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Updated: May 21, 2025
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Quantitative lung CT indicators have good predictive ability for hypoxemia during one-lung ventilation in radical
Jing Peng1, Liang Ma1, Fei Liao2
1Department of Anesthesiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, No. 519 Kunzhou Road, Kunming, 650118, Yunnan, China.
Purpose:
We aimed to identify the preoperative risk factors for hypoxemia during one-lung ventilation (OLV) in patients who underwent thoracoscopic radical resection for lung cancer and to establish a prediction model.
Methods:
This retrospective study included 268 patients who underwent video-assisted thoracoscopic surgery (VATS) radical resection for lung cancer at Yunnan Cancer Hospital from October 2021 to June 2022. Logistic regression analysis was performed to identify independent preoperative risk factors for hypoxemia during OLV. A prediction model was established, and its predictive efficacy was evaluated with the consistency index (C-index) and the area under the receiver operating characteristic curve (AUC).
Results:
The multivariate analysis demonstrated that the ratio of dependent lung FLV to total lung FLV (odds ratio [OR] 0.8434; 95% confidence interval [CI] 0.7281-0.9623), dependent lung HU value (OR 0.9676; 95% CI 0.9419-0.9895), dependent lung LAV% (OR 1.1838; 95% CI 1.0856-1.3138), and DLCO% pred (OR 0.9632; 95% CI 0.9353-0.9864) were independent preoperative risk factors affecting OLV hypoxemia. The prediction model that was constructed by this indicator was internally validated, with a C-index of 0.963, an AUC of 0.96 (95% CI 0.94-0.99) for the training set, and an AUC of 0.92 (95% CI 0.83-1) for the test set.
Conclusion:
CT-based quantitative indicators of the dependent lung are strong predictors of hypoxemia during OLV for lung cancer patients. This prediction model helps anesthesiologists to intuitively and accurately identify patients who may experience hypoxemia during OLV before surgery and develop individualized management strategies.
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