Development and validation of a nomogram model to predict postoperative delirium after resection of esophageal cancer.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-07-21 DOI:10.1038/s41598-025-11255-9

Xia Shen, Long Yang, Lei Jiang, Qian Wang, Yuan-Yuan Liu, Shao-Zheng Song, Jian-Feng Zhang, Ping Cai, Zhun-Zhun Liu

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Abstract

The study aimed to establish and validate a nomogram model to predict postoperative delirium (POD) among esophageal cancer resection patients. Clinical data of 396 patients with esophageal cancer who underwent esophagectomy from November 2020 to June 2023 in the electronic medical records of cardiothoracic Surgery, Affiliated Hospital of Jiangnan University. Participants were randomly divided into training and testing sets in a 7:3 ratio. Predictors were screened by Least absolute shrinkage and selection operator (LASSO) regression analysis and a nomogram model was built. The discrimination and consistency of the model were evaluated using the area under the receiver operating characteristic curve (AUC), C-statistic, Brier score, Hosmer-Lemeshow goodness-of-fit test, calibration curve and decision curve analysis (DCA). The results were validated using 1000 bootstraps resampling internal validation and testing set. Among 32 potential predictors, the final prediction model included 6 variables: postoperative pain, postoperative infection, dexmedetomidine use, propofol use, duration of mechanical ventilation, and Prognostic Nutritional Index (PNI). The model showed a good discrimination with an AUC of 0.919 (95% CI: 0.885- 0.953) in the training set, and adjusted to 0.911 (95% CI: 0.878- 0.944) and 0.871 (95% CI: 0.802- 0.940) in the internal validation and the testing set, respectively. ROC curves, calibration curves, DCA curves, C-statistic, Brier score and Hosmer-Lemeshow goodness-of-fit test showed excellent model performance. This study successfully established and validated the first POD prediction model for patients with esophageal cancer resection. It could accurately predict the occurrence of POD and effectively identify the high-risk patients, which is of great significance for improving the risk stratification of the population and for implementing targeted prevention intervention measures.

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食管癌术后谵妄的nomogram预测模型的建立与验证。

本研究旨在建立并验证一种预测食管癌切除术后谵妄（POD）的nomogram模型。江南大学附属医院心胸外科电子病历中2020年11月至2023年6月396例食管癌行食管切除术患者的临床资料。参与者按7:3的比例随机分为训练组和测试组。通过最小绝对收缩和选择算子（LASSO）回归分析筛选预测因子，并建立nomogram模型。采用受试者工作特征曲线下面积（AUC）、c统计量（C-statistic）、Brier评分、Hosmer-Lemeshow拟合优度检验、校正曲线和决策曲线分析（DCA）评价模型的鉴别性和一致性。使用1000个bootstrap重新采样内部验证和测试集对结果进行验证。在32个潜在预测因素中，最终的预测模型包括6个变量：术后疼痛、术后感染、右美托咪定使用、异丙酚使用、机械通气持续时间和预后营养指数（PNI）。该模型在训练集中具有良好的判别性，AUC为0.919 (95% CI: 0.885 ~ 0.953)，在内部验证和测试集中分别调整为0.911 （95% CI: 0.878 ~ 0.944）和0.871 （95% CI: 0.802 ~ 0.940）。ROC曲线、校正曲线、DCA曲线、c统计量、Brier评分和Hosmer-Lemeshow拟合优度检验均表现出良好的模型性能。本研究成功建立并验证了首个食管癌切除术患者POD预测模型。能够准确预测POD的发生，有效识别高危患者，对改善人群风险分层，实施有针对性的预防干预措施具有重要意义。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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