respectively. In the other 24 patients (29.3%, 24/82), simple aspiration technique was ineffective. An opposite position (from prone to supine or vice versa) was applied, and a new biopsy puncture site was chosen for reaspiration. This procedure was successful in 22 patients but not in 2 patients who had to have a chest tube insertion. The complete and partial regression rates were 25.0% (6/24) and 66.7% (16/24), respectively. Applying the new method, the total effective rate of aspiration improved significantly from 70.7% (58/82) to 97.6% (80/82). Conclusion:The opposite position aspiration can be safe, effective and minimally invasive treatment for CT-guided lung biopsy-induced pneumothorax thus reducing the use of chest tube significantly. Advances in knowledge: (1) Opposite position aspiration can elevate the success rate of aspiration significantly (from 70.7% to 97.6% in our study); (2) this procedure is a safe, effective and minimally invasive treatment for pneumothorax caused by biopsy; and (3) opposite position aspiration is a useful technique to reduce the use of chest tube, which has clinical significance.

Background The World Health Organization has reported that the treatment success rate of multi-drug resistance tuberculosis is approximately 57% globally. Although new drugs such as bedaquiline and linezolid is likely improve the treatment outcome, there are other factors associated with unsuccessful treatment outcome. The factors associated with unsuccessful treatment outcomes have been widely examined, but only a few studies have developed prediction models. We aimed to develop and validate a simple clinical prediction model for unsuccessful treatment outcomes in patients with multi-drug resistance pulmonary tuberculosis (MDR-PTB). Methods This retrospective cohort study was performed between January 2017 and December 2019 at a special hospital in Xi’an, China. A total of 446 patients with MDR-PTB were included. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to select prognostic factors for unsuccessful treatment outcomes. A nomogram was built based on four prognostic factors. Internal validation and leave-one-out cross-validation was used to assess the model. Results Of the 446 patients with MDR-PTB, 32.9% (147/446) cases had unsuccessful treatment outcomes, and 67.1% had successful outcomes. After LASSO regression and multivariate logistic analyses, no health education, advanced age, being male, and larger extent lung involvement were identified as prognostic factors. These four prognostic factors were used to build the prediction nomograms. The area under the curve of the model was 0.757 (95%CI 0.711 to 0.804), and the concordance index (C-index) was 0.75. For the bootstrap sampling validation, the corrected C-index was 0.747. In the leave-one-out cross-validation, the C-index was 0.765. The slope of the calibration curve was 0.968, which was approximately 1.0. This indicated that the model was accurate in predicting unsuccessful treatment outcomes. Conclusions We built a predictive model and established a nomogram for unsuccessful treatment outcomes of multi-drug resistance pulmonary tuberculosis based on baseline characteristics. This predictive model showed good performance and could be used as a tool by clinicians to predict who among their patients will have an unsuccessful treatment outcome.