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. 2022 Jun 2;13:882651. doi: 10.3389/fimmu.2022.882651

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Copyright © 2022 Chen, Liu, Liang, Ye, Huang, Chen, Sun, Yi, Zhou, Jiang, Chen, Li, Chen, Guo, Chen, Yao, Liao, Yu, Wu, Fan, Gan and Zhan

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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The predictive ability and accuracy of the nomogram model was evaluated. (A) The calibration curve for the training set showed that the nomogram predicted risk of spinal tuberculosis was highly consistent with the actual diagnosis of spinal tuberculosis. (B) The calibration curve for the external validation set showed that the predicted and actual values were also highly consistent. (C) The ROC curve of the training set showed that the AUC of the nomogram model for predicting spinal tuberculosis was 0.862. (D) The ROC curve of the external validation set calculated the AUC to be 0.834. (E) The DCA curve of the training set showed that the net benefit of the predictive model ranged from 0.01 to 0.99. (F), The DCA curve of the external validation set showed that the net benefit of the predictive model also ranged from 0.01 to 0.99.