Prediction of the Complication Risk in Drug-Resistant Tuberculosis After Surgery: Development and Assessment of a Novel Nomogram - Wu, Liwei; Dai, Xiyong; Wang, Haijiang; Huang, Chaolin; Xia, Fan; Song, Yanzheng; Wang, Lin (Frontiers in Surgery, 2021)
Background: Surgery is increasingly accepted as an adjunctive approach to treat MDR/XDR TB. However, there is no model that includes all factors to predict the risk of postoperative complications. Methods: We developed a prediction model based on 138 patients who have undergone surgery as a treatment for drug-resistant tuberculosis after 24 months. Clinical features on lesion type(L), treatment history(T), body physiological status(B), and surgery(S) approach were evaluated by condition of patient. Multivariable logistic regression analysis was conducted by clinical features selected in the least absolute shrinkage and selection operator (LASSO) to build a nomogram. Discrimination, calibration, and clinical usefulness of the nomogram were assessed using the C-index, calibration plot, and decision curve. Internal validation was assessed using the bootstrapping validation. Results: The nomogram contained features such as L, B, T, cavitary, recurrent chest infection (RCI) and MDR\XDR tuberculosis. The model displayed good discrimination with a C-index of 0.879 (95% CI: 0.799–0.967). High C-index value of 0.824 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 4%. Conclusion: The novel nomogram could be conveniently used to predict postoperative complication risk in DR-TB patients.