Nomogram model to predict pneumothorax after computed tomography-guided coaxial core needle lung biopsy.

PURPOSE: To develop a predictive model to determine risk factors of pneumothorax in patients undergoing the computed tomography (CT)1-guided coaxial core needle lung biopsy (CCNB). METHODS: A total of 489 patients who underwent CCNBs with an 18-gauge coaxial core needle were retrospectively included. Patient characteristics, primary pulmonary disease, target lesion image characteristics and biopsy-related variables were evaluated as potential risk factors of pneumothorax which was determined on the chest X-ray and CT scans. Univariate and multivariate logistic regressions were used to identify the independent risk factors of pneumothorax and establish the predictive model, which was presented in the form of a nomogram. The discrimination and calibration of the model were evaluated as well. RESULTS: The incidence of pneumothorax was 32.91 % and 31.42 % in the development and validation groups, respectively. Age, emphysema, pleural thickening, lesion location, lobulation sign, and size grade were identified independent risk factors of pneumothorax at the multivariate logistic regression model. The forming model produced an area under the curve of 0.718 (95 % CI = 0.660-0.776) and 0.722 (95 % CI = 0.638-0.805) in development and validation group, respectively. The calibration curve showed good agreement between predicted and actual probability. CONCLUSIONS: The predictive model for pneumothorax after CCNBs had good discrimination and calibration, which could help in clinical practice.

Development of a predictive model of pulmonary hemorrhage in patients after CT-guided coaxial core needle lung biopsy / 西安交通大学学报(医学版)

【Objective】 To establish a predictive model for patients with hemorrhage after CT-guided coaxial core needle lung biopsy （CCNB） based on logistic regression. 【Methods】 A total of 489 patients who had undergone CCNB were retrospectively recruited. The potential risk factors of hemorrhage after lung biopsy were analyzed by univariate and multivariate logistic regression， through which we screened the independent risk factors and established a prediction model for hemorrhage. We evaluated the discrimination， calibration and clinical usefulness of the model. 【Results】 There were 141 cases （42.6%） of hemorrhage in the development group and 66 cases （41.8%） of hemorrhage in the validation group； there was no case of severe hemorrhage or hemothorax. Multivariate logistic regression analysis showed that fibrinogen degradation products， pulmonary interstitial fibrosis， largest diameter and puncture depth were independent predictive factors of hemorrhage. Hemorrhage prediction model was established and presented in the form of a nomogram. Discrimination of the model： the AUC was 0.837 in the development group and 0.777 in the validation group. The calibration curve showed good agreement between predicted probability and actual probability of hemorrhage. The unreliability test yielded a P value of 0.849 in the development group and 0.147 in the validation group. The DCA curve showed that the hemorrhage predictive model could increase the benefit of patients. 【Conclusion】 The predictive model of hemorrhage in patients after CCNB based on logistic regression can be used in clinical practice.