Association of Diaphragmatic Mobility and Thickening Fraction with Postoperative Pulmonary Complications in Patients Undergoing Thoracic Surgery and Their Application in Prediction

Abudushalamu Aisiaiti,&ast; Aiwuzaili Ajiaikebaier,&ast; Aini Maimaitiming, Qiang Geng, Bichen He, Jinhui Sun, Bing Zhang Center for Anesthesia and Perioperative Medicine, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, 830011, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bing Zhang, Center for Anesthesia and Perioperative Medicine, Xinjiang Medical University Affiliated Tumor Hospital, No. 789, Suzhou East Road, Urumqi, 830011, People’s Republic of China, Tel +86-9917819021, Email [email protected]: To investigate the association of diaphragmatic mobility and thickening fraction with postoperative pulmonary complications (PPCs) in patients undergoing thoracic surgery and evaluate their values in predicting PPCs.Patients and Methods: One hundred and nine consecutive patients undergoing thoracic surgery were prospectively enrolled. All patients underwent ultrasound measurements to obtain diaphragmatic mobility and thickening fraction. PPCs were systematically monitored and recorded from postoperative day 1 to 7. The binary logistic regression model was used to perform multivariate analysis, and the receiver operating characteristic (ROC) curve was used to evaluate predictive values.Results: PPCs occurred in 46 patients (42.2%). Multivariate analysis identified age, smoking, surgical sites, and mean diaphragmatic mobility and thickening fraction of operated side and nonoperated side as independent risk factors for PPCs. ROC curves revealed that the AUC of mean diaphragmatic mobility and thickening fraction for predicting PPCs in patients undergoing thoracic surgery was 0.722 [standard error (SE): 0.050, 95% confidence interval (CI): 0.623~0.821, P< 0.001] and 0.757 (SE: 0.050, 95% CI: 0.659~0.855, P< 0.001), respectively. The predictive model integrating age, smoking and surgical sites yielded an AUC of 0.810 (SE: 0.041, 95% CI: 0.728~0.891, P< 0.001), while the predictive model integrating age, smoking, surgical sites and mean diaphragmatic mobility or thickening fraction yielded an AUC of 0.849 (SE: 0.037, 95% CI: 0.777~0.922, P< 0.001) and 0.881 (SE: 0.033, 95% CI: 0.815~0.946, P< 0.001), respectively.Conclusion: Both diaphragmatic mobility and thickening fraction showed independent associations with PPCs following thoracic surgery, demonstrating moderate predictive values. The predictive models integrating age, smoking, surgical sites and diaphragmatic mobility or thickening fraction yielded high predictive values, suggesting significant clinical utility for risk stratification. Diaphragmatic mobility and thickening fraction offer a bedside, noninvasive, and cost-effective alternative for perioperative PPC prediction, particularly in resource-limited settings.Keywords: postoperative pulmonary complications, thoracic surgery, diaphragmatic mobility, diaphragmatic thickening fraction, prediction