This study aimed to evaluate the impact of precise treatment administered according to the results of metagenomic next-generation sequencing (mNGS) on the clinical outcomes of patients with spinal infections. In this multicenter retrospective study, the clinical data of 158 patients with spinal infections who were admitted to Xiangya Hospital Central South University, Xiangya Boai Rehabilitation Hospital, The First Hospital of Changsha, and Hunan Chest Hospital from 2017 to 2022 were reviewed. Among these 158 patients, 80 patients were treated with targeted antibiotics according to the mNGS results and were assigned to the targeted medicine (TM) group. The remaining 78 patients with negative mNGS results and those without mNGS and negative microbial culture results were treated with empirical antibiotics and assigned to the empirical drug (EM) group. The impact of targeted antibiotics based on the mNGS results on the clinical outcomes of patients with spinal infections in the two groups was analyzed. The positive rate of mNGS for diagnosing spinal infections was significantly higher than that of microbiological culture (X2=83.92, P<0.001), procalcitonin (X2=44.34, P<0.001), white blood cells (X2=89.21, P < 0.001), and IGRAs (Interferon-gamma Release Tests) (X2 = 41.50, P < 0.001). After surgery, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) showed a decreasing trend in the patients with spinal infections in both the TM and EM groups. The decrease in CRP was more obvious in the TM group than in the EM group at 7, 14 days, 3, and 6 months after surgery (P<0.05). The decrease in ESR was also significantly obvious in the TM group compared with the EM group at 1 and 6 months after surgery (P<0.05). The time taken for CRP and ESR to return to normal in the TM group was significantly shorter than that in the EM group (P<0.05). There was no significant difference in the incidence of poor postoperative outcomes between the two groups. The positive rate of mNGS for the diagnosis of spinal infection is significantly higher than that of traditional detection methods. The use of targeted antibiotics based on mNGS results could enable patients with spinal infections to achieve a faster clinical cure.

BackgroundDifferential diagnosis of spinal tuberculosis is important for the clinical management of patients, especially in populations with spinal bone destruction. There are few effective tools for preoperative differential diagnosis in these populations. The QuantiFERON-TB Gold In-Tube (QFT-GIT) test has good sensitivity and specificity for the diagnosis of tuberculosis, but its efficacy in preoperative diagnosis of spinal tuberculosis has rarely been investigated.MethodA total of 123 consecutive patients with suspected spinal tuberculosis hospitalized from March 20, 2020, to April 10, 2022, were included, and the QFT-GIT test was performed on each patient. We retrospectively collected clinical data from these patients. A receiver operating characteristic (ROC) curve was plotted with the TB Ag-Nil values. The cutoff point was calculated from the ROC curve of 61 patients in the study cohort, and the diagnostic validity of the cutoff point was verified in a new cohort of 62 patients. The correlations between TB Ag-Nil values and other clinical characteristics of the patients were analyzed.ResultsOf the 123 patients included in the study, 51 had confirmed tuberculosis, and 72 had non-tuberculosis disease (AUC=0.866, 95% CI: 0.798-0.933, P<0.0001). In patients with spinal tuberculosis, the QFT-GIT test sensitivity was 92.16% (95% CI: 80.25%-97.46%), and the specificity was 67.14% (95% CI: 54.77%-77.62%). The accuracy of diagnostic tests in the validation cohort increased from 77.42% to 80.65% when a new cutoff point was selected (1.58 IU/mL) from the ROC curve of the study cohort. The TB Ag-Nil values in tuberculosis patients were correlated with the duration of the patients’ disease (r=0.4148, P=0.0025).ConclusionThe QFT-GIT test is an important test for preoperative differential diagnosis of spinal tuberculosis with high sensitivity but low specificity. The diagnostic efficacy of the QFT-GIT test can be significantly improved via application of a new threshold (1.58 IU/mL), and the intensity of the QFT-GIT test findings in spinal tuberculosis may be related to the duration of a patient’s disease.

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

ObjectiveTo investigate the differences in postoperative deep venous thrombosis (DVT) between patients with spinal infection and those with non-infected spinal disease; to construct a clinical prediction model using patients’ preoperative clinical information and routine laboratory indicators to predict the likelihood of DVT after surgery.MethodAccording to the inclusion criteria, 314 cases of spinal infection (SINF) and 314 cases of non-infected spinal disease (NSINF) were collected from January 1, 2016 to December 31, 2021 at Xiangya Hospital, Central South University, and the differences between the two groups in terms of postoperative DVT were analyzed by chi-square test. The spinal infection cases were divided into a thrombotic group (DVT) and a non-thrombotic group (NDVT) according to whether they developed DVT after surgery. Pre-operative clinical information and routine laboratory indicators of patients in the DVT and NDVT groups were used to compare the differences between groups for each variable, and variables with predictive significance were screened out by least absolute shrinkage and operator selection (LASSO) regression analysis, and a predictive model and nomogram of postoperative DVT was established using multi-factor logistic regression, with a Hosmer- Lemeshow goodness-of-fit test was used to plot the calibration curve of the model, and the predictive effect of the model was evaluated by the area under the ROC curve (AUC).ResultThe incidence of postoperative DVT in patients with spinal infection was 28%, significantly higher than 16% in the NSINF group, and statistically different from the NSINF group (P < 0.000). Five predictor variables for postoperative DVT in patients with spinal infection were screened by LASSO regression, and plotted as a nomogram. Calibration curves showed that the model was a good fit. The AUC of the predicted model was 0.8457 in the training cohort and 0.7917 in the validation cohort.ConclusionIn this study, a nomogram prediction model was developed for predicting postoperative DVT in patients with spinal infection. The nomogram included five preoperative predictor variables, which would effectively predict the likelihood of DVT after spinal infection and may have greater clinical value for the treatment and prevention of postoperative DVT.