A nomogram to predict life-threatening hemoptysis in patients with tuberculous hemoptysis

Abstract Objectives We aimed to develop and validate a nomogram for predicting life-threatening hemoptysis (LTH) in patients with tuberculous hemoptysis. Methods Patients diagnosed and treated for tuberculous hemoptysis at our hospital during January 2018–December 2020 were retrospectively analyzed. Univariable and binary logistic regression analyses were used to identify independent risk factors for LTH in patients with tuberculous hemoptysis. A predictive nomogram was developed to predict the risk of LTH in the participants. Receiver operating characteristic (ROC) curve analysis, calibration analysis, and decision curve analysis (DCA) were used to evaluate the nomogram. The bootstrapping method was used for internal validation. Results Data from 444 patients were analyzed. Hematocrit (P = 0.005, odds ratio [OR]: 0.912, 95% confidence interval [95% CI]: 0.854–0.972), hemoptysis amount (P < 0.01, OR: 1.005, 95% CI: 1.002–1.007), and lung destruction (P < 0.01, OR: 0.221, 95% CI: 0.099–0.49) were identified as risk factors for LTH. Notably, 50% LTH rate was used as the cut-off to validate the nomogram model. Area under the ROC curve for the nomogram was 0.814 (95% CI: 0.82–0.963). The sensitivity and specificity of the nomogram were 90.1% and 62.5%, respectively. Calibration curve indicated good consistency between the risk predicted using the model and the actual risk. The prediction error was low (integrated Brier score: 0.057). The Hosmer–Lemeshow test yielded a nonsignifcant P-value of 0.634. DCA indicated that the nomogram can be an effective diagnostic tool for predicting LTH. Conclusions The preliminary nomogram could help predict LTH; thus, appropriate decisions can be made to gain more time for patients’ treatment.