关键词: 椎间盘切除融合术, 术后住院时间延长, 列线图

Abstract: Objective The aim of this study was to develop a clinical model for predicting prolonged postoperative length of stay （PLOS） after spinal discectomy and fusion. Methods Retrospective analysis was conducted based on the relevant data of 434 patients who underwent spinal discectomy and fusion which was extracted from the electronic medical record system. The main outcome variables of the study were prolonged PLOS. Cases that meet the inclusion criteria were randomly divided into a model training cohort and an internal validation cohort. LASSO regression was used to screen variables, and multivariate Logistic regression analysis was used to construct a prediction model and visualize it using nomogram. The discriminability, calibration, and clinical effectiveness of the model were evaluated through receiver operating characteristic curve （ROC curve）, calibration curve, and decision curve analysis （DCA）. Results The final prediction model included five variables： intraoperative blood transfusion, intraoperative neuroelectrophysiological monitoring, postoperative ICU admission, postoperative complications and postoperative drainage tube removal time. The area under the operating characteristic curve （AUC） of the training set and the validation set were 0.899 （95%CI：0.859-0.838） and 0.864 （95%CI：0.794-0.934） respectively, suggesting that the model has good discrimination. The calibration curve and DCA curve indicate that the model has good calibration and clinical practicality. Conclusion We have developed and validated a new predictive model for prolonged PLOS after spinal discectomy and fusion. The model has excellent comprehensive performance, which can better predict the risk of prolonged PLOS, guide clinical intervention as early as possible to shorten the length of stay, and provide data support for reducing hospitalization costs and implementing rapid recovery after spine surgery.

Key words: spinal discectomy and fusion, prolonged postoperative length of stay, nomogram