Risk prediction and model construction for postoperative electrolyte disorders in lung cancer patients undergoing thoracoscopic surgery

Chen Ling; Zhang Jing; Yue Li; Wang Xiaohong; Jiabaaga; Luo Wei; Zhang Boda

doi:10.11986/j.issn.1673-873X.2025.02.13

Abstract：Objective To analyze risk factors for electrolyte disorders after thoracoscopic lung resection in lung cancer patients and construct a risk prediction model to inform clinical decision-making.Methods Clinical data from 198 lung cancer patients admitted to the Department of Thoracic Surgery at Affiliated Hospital of North Sichuan Medical College(January 2019-Janu-ary 2024)were retrospectively collected.Patients were randomly divided into a training set(n=148)and a validation set(n=50)in a 7∶3 ratio.The training set was further categorized into electrolyte disorder and normal electrolyte groups based on postoperative status.Clinical characteristics were compared,and logistic regression identified risk factors for the prediction model.Receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA)evaluated the model's discrimination,calibration,and clinical applicability.Results No significant differences existed between training and validation sets(P＞0.05).In the training set,significant differences were observed in operative duration,intraoperative blood loss,postoperative drainage volume,age,whether or not to have received chemoradiotherapy,and history of hypertension be-tween electrolyte disorder and normal groups(P＜0.05).Multivariate analysis identified operative duration(OR=4.219,95％CI:1.347-13.218),intraoperative blood loss(OR=1.016,95％CI:1.006-1.026),postoperative drainage volume(OR=1.002,95％CI:1.001-1.004),and history of hypertension(OR=3.140,95％CI:1.189-8.292)as independent in-fluence factors(P＜0.05).The nomogram prediction model was constructed based on the above factors,and the area under the curve(AUC)of the constructed model in the training set was 0.858(95％CI:0.796-0.919),the sensitivity was 0.872,and the specificity was 0.694;the AUC in the validation set was 0.865(95％CI:0.752-0.978),demonstrating that the model had good discrimination.The calibration curve showed that the predicted curve of the model was close to the actual ob-served curve.Clinical decision curve analysis showed that the model had good clinical applicability.Conclusion Prolonged op-erative duration,higher intraoperative blood loss,increased postoperative drainage,and history of hypertension independently predict postoperative electrolyte disorders.The constructed model on the basis of these factors demonstrates robust predictive performance for guiding preventive strategies.