Neutrophil-lymphocyte ratio as a potential marker for differential diagnosis between spinal tuberculosis and pyogenic spinal infection

Neutrophil-lymphocyte ratio: a correlation study of its effect on magnetic resonance imaging enhancement patterns in spinal parenchymal tuberculosis

OBJECTIVE

To explore the impact of the neutrophil-lymphocyte ratio (NLR) on magnetic resonance imaging (MRI) enhancement patterns in patients with spinal parenchymal tuberculosis.

METHODS

In this study, a retrospective analysis was conducted on 42 patients diagnosed for the first time with spinal parenchymal tuberculosis at Kunming Third People's Hospital between 2019 and 2024. They were divided into a homogeneous enhancement group and a ring enhancement group based on MRI characteristics and an analysis of their clinical presentations, imaging features and laboratory test results.

RESULTS

A total of 42 patients were included in the study, with 30 in the ring enhancement group and 12 in the homogeneous enhancement group. The ring enhancement group exhibited a significantly higher proportion of fever, night sweats and limb sensory/motor dysfunction compared with the homogeneous enhancement group (p < 0.05). For laboratory tests, the ring enhancement group showed remarkably elevated peripheral blood neutrophil counts and NLR, along with markedly reduced lymphocyte counts and proportions (p < 0.05). Additionally, the incidence of perilesional oedema was substantially higher in the ring enhancement group than in the homogeneous enhancement group (p < 0.05).

CONCLUSION

The NLR may serve as a potential indicator for assessing MRI enhancement patterns in spinal parenchymal tuberculosis, which is beneficial for identifying patients at different pathological stages of the disease. This study provides novel perspectives for clinical diagnosis and treatment while emphasising the need for further research on the application value of the NLR in tuberculosis.

Development and Prospective Validation of a Novel Risk Score for Predicting the Risk of Poor Surgical Site Healing in Patients Following Surgical Procedure for Spinal Tuberculosis: A Multi-Center Cohort Study

Background: The risk of poor surgical site healing in patients with spinal tuberculosis due to M. tuberculosis infection is known to be higher than in other surgical patients. Early identification and diagnosis are critical if we are to reduce the disability and mortality associated with spinal tuberculosis. We aimed to develop and validate a novel predictive score for predicting the risk of poor surgical site healing in patients following surgical procedure for spinal tuberculosis. Patients and Methods: We retrospectively analyzed the clinical data of patients with spinal tuberculosis who were hospitalized in the orthopedic ward of four regional medical centers in Guizhou Province between January 2015 and October 2022. Univariate and LASSO analysis was used to identify risk factors, construct and evaluate predictive models and novel predictive score for poor surgical site healing following the surgical procedure. Subsequently, 110 patients, admitted to four regional medical centers in Guizhou Province between January 2023 and February 2024, were used as an external prospective validation cohort to test the predictive efficacy of the prediction model. Results: Seven predictors were identified as risk factors for poor surgical site healing in patients undergoing surgical procedure for spinal tuberculosis. The areas under the receiver operating characteristic curve for a risk prediction model constructed based on the significant risk factors were 0.753 (95% CI: 0.693-0.813) and 0.779 (95% CI: 0.696-0.863) for the training and validation sets, respectively. Decision curve analysis demonstrated that the model yielded good clinical benefit. Finally, we applied the newly developed poor surgical site healing risk assessment score for the external prospective validation set; the area under the receiver operating characteristic curve for the poor surgical site healing risk assessment score was 0.846 (95% CI: 0.769-0.923) demonstrated that the model yielded better predictive effectiveness. Conclusion: The novel poor surgical site healing risk assessment score exhibits good discriminatory power and represents a beneficial predictive tool for facilitating suitable postoperative clinical management.

Neutrophil-to-lymphocyte ratio and its association with latent tuberculosis infection and all-cause mortality in the US adult population: a cohort study from NHANES 2011-2012

Background

There has been little study done on the possible connection between all-cause mortality and the neutrophil-to-lymphocyte ratio (NLR), particularly in individuals with latent tuberculosis infection (LTBI). The objective of this research was to examine the correlation between the NLR and LTBI, along with their effects on all-cause mortality in a cohort of individuals who had either LTBI or not.

Methods

This research incorporated data from the National Health and Nutrition Examination Survey (NHANES) 2011-2012, with a total of 4938 subjects involved. To investigate the connection between LTBI and variables, multivariable logistic regression models were used. Multivariable Cox proportional hazards models and Kaplan-Meier (KM) survival curves were employed to examine the association between NLR and all-cause death in individuals with and without LTBI.

Results

When analyzed as a continuous variable, The calculated odds ratios (ORs) for the different models-Model 1, Model 2, and Model 3 were 0.86, 0.83, and 0.84 (P < 0.005). NLR was evaluated as a categorical parameter, revealing that individuals in the tertile T3 had a notably lower rate of LTBI in comparison to those in the T1 group. After adjusting for different confounders, the odds ratio for T3 varied in the various models, being 0.75 (0.60∼0.95), 0.69 (0.54∼0.89), and 0.71 (0.56∼0.92), respectively. Additionally, higher NLR was significantly link to a greater risk of all-cause mortality in individuals with or without LTBI. Following multivariate adjustment, an 8% (Model 3, HR 1.08, 95% CI 1.05-1.12, P < 0.001) greater risk of mortality from all-cause was linked to every unit rise in NLR.

Conclusion

Results from the study revealed a negative correlation between NLR and the likelihood of LTBI as well as a higher risk of death from all causes. Therefore, NLR may be a helpful technique for risk categorization in the adult LTBI in the United States. To clarify the underlying mechanisms and any therapeutic implications of these findings, more investigation is necessary.

Unlocking the Diagnostic Potential: A Systematic Review of Biomarkers in Spinal Tuberculosis

Background/Objectives: Spinal tuberculosis (STB) is frequently misdiagnosed due to the multitude of symptoms it presents with. This review aimed to investigate the biomarkers that have the potential to accurately diagnose spinal TB in its early stages. Methods: A systematic search was conducted across multiple databases, yielding a diverse range of biomarkers categorized into complete blood count parameters, host inflammatory responses, bacterial antigens, and RNA-based markers. This review included studies on spinal tuberculosis patients, including blood serum biomarkers, while exclusion criteria included pediatric cases, cerebrospinal fluid or imaging biomarkers, co-infection with other bacteria, viruses, comorbidities, tumors, immune diseases, HIV infection, metabolic disorders, animal studies, opinion papers, and biomarkers relevant to health problems outside the disease. QUADAS-2 was used as a quality assessment tool for this review. This review identifies several promising biomarkers with significant diagnostic potential. Results: The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), IFN-γ, CXCR3, CXCL9, CXCL10, PSMB9, STAT1, TAP1, and specific miRNA combinations demonstrated noteworthy diagnostic accuracy in distinguishing STB from other spinal pathologies. Additionally, these biomarkers offer insights into disease severity and progression. The review also highlighted the importance of combining multiple biomarkers to enhance diagnostic precision. This comprehensive systematic review underscores the potential of biomarkers to revolutionize the diagnosis of spinal tuberculosis. By integrating these markers into clinical practice, healthcare providers can achieve earlier and more accurate diagnosis, leading to improved patient care and outcomes. Conclusions: The combination of multiple biomarkers, including NLR, PSMB9, STAT1, and specific miRNAs, demonstrates promising diagnostic accuracy.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

Diagnostic value of the neutrophil lymphocyte ratio in discrimination between tuberculosis and bacterial community acquired pneumonia: A meta-analysis

BACKGROUND

We conducted a systematic review and meta-analysis, based on Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, to evaluate current literature on diagnostic value of neutrophil to lymphocyte ratio (NLR) in discrimination between tuberculosis (TB) and bacterial community acquired pneumonia (B-CAP).

METHODS

Literature search was conducted from July 20, 2023 using Scopus, PubMed, and Web of Science databases. STATA software (version 12.0; Stata Corporation) was used for all analyses.

RESULTS

We found that patients with TB had significantly lower levels of NLR compared to those with B-CAP (SMD = -1.09, 95 %CI = -1.78- -0.40, P = 0.002). In the quality subgroup analysis, we found that patients with TB had significantly lower level of NLR compared to those with B-CAP consistent in moderate (SMD = -0.86, 95 %CI = -2.30, 0.57, P = 0.23) and high-quality studies (SMD = -1.25, 95 %CI = -2.07, -0.42). In the subgroup analysis based on continent, we found that patients with TB had significantly lower level of NLR compared to those with B-CAP in studies performed in Asian populations (SMD = -1.37, 95 %CI = -2.13, -0.61, P < 0.001), but not on African population (SMD = -0.02, 95 %CI = -1.06, 1.02, P = 0.97). The result of this study did not change after execution of sensitivity analysis. The pooled sensitivity of NLR was 0.86 (95% CI = 0.80, 0.91), and the pooled specificity was0.88 (95% CI = 0.69, 0.95).

CONCLUSION

Patients with TB had a significantly lower NLR levels compared to those with B-CAP, so we utilized this biomarker for distinguishing between the disorders.

Neutrophil-to-Lymphocyte Ratio, Neutrophil-to-Monocyte Ratio, Platelet-to-Lymphocyte Ratio, and Systemic Immune-Inflammation Index in Psoriasis Patients: Response to Treatment with Biological Drugs

BACKGROUND

Psoriasis is a chronic immune-mediated skin disease in which systemic inflammation plays an important role in its pathogenesis. In recent years, the neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-monocyte ratio (NMR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were shown to be important indicators of inflammation. This study aimed to investigate the NLR, NMR, PLR, and SII levels in psoriasis patients treated with biological agents.

METHOD

Clinical and biochemical data of 209 patients who received systemic therapy for psoriasis were obtained by retrospectively reviewing their medical records. The NLR, NMR, PLR, and SII values were calculated from the hemogram values of the patients.

RESULTS

In the third month of follow-up, the mean CRP, NLR, NMR, PLR, and SII values were significantly decreased compared with the baseline values. The SII values showed strong positive correlations with the NLR, NMR, and PLR. Adalimumab, etanercept, and infliximab, which are TNF-α blockers, were observed to be more effective on the PLR and NLR, and especially the NMR.

CONCLUSIONS

The NLR, NMR, PLR, and SII, which are data derived from routine blood tests, can be used in the monitoring of the treatment of psoriasis, especially with TNF-α blockers.

A predictive model for early clinical diagnosis of spinal tuberculosis based on conventional laboratory indices: A multicenter real-world study

Background

Early diagnosis of spinal tuberculosis (STB) remains challenging. The aim of this study was to develop a predictive model for the early diagnosis of STB based on conventional laboratory indicators.

Method

The clinical data of patients with suspected STB in four hospitals were included, and variables were screened by Lasso regression. Eighty-five percent of the cases in the dataset were randomly selected as the training set, and the other 15% were selected as the validation set. The diagnostic prediction model was established by logistic regression in the training set, and the nomogram was drawn. The diagnostic performance of the model was verified in the validation set.

Result

A total of 206 patients were included in the study, including 105 patients with STB and 101 patients with NSTB. Twelve variables were screened by Lasso regression and modeled by logistic regression, and seven variables (TB.antibody, IGRAs, RBC, Mono%, RDW, AST, BUN) were finally included in the model. AUC of 0.9468 and 0.9188 in the training and validation cohort, respectively.

Conclusion

In this study, we developed a prediction model for the early diagnosis of STB which consisted of seven routine laboratory indicators.

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors

OBJECTIVE

The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS).

METHODS

A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis.

RESULTS

A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877).

CONCLUSION

The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.