Tibial cortex transverse transport promotes ischemic diabetic foot ulcer healing via enhanced angiogenesis and inflammation modulation in a novel rat model.

BACKGROUND: Tibial Cortex Transverse Transport (TTT) represents an innovative surgical method for treating lower extremity diabetic foot ulcers (DFUs), yet its underlying mechanisms remain elusive. Establishing an animal model that closely mirrors clinical scenarios is both critical and novel for elucidating the mechanisms of TTT. METHODS: We established a diabetic rat model with induced hindlimb ischemia to mimic the clinical manifestation of DFUs. TTT was applied using an external fixator for regulated bone movement. Treatment efficacy was evaluated through wound healing assessments, histological analyses, and immunohistochemical techniques to elucidate biological processes. RESULTS: The TTT group demonstrated expedited wound healing, improved skin tissue regeneration, and diminished inflammation relative to controls. Marked neovascularization and upregulation of angiogenic factors were observed, with the HIF-1α/SDF-1/CXCR4 pathway and an increase in EPCs being pivotal in these processes. A transition toward anti-inflammatory M2 macrophages indicated TTT's immunomodulatory capacity. CONCLUSION: Our innovative rat model effectively demonstrates the therapeutic potential of TTT in treating DFUs. We identified TTT's roles in promoting angiogenesis and modulating the immune system. This paves the way for further in-depth research and potential clinical applications to improve DFU management strategies.

The causal relationship between autoimmune diseases and osteoporosis: a study based on Mendelian randomization.

Objective: The relationship between different autoimmune diseases and bone mineral density (BMD) and fractures has been reported in epidemiological studies. This study aimed to explore the causal relationship between autoimmune diseases and BMD, falls, and fractures using Mendelian randomization (MR). Methods: The instrumental variables were selected from the aggregated statistical data of these diseases from the largest genome-wide association study in Europe. Specifically, 12 common autoimmune diseases were selected as exposure. Outcome variables included BMD, falls, and fractures. Multiple analysis methods were utilized to comprehensively evaluate the causal relationship between autoimmune diseases and BMD, falls, and fractures. Additionally, sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and one analysis, were conducted to verify the result's reliability. Results: Strong evidence was provided in the results of the negatively association of ulcerative colitis (UC) with forearm BMD. UC also had a negatively association with the total body BMD, while inflammatory bowel disease (IBD) depicted a negatively association with the total body BMD at the age of 45-60 years. Horizontal pleiotropy or heterogeneity was not detected through sensitivity analysis, indicating that the causal estimation was reliable. Conclusion: This study shows a negative causal relationship between UC and forearm and total body BMD, and between IBD and total body BMD at the age of 45-60 years. These results should be considered in future research and when public health measures and osteoporosis prevention strategies are formulated.

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning.

OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. RESULTS: The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. CONCLUSION: PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB.

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery.

The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.

Introduction: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods: In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results: The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion: In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

MMP9 and STAT1 are biomarkers of the change in immune infiltration after anti-tuberculosis therapy, and the immune status can identify patients with spinal tuberculosis.

BACKGROUND: Due to a lack of studies on immune-related pathogenesis and a clinical diagnostic model, the diagnosis of Spinal Tuberculosis (STB) remains uncertain. Our study aimed to investigate the possible pathogenesis of STB and to develop a clinical diagnostic model for STB based on immune cell infiltration. METHODS: Label-free quantification protein analysis of five pairs of specimens was used to determine the protein expression of the intervertebral disc in STB and non-STB. GO enrichment analysis, and KEGG pathway analysis were used to investigate the pathogenesis of STB. The Hub proteins were then eliminated. Four datasets were downloaded from the GEO database to analyze immune cell infiltration, and the results were validated using blood routine test data from 8535TB and 7337 non-TB patients. Following that, clinical data from 164 STB and 162 non-STB patients were collected. The Random-Forest algorithm was used to screen out clinical predictors of STB and build a diagnostic model. The differential expression of MMP9 and STAT1 in STB and controls was confirmed using immunohistochemistry. RESULTS: MMP9 and STAT1 were STB Hub proteins that were linked to disc destruction in STB. MMP9 and STAT1 were found to be associated with Monocytes, Neutrophils, and Lymphocytes in immune cell infiltration studies. Data from 15,872 blood routine tests revealed that the Monocytes ratio and Neutrophils ratio was significantly higher in TB patients than in non-TB patients (p < 0.001), while the Lymphocytes ratio was significantly lower in TB patients than in non-TB patients (p < 0.001). MMP9 and STAT1 expression were downregulated following the anti-TB therapy. For STB, a clinical diagnostic model was built using six clinical predictors: MR, NR, LR, ESR, BMI, and PLT. The model was evaluated using a ROC curve, which yielded an AUC of 0.816. CONCLUSIONS: MMP9 and STAT1, immune-related hub proteins, were correlated with immune cell infiltration in STB patients. MR, NR, LR ESR, BMI, and PLT were clinical predictors of STB. Thus, the immune cell Infiltration-related clinical diagnostic model can predict STB effectively.

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.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.

INTRODUCTION: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible. METHODS: We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort. RESULTS: Seven factors-erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)-were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%. CONCLUSION: Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.

AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram.

Background: The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram. Patients and Methods: We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression. The logistic regression analysis and receiver operating characteristic (ROC) curve analysis were further used to obtain statistically significant parameters. These parameters were then used to construct a nomogram. The C-index, ROC curve, and decision curve analysis (DCA) were used to assess the predictive ability and accuracy of the nomogram, whereas internal verification was used to calculate the C-index by bootstrapping with 1,000 resamples. Results: A total of 394 patients with spinal tuberculosis surgery were included in the study, of whom 76 patients had surgical site infections whereas 318 patients did not. The predicted risk of surgical site infection in the nomogram ranged between 0.01 and 0.98. Both the value of the C-index of the nomogram (95% confidence interval [CI], 0.62-0.76) and the area under the curve (AUC) were found to be 0.69. The net benefit of the model ranged between 0.01 and 0.99. In contrast, the C-index calculated by the internal verification method of the nomogram was found to be 0.68. Conclusions: The risk factors predicting surgical site infection after spinal tuberculosis surgery included albumin, lesion segment, operation time, and incision length.

Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation.

Purpose: The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB). Methods: Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI). Results: The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes. Conclusion: Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.

Monocyte-to-Lymphocyte Ratio Was an Independent Factor of the Severity of Spinal Tuberculosis.

Purpose: The purpose was to explore the relationship between monocyte-to-lymphocyte ratio (MLR) and the severity of spinal tuberculosis. Methods: A total of 1,000 clinical cases were collected, including 496 cases of spinal tuberculosis and 504 cases of nonspinal tuberculosis. Laboratory blood results were collected, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cells (WBC), hemoglobin (HGB), platelets (PLT), neutrophil count, percentage of neutrophils, lymphocyte count, percentage of lymphocytes, monocyte count, percentage of monocytes, MLR, platelets -to- monocyte ratio (PMR), platelets -to- lymphocyte ratio (PLR), neutrophil -to- lymphocyte ratio (NLR), and platelets -to- neutrophil ratio (PNR). The statistical parameters analyzed by the Least Absolute Shrinkage and Selection Operator (LASSO) and receiver-operating characteristic (ROC) curves were used to construct the nomogram. The nomogram was assessed by C-index, calibration curve, ROC curve, and decision curve analysis (DCA) curve. Results: The C-index of the nomogram in the training set and external validation set was 0.801 and 0.861, respectively. Similarly, AUC was 0.801 in the former and 0.861 in the latter. The net benefit of the former nomogram ranged from 0.1 to 0.95 and 0.02 to 0.99 in the latter nomogram. Furthermore, there was a correlation between MLR and the severity of spinal tuberculosis. Conclusion: MLR was an independent factor in the diagnosis of spinal tuberculosis and was associated with the severity of spinal tuberculosis. Additionally, MLR may be a predictor of active spinal tuberculosis.

Comparison of Clinical Data Between Patients With Complications and Without Complications After Spinal Tuberculosis Surgery: A Propensity Score Matching Analysis.

Purpose: This study used a propensity score matching (PSM) analysis to explore the risk factors of post-operative complications and compared the differences in clinical data between them following spinal tuberculosis surgery. Methods: The clinical data of patients with spinal tuberculosis were collected in our hospital from June 2012 to June 2021, including general information, laboratory results, surgical information, and hospitalization costs. The data were divided into two groups: complication and without complication groups. The baseline data of the two groups were obtained using the PSM analysis. Univariate and multivariate logistic analyses were used to analyze the differences between the two groups. Results: A total of 292 patients were included in the PSM analysis: 146 patients with complications and 146 patients without complications. The operation time, incision length, hospital stay, and albumin quantity in the complications group were 162 ± 74.1, 11.2 ± 4.76, 14.7 ± 9.34, and 1.71 ± 2.82, respectively, and those in the without complication group were 138 ± 60.5, 10.2 ± 3.56, 11.7 ± 7.44, and 0.740 ± 2.44, respectively. The laboratory costs, examination costs, guardianship costs, oxygen costs, and total costs in the complications group were higher than those in the without complication group. A significant difference was observed in the albumin quantity by logistic regression analysis (P < 0.05). Conclusion: Several costs in the complication group were higher than in the without complication group. The albumin quantity may be an independent factor to predict post-operative complications of spinal tuberculosis by logistic regression analysis.

Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram.

OBJECTIVE: The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. METHODS: The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. RESULTS: The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%-.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01-0.79. CONCLUSION: A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.

Pulmonary embolism following the third thoracic tuberculosis surgery: A case report and literature review.

BACKGROUND: The study aimed to analyze the clinical effects of pulmonary embolism succeeding a third surgery conducted for multiple recurrences in thoracic tuberculosis (TB). CASE REPORT: A 74-year-old female patient developed thoracic tuberculosis and was subsequently treated in our hospital in March 2019, October 2020, and February 2021. The third surgical intervention included anterolateral thoracic lesion resection, internal fixation, posterior spinal tuberculous sinus resection, and debridement with suture. The operative time was 172 min resulting in a substantial intraoperative blood loss (2321 ml). Postoperative re-examination of chest CTPA indicated a strip filling defect and pulmonary embolism in the external branch of the right middle lobe of the lung. After completing the active treatment, the D-dimer quantification, WBC, CRP, and ESR values were 1261 ng/ml, 7.71 × 109 /L, 74.66 mg/L, and 63 mm, respectively. Chest CTPA re-examination after the treatment showed no signs of pulmonary embolism. CONCLUSION: Patients with a long-term history of multiple operations, high BMI, cerebral infarction, diabetes, and older age group were more likely to develop pulmonary embolism after spinal tuberculosis surgery. Thus, the possibility of postoperative pulmonary embolism should be thoroughly analyzed before any subsequent surgical treatment in patients with recurrent spinal tuberculosis.

Development and Validation of a Nomogram for Predicting Albumin Transfusion After Spinal Tuberculosis Surgery: Based on Propensity Score Matching Analysis.

BACKGROUND: There have been few literature reports on the use of perioperative parameters to predict the risk of albumin transfusion after spinal tuberculosis surgery based on the application of nomogram and propensity score matching (PSM) analysis. OBJECTIVE: The purpose was to predict the risk of albumin transfusion after spinal tuberculosis surgery based on a combination of PSM and nomogram. METHODS: The clinical data of the patients were collected in our hospital, including preoperative clinical data, preoperative laboratory tests, and postoperative clinical data. All data were divided into 2 groups, including the albumin transfusion group and the non-albumin transfusion group. The PSM analysis was used to adjust the baseline data of the 2 groups. The nomogram was further constructed. The practicability and predictive ability of the model were evaluated. RESULTS: A total of 494 cases were collected in this article; 102 pairs by PSM analysis were used to construct the nomogram. There were statistical differences in surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis by logistic analysis, and these parameters were included in the construction of the nomogram. The C-index of the prediction model was 0.734. The area under the curve was 0.73 and the net benefit was between 0.13 and 0.99. The calculated C-index was 0.71 by the internal verification method. CONCLUSIONS: The PSM analysis had a good matching effect and the nomogram had a good predictive ability. Surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis might be predictors of albumin transfusion after spinal tuberculosis surgery.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning.

Background: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. Methods: A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. Results: The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. Conclusion: We used ML methods to screen out the blood-specific factors-PDW, LYM, AST/ALT, BUN, and Na+-of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.