基于术前指标的老年髋部骨折全麻手术患者并发苏醒期谵妄预测模型的构建-现代医学

基于术前指标的老年髋部骨折全麻手术患者并发苏醒期谵妄预测模型的构建

单位：扬州大学附属医院手术室, 江苏扬州 225000

分类号：R683.4

出版年·卷·期（页码）：2026·54·第七期（1132-1140）

摘要：

目的：基于术前指标构建老年髋部骨折全麻手术患者苏醒期谵妄(ED)的列线图模型，分析其预测性能。方法：本研究采用单中心回顾性队列研究设计，选取2021年6月至2025年2月扬州大学附属医院收治的老年髋部骨折全麻手术患者230例，收集患者一般资料、既往病史、麻醉相关资料、手术相关资料及实验室指标。依据患者是否发生苏醒期ED，将其分为ED组(n=63)、非ED组(n=167)。单因素分析初筛变量后，借助最小绝对值收敛和选择算子(LASSO)回归结合十折交叉验证降维筛选自变量，将筛选出的变量纳入多因素 Logistic 回归确定老年髋部骨折全麻术后并发 ED 的独立危险因素，构建列线图预测模型。采用Bootstrap重抽样法行内部验证，以校正后C-index、校准曲线及决策曲线分别评价模型的区分度、校准度及临床净获益。结果：230例患者共63例(27.39%)出现苏醒期ED。ED组患者年龄、糖尿病占比、卒中史占比、受伤至手术时长、术前匹兹睡眠质量指数量表(PSQI)评分、主观认知下降占比及衰弱状态高于非ED组，术前白蛋白水平低于非ED组(均P＜0.05)。LASSO回归筛选出6个非零系数变量：年龄、糖尿病、术前PSQI评分、主观认知下降、衰弱状态和白蛋白。多因素Logistic回归分析显示，年龄、糖尿病、术前PSQI评分、存在主观认知下降、衰弱状态和白蛋白均是患者发生ED的独立影响因素(均P＜0.05)。基于上述因素构建预测模型并可视化为列线图，通过Bootstrap法内部验证后显示，列线图模型预测患者并发ED的C-index指数为0.974，校准曲线的平均绝对误差为0.018；决策曲线显示，当阈值概率取0.05~0.98，应用模型预测均有较高临床获益。结论：基于年龄、糖尿病、术前PSQI评分、主观认知下降、衰弱状态、白蛋白构建的老年髋部骨折全麻手术患者并发ED的列线图模型表现出较好的预测能力。

Objective: To establish a nomogram model for predicting emergence delirium(ED) in elderly patients undergoing hip fracture surgery under general anesthesia based on preoperative indicators, and to verify its predictive efficacy. Methods: A single-center retrospective cohort study was conducted. A total of 230 elderly patients receiving general anesthesia for hip fracture surgery admitted to the Affiliated Hospital of Yangzhou University between June 2021 and February 2025 were enrolled. Baseline demographics, past medical history, anesthesia-related parameters, surgical data and laboratory test results were collected. Patients were divided into the ED group(n=63) and non-ED group(n=167) according to the occurrence of postoperative ED. Univariate analysis was used for preliminary variable screening. The least absolute shrinkage and selection operator(LASSO) regression combined with 10-fold cross-validation was adopted for dimensionality reduction and predictor selection. Variables with non-zero coefficients were further incorporated into multivariate Logistic regression to identify independent risk factors for ED, followed by nomogram construction. Bootstrap resampling was performed for internal validation. The corrected C-index, calibration curve and decision curve analysis were applied to assess discrimination, calibration and clinical net benefit of the model, respectively. Results: Among the 230 enrolled patients, 63 cases(27.39%) developed ED. Compared with the non-ED group, the ED group had significantly older age, higher proportions of diabetes mellitus and stroke history, longer interval from injury to surgery, higher Pittsburgh Sleep Quality Index(PSQI)score, higher rate of subjective cognitive decline and frailty status, as well as lower preoperative serum albumin level(all P<0.05). Six predictors with non-zero regression coefficients were screened out via LASSO regression, including age, diabetes mellitus, PSQI score, subjective cognitive decline, frailty status and serum albumin level. Multivariate Logistic regression revealed that advanced age, diabetes mellitus, elevated PSQI score, presence of subjective cognitive decline, frailty status and serum albumin level were independent risk factors for ED(all P<0.05). A nomogram prediction model was established based on these independent risk factors. Internal validation via Bootstrap resampling yielded a corrected C-index of 0.974 and a mean absolute error of 0.018 for the calibration curve. Decision curve analysis demonstrated favorable clinical net benefit of the model within the threshold probability range of 0.05-0.98. Conclusion: The nomogram model constructed using age, diabetes mellitus, PSQI score, subjective cognitive decline, frailty status and serum albumin achieves satisfactory predictive performance for ED in elderly hip fracture patients under general anesthesia.

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