracic or lumbar lesion based on the extent of vertebral body destruction, with thoracic areas being more severe.

Description of individual MRI features

This study was performed in accordance with the Helsinki Declaration and approved by the Maharat Nakhon Ratchasima Hospital Institutional Review Board (MNRH IRB No. 089/2020). The patients were informed that the data concerning their cases would be submitted for publication and provided their consent.

Statistical analyses were performed using Stata Statistical Software (ver. 14; StataCorp LP., College Station, TX, USA). After a descriptive study of the variables, t-tests were used to compare continuous variables. Chi-square tests were used to compare predisposing factors and associated illnesses. All tests were 2-sided, and a p-value of 0.05 was considered significant. All variables that were significant in the chi-square test were included in a multivariate logistic regression analysis using stepwise backward elimination for the derived independent variables.

The diagnostic accuracy of the reduced multivariate model was evaluated in terms of calibration and discrimination. Calibration was performed using Hosmer-Lemeshow goodness of fit statistics. A calibration plot comparing the agreement between the disease probabilities estimated using the model and the observed disease data is also presented. Discriminative power was evaluated using the area under the receiver operating characteristic (ROC) curve. Internal validation was performed using a bootstrapping procedure with 1,000 replicates. Bootstrap resampling is a statistical technique used for estimating the sampling distribution of a statistic by resampling with replacement from the observed data. This resampling is applicable in various situations, offering versatility in statistical problems like parameter estimation and hypothesis testing. It requires minimal assumptions and is easy to implement, making it a practical way to assess statistic variability without complex mathematical derivations. However, this resampling procedure has several drawbacks, including its reliance on the original sample, its inability to accurately represent population variability in small samples, and its assumption of stationary data distribution, which may not be suitable in dynamic environments.

Subsequently, a simplified risk score transformation was generated. Each item was assigned a specific score based on the logistic regression coefficients of the multivariate model. To achieve this, the regression coefficient of each item was divided by its lowest coefficient, the result was rounded to the closest integer. The total scores were then categorized into 2 groups (TbS and PyS) for clinical applicability. Sensitivity and specificity were calculated separately for each group using a population-analog approach. Calibration and discrimination were assessed using a score-based multivariate logistic model.

RESULTS

Among the 420 patients diagnosed with IS, 190 had a confirmed diagnosis, matched all inclusion criteria, and were retrospectively enrolled. The characteristics of the 190 patients are summarized in Table 2. The mean age at diagnosis was 56.8 years (range, 18–84 years), with 106 males and 84 female patients. Data were collected from 67 patients with PyS and 123 patients with TbS. Among the 67 patients with PyS, the causative organism was confirmed by culture of percutaneous spinal biopsy and surgical drainage in 64.2% (n = 43), and blood culture in 35.8% (n = 24). Staphylococcus aureus was the most common microorganism identified in 56.7% (n = 38), followed by Streptococcus spp. (19.4%, n = 13), Escherichia coli (13.4%, n = 9), Bacillus spp. (2.9%, n = 2), Klebsiella pneumoniae (2.9%, n = 2), Brucellosis (1.4%, n =1), Burkholderia pseudomallei (1.4%, n = 1), and Pseudomonas aeruginosa (1.4%, n = 1). Among the 123 patients with TbS, the diagnosis was confirmed by percutaneous spinal biopsy and surgical drainage in 25.2% (n = 31) of patients. The remaining patients with TbS were confirmed by positive results for PCR of Mycobacterium tuberculosis and pathology demonstrating caseous granulomatous inflammation.

General demographic data

Clinically, back pain was the most common symptom observed in both groups with 94.3% (n = 116) among TbS and 94% (n = 63) among PyS patients. The duration of symptoms lasted > 4 weeks in 102 TbS patients (82.93%) and 26 PyS patients (39.39%) (p < 0.01). The number of patients with diabetic mellitus was 12 (9.76%) in the TbS group and 16 (23.88%) in the PyS group (p < 0.01). Laboratory findings of the 2 groups are shown in Table 2. PyS was more frequently associated with the following parameters: WBC > 10,000/mm3, a higher proportion of neutrophils > 75%, and ALP > 120 IU/L (p < 0.01).

As shown in Table 3, thoracic involvement was significantly more frequent in TbS than in PyS (61.78% vs. 22.39%, p < 0.001), while lumbar involvement was more common in PyS than in TbS (85.07% vs. 56.91%, p < 0.001). No significant differences were observed in cervical or sacral involvement. Moreover, no differences were found in the number of involved vertebrae, involvement of the posterior elements, or posterior wall retropulsion. On T1-weighted MRI, the vertebral body signal was typically hypointense in both groups. However, the TbS group exhibited a proportionately more heterogeneous intensity than the PyS group (8.13% vs. 0%, p < 0.03). Destruction of vertebral endplates and vertebral destruction > 50% were more common in the TbS group than in the PyS group (26.83% vs. 8.96%, p < 0.001 and 60.16% vs. 19.4%, p < 0.001, respectively). No differences were found in the disc signal or extent of disc destruction between the groups. On T1-weighted gadolinium MRI, the vertebral body was more frequently heterogeneously enhanced in the TbS group (89.43% vs. 38.81%, p < 0.001). Moreover, vertebral intraosseous abscesses were more frequent in TbS compared to PyS (69.1% vs. 7.46%, p < 0.001). No significant differences were reported between the intervertebral disc contrast enhancement and disc abscesses. In terms of paravertebral involvement, the TbS group exhibited a higher prevalence of well-defined paravertebral abscesses (82.93% vs. 44.78%, p < 0.001), abscesses with thin and regular walls (81.3% vs. 2.99%, p < 0.001), epidural abscesses (67.48% vs. 43.28%, p = 0.001), and anterior longitudinal subligamentous spreading (94.3% vs. 41.79%, p < 0.001). However, the presence of epidural phlegmon (4.88% vs. 80.6%, p < 0.001) and facet joint arthritis (46.34% vs. 80.6%, p < 0.001) was strongly associated with PyS. No significant differences were observed between the presence or absence of spinal cord compression.

General MRI parameters

The duration of symptoms > 4 weeks (odds ratio [OR], 8.19; 95% confidence interval [CI], 4.10–16.33; p < 0.001), Diabetes mellitus (OR, 0.49; 95% CI, 0.15–0.77; p = 0.01),WBC > 10,000/mm3 (OR, 0.21; 95% CI, 0.11–0.40; p < 0.01), neutrophil proportion > 75% (OR, 0.35; 95% CI, 0.19–0.65; p = 0.001), ALP > 120 IU/L (OR, 0.31; 95% CI, 0.17–0.58; p < 0.001), presence of thoracic lesions (OR, 5.67; 95% CI, 2.87–11.20; p < 0.001), severe vertebral destruction (OR, 6.35; 95% CI, 3.14–12.86; p < 0.001), heterogenous contrast-enhanced vertebral body (OR, 19.21; 95% CI, 3.91–94.26; p < 0.001), presence of vertebral intraosseous abscess (OR, 28.06; 95% CI, 10.44–75.36; p < 0.001), well-defined paravertebral enhancement (OR, 84.75; 95% CI, 8.74–820.87; p < 0.001), presence of epidural abscess (OR, 2.75; 95% CI, 1.49–5.07; p = 0.001), absence of facet joint arthritis (OR, 4.88; 95% CI, 2.42–9.84; p < 0.001), and anterior longitudinal subligamentous spreading (OR, 23.28; 95% CI, 9.42–57.49; p < 0.001) were identified as possible risk factors for TbS in the univariate analysis (p < 0.2) in our study (Table 4). No significant differences were found in temperature > 38°C (OR, 0.49; 95% CI, 0.24–1.02; p = 0.57), peak ESR > 40 mm/hr (OR, 0.96; 95% CI, 0.43–2.14; p = 0.93), peak CRP > 5 mg/dL (OR, 0.35; 95% CI, 0.07–1.65; p = 0.19), or ill-defined paravertebral enhancement (OR, 0.21; 95% CI, 0.03–1.49; p = 0.12). Multiple logistic regression analysis showed that thoracic lesion (OR, 819.81; 95% CI, 6.84–98,313.95; p = 0.006), absence of epidural phlegmon (OR, 900.86; 95% CI, 31.39–25,857.73; p < 0.001), anterior longitudinal subligamentous spreading (OR, 185.78; 95% CI, 7.92–4,360.64; p = 0.001), presence of vertebral intraosseous abscess (OR, 19.59; 95% CI, 1.75–219.70; p = 0.016), well-defined paravertebral enhancement (OR, 10.79; 95% CI, 1.28–90.80; p = 0.029), presence of epidural abscess (OR, 9.69; 95% CI, 0.78–121.06; p = 0.038), and absence of facet joint arthritis (OR, 7.25; 95% CI, 0.91–57.94; p = 0.042) were independent predictive factors for TbS (Table 5).

Results of a univariate analysis of possible risk factors for tuberculous spondylodiscitis

Results of multiple logistic regression analysis and scoring system for tuberculous spondylodiscitis

1. MRI Scoring Transformation

Each potential predictor of TbS in the multivariate model was assigned a specific score derived from the logistic regression coefficient: thoracic lesion, 7 points, no epidural phlegmon 7 points, subligamentous spreading 5 points, intraosseous abscess 3 points, well-defined paravertebral abscess 2.5 points, epidural abscess 2.5 points, and no facet joint arthritis 2 points (Table 5). The scoring scheme, with a total score ranging from 0 to 29, included categories for differentiation. This cutoff point was based on a calibration plot of sensitivity and specificity. For discriminative ability, the area under the parametric ROC curve for the score-based logistic regression model was 0.96 (95% CI, 125.40–3,257.95) (Fig. 1). The calibration was illustrated using a calibration plot, with a p-value of < 0.001. The predicted probability of TbS increased as the score increased, with a high level of agreement between actual and predicted diseases (Fig. 2). The total score was significantly different between the groups (> 14 points, p < 0.001), with a sensitivity of 97.58% and specificity of 92.54%. The application of this MRI scoring transformation is illustrated in Figs. 3 and 4.

Fig. 1.

Using the prediction probability of a multivariate logistic regression model, the receiver operator characteristic (ROC) analysis of magnetic resonance imaging scores for separated tuberculous spondylodiscitis from pyogenic spondylodiscitis is presented.

Fig. 2.

Model calibration plots illustrating the predicted probability of tuberculous spondylodiscitis increased as the total score >14 points with a high level of agreement between actual and predicted risks (sensitivity, 97.58%; specificity, 92.54%)

Fig. 3.

Infectious spondylodiscitis in a 22-year-old woman with low back pain for 2 months. (A) Sagittal T1-weighted magnetic resonance imaging (MRI) scan demonstrates T8–10 hypointensity with anterior subligamentous spreading at T8–9. (B) Sagittal T2-weighted MRI scan demonstrates T8–10 inhomogeneous hyperintensity. (C) Sagittal T1-weighted gadolinium-enhanced MRI scan demonstrates focal inhomogeneity at T9–10 and intraosseous rim enhancement. (D) Axial T1-weighted gadolinium-enhanced MRI scan demonstrates a well-defined paraspinal abscess (white arrow) and epidural abscess (red arrow) at T8 level. Using the MRI scoring transformation, the patient’s score was 7 (thoracic)+7 (no epidural phlegmon)+5 (subligamentous spreading)+3 (intraosseous abscess)+2.5 (well-defined paravertebral abscess)+2.5 (epidural abscess)+2 (no facet joint arthritis)=29, indicating a high probability of tuberculous spondylodiscitis. This patient underwent decompressive laminectomy and fusion, and a tissue biopsy revealed a positive polymerase chain reaction for Mycobacterium tuberculosis.

Fig. 4.

Infectious spondylodiscitis in a 63-year-old man with low back pain and lower extremity radiation pain for 1 month. (A) Sagittal T1-weighted magnetic resonance imaging (MRI) scan demonstrates hypointensity of the T7–8 vertebral body. (B) Sagittal T2-weighted MRI scan demonstrates isointensity of the vertebral bodies and destruction of the intervertebral disc. (C) Sagittal T1-weighted gadolinium-enhanced MRI scan demonstrates epidural encroachment or indentation by granulation tissue, also known as epidural phlegmon (white arrows). (D) Axial T1-weighted gadolinium-enhanced MRI scan demonstrates ill-defined paravertebral enhancement and soft tissue surrounding the disc level, but no obvious paraspinal abscess. Using the MRI scoring transformation, the patient’s score was 7 (thoracic)+0 (epidural phlegmon)+0 (no subligamentous spreading)+0 (no intraosseous abscess)+0 (ill-defined paravertebral abscess)+0 (no epidural abscess)+2 (no facet joint arthritis)=9, indicating a low probability of tuberculous spondylodiscitis. This patient underwent decompressive laminectomy and fusion, and a tissue biopsy for culture revealed a Staphylococcus aureus infection.

DISCUSSION

Despite the number of earlier studies [10-15] that have described the clinical, laboratory, and MRI features of PyS and TbS, we obtained significant distinguishing characteristics from our comparison of the 2 groups. However, no single result can distinguish between these circumstances. Compared to other studies [10-16,19], our study represents the largest series comparing microbiologically confirmed cases of PyS and TbS. A longer symptom duration (> 4 weeks) and the absence of fever (Table 2) were more frequently associated with TbS than with PyS. According to Yoon et al. [20], TbS risk factors included a median latency to spondylodiscitis diagnosis of > 7 days, and patients with TbS experienced fever less frequently than those with PyS. The diagnosis of spinal infection is highly sensitive to inflammatory indicators, such as WBC count, neutrophil count, ESR, and CRP level [21]. Our results were in agreement with the findings of Kim et al. [4], who reported that high levels of ALP (> 120 IU/L) and neutrophil predominance (> 75%) in leukocytosis (> 10,000/mm3) were more commonly predictive of PyS. Similarly, Lertudomphonwanit et al. [17] observed that a neutrophil fraction < 78% and WBC count < 9,700/mm3 were highly suggestive diagnostic clues for differentiating patients with TbS from those with PyS. Compared with previous studies, Kim et al. [4] found that ESR > 40 mm3 and CRP > 5 mg/dL were more frequently associated with PyS. In contrast to our study, these biomarker cutoff values were incapable of differentiating PyS from TbS. According to Lertudomphonwanit et al. [17], ESR levels of < 92 mm/hr were highly suggestive indicators of TbS. Nevertheless, CRP level was not shown to be a predictive factor in their study. The demographics of our patient group may partially explain this outcome. As demonstrated in this study, both groups had delayed time to diagnosis; therefore, ESR and CRP levels may have been more significant at the time of diagnosis [22].

MRI has substantially improved the diagnosis of spinal infections. Even in the early stages of spinal infections, the increased sensitivity of MRI allows the identification of pathogenic alterations in the spine. According to a previous study, contrast-enhanced MRI is a reliable method for differentiating TbS from PyS [15]. Our study demonstrated the presence of thoracic lesions, intraosseous abscesses, anterior longitudinal subligamentous spreading, and well-defined paravertebral enhancement as predictive factors for TbS, which corresponded well with the review by Lee [23] Tuberculous spondylitis typically begins in the anterior cancellous bone of the vertebral body. This is followed by the destruction of the vertebral body, extending beneath the anterior longitudinal ligament, leading to the formation of an abscess near the vertebral body. The thoracic spine is the region most frequently affected by this process [18,24,25]. This finding is supported by a recent study that indicated a well-defined paraspinal abscess as one of the hallmarks of TbS, whereas PyS typically exhibits more widespread, ill-defined areas of enhancement [12]. According to a recent study by Kanna et al. [26], large abscesses with a thin wall are one of the MRI findings that are strongly predictive of TbS.

Epidural soft-tissue thickening, also known as epidural phlegmon, manifests as a diffuse and homogeneous contrast-enhancing process. This presentation may indicate an inflammatory process before turning into an epidural abscess and is less amenable to surgical drainage [27]. Our study revealed a higher prevalence of epidural phlegmon among PyS patients. According to Zhang et al. [18], patients with PyS presented with phlegmon characterized by ill-defined boundaries and occasional small abscesses with thick and irregular walls (97% in PyS vs. 37% in TbS). Conversely, the epidural abscesses that were more common in the TbS group were larger and had well-defined borders. They are more likely to develop into a ring-shaped, thin, smooth-walled, polysoluble abscess [17]. These results were also observed in our study. Patients with TbS more frequently have a slow-growing infection that finally results in an epidural abscess at a later stage.

Septic arthritis of the facet joint was diagnosed based on erosion, edema, and enlargement of the facet joint space (Fig. 5). Associated inflammatory changes in the epidural space or adjacent paraspinal muscles can be seen with gadolinium-enhanced T1-weighted MRI [28]. In our study, this finding was a reliable predictor of PyS. Due to the aggressiveness of the organism and its propensity to spread outside the facet capsule, which can cause synovitis, perisynovial inflammation, and erosive changes to the articular surface, the synthesis of a proteolytic enzyme is implicated in the inflammatory process of PyS [12,15,24]. Similar to Harada’s results [7], the enhancement of soft tissues around the facet joints was more frequent in PyS than in TbS.

Fig. 5.

Staphylococcus aureus spondylodiscitis in a 65-year-old man with low back pain and radiculopathy for 2 weeks. (A) Sagittal T1-weighted magnetic resonance imaging (MRI) scan demonstrates hypointensity at the intervertebral disc of L4–5. (B) A sagittal T2-weighted MRI scan demonstrates hyperintensity of the L5 vertebral bodies and destruction of the intervertebral disc. (C) Sagittal T1-weighted gadolinium-enhanced MRI scan demonstrates epidural phlegmon (red arrows). (D) Axial T1-weighted gadolinium-enhanced MRI scan at the facet joint demonstrates the presence of collections within the facet joint or soft tissue surrounding the facet joint (white arrow).

To the best of our knowledge, there are few diagnostic prediction tools available to effectively distinguish TbS from PyS. Zhang et al. [18] analyzed the MRI findings of spinal infections (32 cases of PyS, 38 cases of TbS), to identify key distinguishing features between PyS and TbS, and establish a systematic scoring method. Using the scoring system, the correct coincidence rate was 95.23%, with a sensitivity of 91.67%, and specificity of 100%. However, the predictive parameters for detecting PyS and TbS were separated using a prediction tool. In our study, we developed a simplified MRI scoring system for the diagnostic prediction of TbS, based primarily on predictive factors (Table 5). Total scores ≥ 14 points may significantly predict the risk of TbS, with a sensitivity of 97.58% and specificity of 92.54%. The discriminative ability of the score-based logistic regression model was 0.96, as indicated by the ROC curve. Figs. 3 and 4 provide demonstrations of the scheme used. As the average duration of symptoms was 3 months in patients with TbS and 4 weeks in patients with PyS, this scoring system is a valuable diagnostic tool that can help distinguish between TbS and PyS, particularly in the subacute to chronic stages of the disease.

The clinical predictive model of this study provided significant advantages. We included all diagnostically relevant variables in the model and transformed the regression equation into a scoring system for use in clinical settings. Nevertheless, this study has some limitations. First, the study, a retrospective cohort study, included patients from a single hospital. However, a data imbalance occurred between groups, which was corrected using multivariable logistic regression analysis. Adjusting the data ratio could potentially reduce the study’s power. Second, the clinical or laboratory data regarding the onset of symptoms may have been biased because our center was a referral center. Larger population studies are required to assess the clinical relevance of these findings. Third, although internal validation was performed in our study, the reproducibility of the scoring remains unknown until a prospective external validation study is conducted in another setting or at a different time. Finally, because we did not include individuals with other low-virulence causative organisms, such as fungi, in our investigation, the generalizability of our findings for these patients may be limited.

CONCLUSION

This study validated the predictive parameters for differentiating TbS from PyS and developed a simplified MRI-based scoring system to predict the likelihood of TbS, assisting clinicians in making judgments when the causative pathogen remains elusive.

Notes

Conflict of Interest

The authors have nothing to disclose.

Funding/Support

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author Contribution

Conceptualization: TT, SW; Formal analysis: KC; Investigation: PP, TV, SJ; Methodology: UP; Project administration: WS; Writing – original draft: TT; Writing – review & editing: TT.

References

1. Govender S. Spinal infections. J Bone Joint Surg Br 2005;87:1454–8.
2. Rasouli MR, Mirkoohi M, Vaccaro AR, et al. Spinal tuberculosis: diagnosis and management. Asian Spine J 2012;6:294–308.
3. Grammatico L, Baron S, Rusch E, et al. Epidemiology of vertebral osteomyelitis (VO) in France: analysis of hospital-discharge data 2002–2003. Epidemiol Infect 2008;136:653–60.
4. Kim CJ, Song KH, Jeon JH, et al. A comparative study of pyogenic and tuberculous spondylodiscitis. Spine (Phila Pa 1976) 2010;35:E1096–100.
5. Kourbeti IS, Tsiodras S, Boumpas DT. Spinal infections: evolving concepts. Curr Opin Rheumatol 2008;20:471–9.
6. Tsiodras S, Falagas ME. Clinical assessment and medical treatment of spine infections. Clin Orthop Relat Res 2006;444:38–50.
7. Duarte RM, Vaccaro AR. Spinal infection: state of the art and management algorithm. Eur Spine J 2013;22:2787–99.
8. Marschall J, Bhavan KP, Olsen MA, et al. The impact of prebiopsy antibiotics on pathogen recovery in hematogenous vertebral osteomyelitis. Clin Infect Dis 2011;52:867–72.
9. Colmenero JD, Ruiz-Mesa JD, Sanjuan-Jimenez R, et al. Establishing the diagnosis of tuberculous vertebral osteomyelitis. Eur Spine J 2013;22 Suppl 4(Suppl 4):579–86.
10. Galhotra RD, Jain T, Sandhu P, et al. Utility of magnetic resonance imaging in the differential diagnosis of tubercular and pyogenic spondylodiscitis. J Nat Sci Biol Med 2015;6:388–93.
11. Frel M, Białecki J, Wieczorek J, et al. Magnetic resonance imaging in differential diagnosis of pyogenic spondylodiscitis and tuberculous spondylodiscitis. Pol J Radiol 2017;82:71–87.
12. Harada Y, Tokuda O, Matsunaga N. Magnetic resonance imaging characteristics of tuberculous spondylitis vs. pyogenic spondylitis. Clin Imaging 2008;32:303–9.
13. Lee Y, Kim BJ, Kim SH, et al. Comparative analysis of spontaneous infectious spondylitis: pyogenic versus tuberculous. J Korean Neurosurg Soc 2018;61:81–8.
14. Naselli N, Facchini G, Lima GM, et al. MRI in differential diagnosis between tuberculous and pyogenic spondylodiscitis. Eur Spine J 2022;31:431–41.
15. Chang MC, Wu HTH, Lee CH, et al. Tuberculous spondylitis and pyogenic spondylitis: comparative magnetic resonance imaging features. Spine (Phila Pa 1976) 2006;31:782–8.
16. Jung NY, Jee WH, Ha KY, et al. Discrimination of tuberculous spondylitis from pyogenic spondylitis on MRI. AJR Am J Roentgenol 2004;182:1405–10.
17. Lertudomphonwanit T, Somboonprasert C, Lilakhunakon K, et al. A clinical prediction model to differentiate tuberculous spondylodiscitis from pyogenic spontaneous spondylodiscitis. PLoS One 2023;18e0290361.
18. Zhang N, Zeng X, He L, et al. The value of MR imaging in comparative analysis of spinal infection in adults: pyogenic versus tuberculous. World Neurosurg 2019;128:e806–13.
19. Jiménez-Mejías ME, de Dios Colmenero J, Sánchez-Lora FJ, et al. Postoperative spondylodiskitis: etiology, clinical findings, prognosis, and comparison with nonoperative pyogenic spondylodiskitis. Clin Infect Dis 1999;29:339–45.
20. Yoon YK, Jo YM, Kwon HH, et al. Differential diagnosis between tuberculous spondylodiscitis and pyogenic spontaneous spondylodiscitis: a multicenter descriptive and comparative study. Spine J 2015;15:1764–71.
21. Khanna K, Sabharwal S. Spinal tuberculosis: a comprehensive review for the modern spine surgeon. Spine J 2019;19:1858–70.
22. Pääkkönen M, Kallio MJ, Kallio PE, et al. Sensitivity of erythrocyte sedimentation rate and C-reactive protein in childhood bone and joint infections. Clin Orthop Relat Res 2010;468:861–6.
23. Lee KY. Comparison of pyogenic spondylitis and tuberculous spondylitis. Asian Spine J 2014;8:216–23.
24. An HS, Seldomridge JA. Spinal infections: diagnostic tests and imaging studies. Clin Orthop Relat Res 2006;444:27–33.
25. Tanaviriyachai T, Choovongkomol K, Pornsopanakorn P, et al. Factors affecting neurological deficits in thoracic tuberculous spondylodiscitis. Int J Spine Surg 2023;17:645–51.
26. Kanna RM, Babu N, Kannan M, et al. Diagnostic accuracy of whole spine magnetic resonance imaging in spinal tuberculosis validated through tissue studies. Eur Spine J 2019;28:3003–10.
27. Talbott JF, Shah VN, Uzelac A, et al. Imaging-based approach to extradural infections of the spine. Semin Ultrasound CT MR 2018;39:570–86.
28. Kwee RM, Kwee TC. Imaging of facet joint diseases. Clin Imaging 2021;80:167–79.

Article information Continued

Fig. 1.

Using the prediction probability of a multivariate logistic regression model, the receiver operator characteristic (ROC) analysis of magnetic resonance imaging scores for separated tuberculous spondylodiscitis from pyogenic spondylodiscitis is presented.

Fig. 2.

Model calibration plots illustrating the predicted probability of tuberculous spondylodiscitis increased as the total score >14 points with a high level of agreement between actual and predicted risks (sensitivity, 97.58%; specificity, 92.54%)

Fig. 3.

Infectious spondylodiscitis in a 22-year-old woman with low back pain for 2 months. (A) Sagittal T1-weighted magnetic resonance imaging (MRI) scan demonstrates T8–10 hypointensity with anterior subligamentous spreading at T8–9. (B) Sagittal T2-weighted MRI scan demonstrates T8–10 inhomogeneous hyperintensity. (C) Sagittal T1-weighted gadolinium-enhanced MRI scan demonstrates focal inhomogeneity at T9–10 and intraosseous rim enhancement. (D) Axial T1-weighted gadolinium-enhanced MRI scan demonstrates a well-defined paraspinal abscess (white arrow) and epidural abscess (red arrow) at T8 level. Using the MRI scoring transformation, the patient’s score was 7 (thoracic)+7 (no epidural phlegmon)+5 (subligamentous spreading)+3 (intraosseous abscess)+2.5 (well-defined paravertebral abscess)+2.5 (epidural abscess)+2 (no facet joint arthritis)=29, indicating a high probability of tuberculous spondylodiscitis. This patient underwent decompressive laminectomy and fusion, and a tissue biopsy revealed a positive polymerase chain reaction for Mycobacterium tuberculosis.

Fig. 4.

Infectious spondylodiscitis in a 63-year-old man with low back pain and lower extremity radiation pain for 1 month. (A) Sagittal T1-weighted magnetic resonance imaging (MRI) scan demonstrates hypointensity of the T7–8 vertebral body. (B) Sagittal T2-weighted MRI scan demonstrates isointensity of the vertebral bodies and destruction of the intervertebral disc. (C) Sagittal T1-weighted gadolinium-enhanced MRI scan demonstrates epidural encroachment or indentation by granulation tissue, also known as epidural phlegmon (white arrows). (D) Axial T1-weighted gadolinium-enhanced MRI scan demonstrates ill-defined paravertebral enhancement and soft tissue surrounding the disc level, but no obvious paraspinal abscess. Using the MRI scoring transformation, the patient’s score was 7 (thoracic)+0 (epidural phlegmon)+0 (no subligamentous spreading)+0 (no intraosseous abscess)+0 (ill-defined paravertebral abscess)+0 (no epidural abscess)+2 (no facet joint arthritis)=9, indicating a low probability of tuberculous spondylodiscitis. This patient underwent decompressive laminectomy and fusion, and a tissue biopsy for culture revealed a Staphylococcus aureus infection.

Fig. 5.

Staphylococcus aureus spondylodiscitis in a 65-year-old man with low back pain and radiculopathy for 2 weeks. (A) Sagittal T1-weighted magnetic resonance imaging (MRI) scan demonstrates hypointensity at the intervertebral disc of L4–5. (B) A sagittal T2-weighted MRI scan demonstrates hyperintensity of the L5 vertebral bodies and destruction of the intervertebral disc. (C) Sagittal T1-weighted gadolinium-enhanced MRI scan demonstrates epidural phlegmon (red arrows). (D) Axial T1-weighted gadolinium-enhanced MRI scan at the facet joint demonstrates the presence of collections within the facet joint or soft tissue surrounding the facet joint (white arrow).

Table 1.

Description of individual MRI features

No. Radiological parameter MRI features in identification
Parameters evaluated on T1-weighted MRI
 1 Pathologic vertebral body signal Classified as hypointense, isointense, hyperintense or heterogeneous, compared to the unaffected vertebrae.
 2 Pathologic intervertebral disc signal Classified as hypointense, hyperintense or isointense, compared to unaffected discs.
 3 Vertebral endplate involvement Classified as
 - eroded: vertebral body destruction with reduction in body height of less than half.
 - destroyed: vertebral body destruction with reduction in body height of more than half.
 4 Extent of vertebral destruction Classified as
 - minimal: confined to vertebral endplate.
 - severe: extended to vertebral body including vertebral body abscess or vertebral collapse.
 5 Extent of intervertebral disc destruction Classified as
 - none/mild, moderate, or severe including complete disc destruction or disc abscess.
Parameters evaluated on T1-weighted+gadolinium
 6 Pathologic vertebral body contrast enhancement Classified as marginal, homogeneous, or heterogeneous*.
 *Heterogenous enhancement is defined as abnormal bone marrow signals combined with hypo- and hypersignal intensity in the coronal, sagittal, and axial planes.
 7 Pathologic intervertebral disc contrast enhancement Classified as diffuse, marginal, focal, or absent.
 8 Presence of intraosseous or intervertebral disc abscess Presence of collections within the vertebra or intervertebral disc space.
 9 Paravertebral abscess Presence of collections within the adjacent paravertebral soft tissues classified as absent, well-defined* or ill-defined.
*A well-defined paraspinal abscess is defined as regular and smooth abscess wall.
 10 Paravertebral abscess wall characteristics Classified as thin wall (a wall thickness < 2 mm) or thick wall (a wall thickness ≥ 2 mm).
 11 Epidural abscess Epidural encroachment or indentation by pus.
 12 Epidural phlegmon Epidural encroachment or indentation by granulation tissue.
 13 Septic facet joint arthritis Presence of collections within facet joint or soft tissue surround the facet joint.
 14 Subligamentous spreading Presence of anterior subligamentous bone signal alterations and abscess which extends more than 2 vertebral levels.
 15 Spinal cord compression Epidural encroachment or indentation to the spinal cord.

MRI, magnetic resonance imaging.

Table 2.

General demographic data

Variable Tuberculous (n = 123) Pyogenic (n = 67) p-value
Age (yr) 56.89 ± 15.38 56.86 ± 11.52 0.989
Sex 0.001*
 Male 58 48
 Female 65 19
Duration of symptoms (wk) < 0.001*
 <2 9 (7.31) 26 (39.39)
 2–4 12 (9.76) 15 (22.73)
 > 4 102 (82.93) 26 (39.39)
Temperature (> 38°C) 18 (14.63) 20 (29.85) 0.013*
Frankel grade 0.294
 A 15 (12.20) 5 (7.46)
 B 15 (12.20) 8 (11.94)
 C 30 (24.39) 14 (20.90)
 D 31 (25.20) 12 (17.91)
 E 32 (26.01) 28 (41.79)
Underlying disease
 Diabetes mellitus 0.008*
  Yes 12 (9.76) 16 (23.88)
  No 111 (90.24) 51 (76.12)
 Hypertension 0.890
  Yes 34 (27.65) 19 (28.36)
  No 89 (72.35) 48 (71.64)
 Chronic kidney disease 0.665
  Yes 5 (4.07) 1 (1.64)
  No 118 (95.93) 66 (98.51)
 Cirrhosis 0.282
  Yes 1 (0.81) 2 (2.99)
  No 122 (99.19) 65 (97.01)
White blood cells (/mm3) < 0.001*
 ≤ 10,000 85 (69.11) 21 (31.34)
 > 10,000 38 (30.89) 46 (68.66)
Neutrophil (> 75%) 32 (26.01) 36 (53.73) 0.008*
Peak ESR (> 40 mm/hr) 100 (81.30) 53 (82.81) 0.886
Peak CRP (> 5 mg/dL) 112 (91.05) 62 (96.88) 0.224
Alkaline phosphatase (> 120 IU/L) 40 (32.52) 38 (59.38) < 0.001*

Values are presented as mean±standard deviation or number (%).

ESR, erythrocyte sedimentation rate; CRP, C-reactive protein.

*

p<0.05, statistically significant differences.

Table 3.

General MRI parameters

Variable Tuberculous (n = 123) Pyogenic (n = 67) p-value
Location of lesion
 Cervical 0.060
  Yes 4 (3.25) 10 (14.93)
  No 119 (96.75) 57 (85.07)
 Thoracic < 0.001*
  Yes 76 (61.78) 15 (22.39)
  No 47 (37.90) 52 (77.61)
 Lumbar < 0.001*
  Yes 70 (56.91) 57 (85.07)
  No 53 (43.09) 10 (14.93)
 Sacral 0.051
  Yes 14 (11.38) 15 (22.39)
  No 109 (88.62) 52 (77.61)
No. of vertebrae involved 0.517
 ≤2 68 (55.28) 34 (50.75)
 >2 55 (44.72) 33 (49.25)
Vertebral involvement 0.106
 Continuous 115 (93.50) 58 (86.57)
 Noncontinuous 8 (6.50) 9 (13.43)
Involvement posterior element 0.123
 Yes 54 (43.90) 37 (55.22)
 No 69 (56.10) 30 (44.78)
Posterior somatic wall retropulsion 0.168
 Yes 20 (16.26) 6 (8.96)
 No 103 (83.74) 61 (91.04)
Parameters on T1-weighted MRI
 Vertebral body signal 0.033*
  Hypointense 110 (89.43) 65 (97.01)
  Isointense 3 (2.44) 2 (2.99)
  Hyperintense 0 (0) 0 (0)
  Heterogeneous 10 (8.13) 0 (0)
 Disc signal 0.531
  Hypointense 102 (82.92) 60 (89.55)
  Isointense 15 (12.20) 5 (7.46)
  Hyperintense 6 (4.88) 2 (2.99)
 Vertebral endplate involvement <0.001*
  Normal 8 (6.50) 16 (23.88)
  Eroded 82 (66.67) 45 (67.16)
  Completely destroyed 33 (26.83) 6 (8.96)
 Extent of vertebral destruction < 0.001*
  < 50% 49 (39.84) 54 (80.60)
  > 50% 74 (60.16) 13 (19.40)
 Extent of disc destruction 0.027*
  None 24 (19.51) 7 (10.45)
  < 50% 53 (43.09) 22 (32.84)
  > 50% 46 (37.40) 38 (56.72)
Parameters evaluated on T1-weighted+gadolinium MRI (intravertebral involvement)
 Vertebral body contrast enhancement < 0.001*
  Marginal 2 (1.63) 9 (13.43)
  Homogeneous 11 (8.94) 32 (47.76)
  Heterogeneous 110 (89.43) 26 (38.81)
 % Vertebral body enhancement 0.774
  < 50% 10 (8.13) 4 (5.97)
  > 50% 113 (91.87) 63 (94.03)
 Intravertebral disc contrast enhancement 0.093
  Diffuse 34 (27.64) 10 (14.93)
  Marginal 33 (26.83) 28 (41.79)
  Focal 45 (36.59) 22 (32.84)
  Absent 11 (8.94) 7 (10.45)
 Vertebral Intraosseous abscess < 0.001*
  Yes 85 (69.10) 5 (7.46)
  No 38 (30.90) 62 (92.54)
 Disc abscess 0.619
  Yes 16 (13.00) 7 (10.45)
  No 107 (87.00) 60 (89.55)
Parameters evaluated on T1-weighted+gadolinium MRI (extravertebral involvement)
 Paravertebral tissue < 0.001*
  Absent 2 (1.63) 3 (4.48)
  Well-defined 112 (91.06) 2 (2.99)
  Ill-defined 9 (7.31) 62 (92.54)
 Paravertebral abscess < 0.001*
  Yes 102 (82.93) 30 (44.78)
  No 21 (17.07) 37 (55.22)
 Paravertebral abscess wall characteristics < 0.001*
  Thin and regular 100 (81.30) 2 (2.99)
  Thick and irregular 2 (1.63) 28 (43.28)
  None 21 (17.07) 37 (55.22)
 Epidural abscess 0.001*
  Yes 83 (67.48) 29 (43.28)
  No 40 (32.52) 38 (56.72)
 Epidural phlegmon < 0.001*
  Yes 6 (4.88) 54 (80.60)
  No 117 (95.12) 13 (19.40)
 Facet joint arthritis < 0.001*
  Yes 57 (46.34) 54 (80.60)
  No 66 (53.66) 13 (19.40)
 Subligamentous spreading < 0.001*
  Yes 116 (94.30) 28 (41.79)
  No 7 (5.70) 39 (58.21)
 Spinal cord compression 0.101
  Yes 101 (82.11) 61 (91.04)
  No 22 (17.89) 6 (8.96)

MRI, magnetic resonance imaging.

*

p<0.05, statistically significant differences.

Table 4.

Results of a univariate analysis of possible risk factors for tuberculous spondylodiscitis

Variable Odds ratio 95% CI p-value
Duration of symptoms (wk)
 <2 Ref
 2–4 2.29 0.75–6.96 0.144
 >4 12.15 4.93–29.94 < 0.001*
Duration of symptoms (wk)
 >4 8.19 4.10–16.33 < 0.001*
Temperature (> 38°C) 0.49 0.24–1.02 0.057
Diabetes mellitus 0.34 0.15–0.77 0.01*
White blood cells (/mm3) 0.21 0.11–0.40 < 0.001*
Neutrophil (> 75%) 0.35 0.19–0.65 0.001*
Peak ESR (> 40 mm/hr) 0.96 0.43–2.14 0.927
Peak CRP (> 5 mg/dL) 0.35 0.07–1.65 0.185
Alkaline phosphatase (> 120 IU/L) 0.31 0.17–0.58 < 0.001*
Location of lesion
 Cervical 0.19 0.05–0.63 0.007*
 Thoracic 5.67 2.87–11.20 < 0.001*
 Lumbar 0.23 0.10–0.50 < 0.001*
Severe vertebral destruction 6.35 3.14–12.86 < 0.001*
Heterogenous contrast-enhanced vertebral body 19.21 3.91–94.26 < 0.001*
Vertebral Intraosseous abscess 28.06 10.44–75.36 < 0.001*
Well-defined paravertebral enhancement 84.75 8.74–820.87 < 0.001*
Ill-defined paravertebral enhancement 0.21 0.03–1.49 0.120
Epidural abscess 2.75 1.49–5.07 0.001*
Epidural phlegmon 0.012 0.004–0.033 < 0.001*
No facet joint arthritis 4.88 2.42–9.84 < 0.001*
Subligamentous spreading 23.28 9.42–57.49 < 0.001*

CI, confidence interval; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein.

*

p<0.05, statistically significant differences.

Table 5.

Results of multiple logistic regression analysis and scoring system for tuberculous spondylodiscitis

Variable Odds ratio Significance 95% CI Coefficients Score
Thoracic lesion 819.81 0.006 6.84–98,313.95 6.71 7
No epidural phlegmon 900.86 < 0.001 31.39–25,857.73 6.80 7
Subligamentous spreading 185.78 0.001 7.92–4,360.64 5.22 5
Vertebral Intraosseous abscess 19.59 0.016 1.75–219.70 2.97 3
Well-defined paravertebral enhancement 10.79 0.029 1.28–90.80 2.38 2.5
Epidural abscess 9.69 0.038 0.78–121.06 2.27 2.5
No facet joint arthritis 7.25 0.042 0.91–57.94 1.98 2

CI, confidence interval.