Nomogram model to predict pneumothorax after computed tomography-guided coaxial core needle lung biopsy. Issue 140 (July 2021)

• Record Type: Journal Article
• Authors: Yang, Linyun; Liang, Ting; Du, Yonghao; Guo, Chenguang; Shang, Jin; Pokharel, Saugat; Wang, Rong; Niu, Gang
• Is Part Of: European journal of radiology. Issue 140(2021)
• Publication Date: 2021-07
• Subjects: CCNB coaxial core needle lung biopsy -- CT computed tomography -- PTNB percutaneous transthoracic needle biopsy -- AUC area under the curve -- APTT activated partial thromboplastin time -- BMI body mass index -- D-D D-dimer -- FDP fibrinogen degradation products -- FIB fibrinogen -- PLT platelets -- PT prothrombin time -- PTNB percutaneous transthoracic needle biopsy RBC red blood cells -- TT thrombin time -- WBC white blood cells -- OR odds ratio -- CI confidence interval; Pneumothorax -- Coaxial core needle lung biopsy -- Normogram -- Computed tomography -- Prediction model; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109749
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Previous predictive pneumothorax after pulmonary biopsy models were for non−CCNB. Risk factors of pneumothorax after CT-guided CCNBs were explored. A predictive nomogram model was established to predict pneumothorax. The predictive model had good discrimination and calibration. This could help in clinical practice. Abstract: 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.