Clinical and Procedural Predictive Model for Pneumothorax Risk After CT-Guided Lung Biopsy

Area of Science:

• Pulmonary Medicine
• Radiology
• Medical Informatics

Background:

• CT-guided percutaneous lung nodule biopsy is a common procedure for diagnosing lung abnormalities.
• Pneumothorax is a significant complication, occurring in approximately 22% of patients post-biopsy.
• Predictive models are needed to identify patients at higher risk and optimize management.

Purpose of the Study:

• To develop and validate a predictive model for pneumothorax following CT-guided percutaneous lung nodule biopsy.
• To integrate clinical, imaging, and procedural variables for enhanced predictive accuracy.
• To assess the model's performance in discrimination, calibration, and clinical utility.

Main Methods:

• Retrospective enrollment of 395 patients, divided into training (n=276) and validation (n=119) cohorts.
• Univariable logistic regression identified candidate variables (P < 0.15) and clinical relevance.
• Two models were fitted: a baseline (clinical/imaging) and a full model (including procedural variables); assessed using ROC curves, calibration plots, and decision curve analysis (DCA).

Main Results:

• Pneumothorax incidence was 22.1% (training) and 22.7% (validation).
• The full model, incorporating BMI, emphysema grade, patient position, and needle adjustments, showed improved discrimination (AUC 0.722 vs 0.630 in training; AUC 0.720 vs 0.670 in validation) and higher sensitivity.
• Both models demonstrated good calibration; the full model offered greater net benefit across most threshold probabilities per DCA.

Conclusions:

• A combined model using clinical, imaging, and procedural characteristics effectively predicts pneumothorax risk after CT-guided lung nodule biopsy.
• This model can enhance peri-operative risk communication between clinicians and patients.
• It supports improved peri-procedural risk management strategies for CT-guided lung biopsies.