开发和验证一种新的列线图模型，以预测腰椎椎管狭窄症患者术后住院时间延长的风险。

BACKGROUND: Lumber spinal stenosis (LSS) is the increasingly reason for spine surgery for elder patients since China is facing the fastest-growing aging population. The aim of this research was to create a model to predict the probabilities of requiring a prolonged postoperative length of stay (PLOS) for lumbar spinal stenosis patients, minimizing the healthcare burden. METHODS: A total of 540 LSS patients were enrolled in this project. The outcome was a prolonged PLOS after spine surgery, defined as hospitalizations ≥ 75th percentile for PLOS, including the day of discharge. The least absolute shrinkage and selection operator (LASSO) was used to identify independent risk variables related to prolonged PLOS. Multivariable logistic regression analysis was utilized to generate a prediction model utilizing the variables employed in the LASSO approach. The receiver operating characteristic (ROC) curve's area under the curve (AUC) and the calibration curve's respective curves were used to further validate the model's calibration with predictability and discriminative capabilities. By using decision curve analysis, the resulting model's clinical effectiveness was assessed. RESULTS: Among 540 individuals, 344 had PLOS that was within the usual range of P75 (8 days), according to the interquartile range of PLOS, and 196 had PLOS that was above the normal range of P75 (prolonged PLOS). Four variables were incorporated into the predictive model, named: transfusion, operation duration, blood loss and involved spine segments. A great difference in clinical scores can be found between the two groups (P < 0.001). In the development set, the model's AUC for predicting prolonged PLOS was 0.812 (95% CI: 0.768-0.859), while in the validation set, it was 0.830 (95% CI: 0.753-0.881). The calibration plots for the probability showed coherence between the expected probability and the actual probability both in the development set and validation set respectively. When intervention was chosen at the potential threshold of 2%, analysis of the decision curve revealed that the model was more clinically effective. CONCLUSIONS: The individualized prediction nomogram incorporating five common clinical features for LSS patients undergoing surgery can be suitably used to smooth early identification and improve screening of patients at higher risk of prolonged PLOS and minimize health care.

背景：随着中国老龄化人口增长速度最快，腰椎管狭窄症（LSS）已成为老年患者脊柱手术的主要原因。本研究旨在建立一个预测腰椎管狭窄症患者术后延长住院时间（PLOS）概率的模型，以减轻医疗负担。

方法：本研究共纳入 540 例 LSS 患者。术后 PLOS 延长的结局定义为住院时间超过 PLOS 第 75 百分位数（包括出院当天）。采用最小绝对收缩和选择算子（LASSO）筛选与 PLOS 延长相关的独立风险变量。利用 LASSO 方法筛选出的变量进行多变量逻辑回归分析，建立预测模型。受试者工作特征（ROC）曲线下面积（AUC）和校准曲线分别评估模型的预测能力和校准度。通过决策曲线分析评估模型的临床有效性。

结果：540 例患者中，根据 PLOS 的四分位间距，344 例患者的 PLOS 在正常范围内（75 天），196 例患者的 PLOS 超过正常范围（PLOS 延长）。该预测模型纳入了输血、手术时间、出血量和受累脊柱节段 4 个变量。两组患者的临床评分有显著差异（P<0.001）。在开发集，该模型预测 PLOS 延长的 AUC 为 0.812（95%CI：0.768-0.859），在验证集，AUC 为 0.830（95%CI：0.753-0.881）。开发集和验证集的概率校准图均显示，预测概率与实际概率之间存在一致性。当干预概率设定在潜在阈值的 2%时，决策曲线分析表明，该模型具有更高的临床有效性。

结论：本研究建立的预测列线图纳入了 5 个常见的 LSS 患者临床特征，可为术后 PLOS 延长风险较高的患者提供个体化预测，并有助于早期识别和筛选，从而减轻医疗负担。