Abstract:

Objective To construct and validate the efficacy of a risk prediction model for pneumothorax after CT-guided percutaneous pulmonary puncture biopsy （CT-PCNB） based on nomograms. Methods A total of 246 patients who underwent CT-PCNB examination in the hospital from October 2020 to October 2023 were selected and divided into training set （n = 144） and validation set （n = 102） using a random sampling method. In the training set， univariate and multivariate logistic regression analyses were performed to identify risk factors for pneumothorax after CT-PCNB. A nomogram model was constructed based on the identified risk factors， and its accuracy was validated using the validation set. Results Multifactorial logistic regression analysis showed that age ≥ 60 years， concomitant underlying lung disease， lesion diameter < 2 cm， distance from lesion to pleura ≥ 10 mm， puncture through interlobular pleura， and ≥ 2 pleural punctures were the risk factors for pneumothorax after CT-PCNB in the training set （P < 0.05）. A nomogram model was constructed based on these six factors. The ROC curve results for the training set showed an AUC of 0.852， sensitivity of 84.50%， and specificity of 67.50%. The nomogram model was validated using the validation set， with ROC curve results showing an AUC of 0.845， sensitivity of 83.00%， and specificity of 69.50%. There was no statistically significant difference between the predicted and actual values in both the training and validation sets （χ2 = 1.803， 1.225； P > 0.05）， indicating clinical validity. Conclusion The nomogram model constructed based on the risk factors for pneumothorax after CT-PCNB has high predictive efficacy and is clinically meaningful.

Key words: lung puncture biopsy, tomography, postoperative pneumothorax, risk prediction, columnar graphic modeling