The Angiopoietin-like protein 4: a promising biomarker to distinguish brucella spondylitis from tuberculous spondylitis

Lan, Siqin; He, Yuanlin; Tiheiran, Maijudan; Liu, Wenya; Guo, Hui

doi:10.1007/s10067-021-05752-1

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…None of these were selected as predictors in ML models because the training set cannot be represented with a small number of samples. Thus, we maintained that there were no significant differences in demographic characteristics, including ethnicity, gender, history of weight loss, history of tuberculosis in other solid organs, and age, between the BS and TS patients after the scientific and precise analysis of our data, which is in line with previous studies (43). Clinical laboratory tests, such as WBC count, ESR, and CRP level, which are all nonspecific in showing infectious processes and linked to spondylitis in the majority of cases, are a significant part of clinical diagnoses (40,42,44,45).…”

Section: Discussionsupporting

confidence: 89%

Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study

Yasin

Mardan

et al. 2023

Front. Surg.

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BackgroundTuberculous spondylitis (TS) and brucellar spondylitis (BS) are commonly observed in spinal infectious diseases, which are initially caused by bacteremia. BS is easily misdiagnosed as TS, especially in underdeveloped regions of northwestern China with less sensitive medical equipment. Nevertheless, a rapid and reliable diagnostic tool remains to be developed and a clinical diagnostic model to differentiate TS and BS using machine learning algorithms is of great significance.MethodsA total of 410 patients were included in this study. Independent factors to predict TS were selected by using the least absolute shrinkage and selection operator (LASSO) regression model, permutation feature importance, and multivariate logistic regression analysis. A TS risk prediction model was developed with six different machine learning algorithms. We used several metrics to evaluate the accuracy, calibration capability, and predictability of these models. The performance of the model with the best predictability was further verified with the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve. The clinical performance of the final model was evaluated by decision curve analysis.ResultsSix variables were incorporated in the final model, namely, pain severity, CRP, x-ray intervertebral disc height loss, x-ray endplate sclerosis, CT vertebral destruction, and MRI paravertebral abscess. The analysis of appraising six models revealed that the logistic regression model developed in the current study outperformed other methods in terms of sensitivity (0.88 ± 0.07) and accuracy (0.79 ± 0.07). The AUC of the logistic regression model predicting TS was 0.86 (95% CI, 0.81–0.90) in the training set and 0.86 (95% CI, 0.78–0.92) in the validation set. The decision curve analysis indicated that the logistic regression model displayed a higher clinical efficiency in the differential diagnosis.ConclusionsThe logistic regression model developed in this study outperformed other methods. The logistic regression model demonstrated by a calculator exerts good discrimination and calibration capability and could be applicable in differentiating TS from BS in primary health care diagnosis.

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Section: Discussionsupporting

confidence: 89%

Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study

Yasin

Mardan

et al. 2023

Front. Surg.

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“…Firstly, chronic inflammation along with in-acute-phase chronic inflammation are the most commonly encountered pathological changes of spinal brucellosis [78]. Furthermore, histopathology can potentially aid in the differentiation between brucellar and tuberculous spondylitis through specific findings like caseous necrosis, which is typically identified in tuberculous lesions, and through staining markers like Angiopoietin-like protein 4 [79].…”

Section: Microbiological Diagnosismentioning

confidence: 99%

Brucella Spondylitis: Current Knowledge and Recent Advances

Spernovasilis,

Karantanas,

Markaki

et al. 2024

JCM

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The most prevalent zoonotic disease is brucellosis, which poses a significant threat for worldwide public health. Particularly in endemic areas, spinal involvement is a major source of morbidity and mortality and can complicate the course of the disease. The diagnosis of Brucella spondylitis is challenging and should be suspected in the appropriate epidemiological and clinical context, in correlation with microbiological and radiological findings. Treatment depends largely on the affected parts of the body. Available treatment options include antibiotic administration for an adequate period of time and, when appropriate, surgical intervention. In this article, we examined the most recent data on the pathophysiology, clinical manifestation, diagnosis, and management of spinal brucellosis in adults.

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