Zhou Yan, Huang Jian, Cao Lei, Gao Yaoyi, Li Yihao, Wang Beili, Pan Baishen, Guo Wei, Cang Jing
Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, 111 Yi Xue Yuan Road, Shanghai, 200032, PR China.
Department of Anesthesiology, Zhongshan Hospital, Fudan University, 111 Yi Xue Yuan Road, Shanghai, 200032, PR China.
Heliyon. 2023 Jul 22;9(8):e18560. doi: 10.1016/j.heliyon.2023.e18560. eCollection 2023 Aug.
To make early predictions of PACU VAS before surgery, we created a novel nomogram for the early prediction of PACU VAS in patients having laparoscopic radical excision of colorectal cancer with fentanyl.
From July 2018 to December 2020, a total of 101 patients in Zhongshan Hospital Affiliated to Fudan University who underwent laparoscopic radical resection of colorectal cancer were enrolled in this study. For feature selection, a stepwise regression model was utilized. Multivariable logistic regression analysis was used to establish a prediction model. We incorporated age, gender, weight, height, fentanyl dosage during operation, operation time, and genotype, and this was presented with a nomogram. The nomogram's performance was evaluated in terms of discrimination and clinical utility.
The signature, which comprised of seven carefully chosen characteristics, was linked to the PACU VAS for the development dataset. Predictors contained in the individualized prediction nomogram included age, gender, weight, height, fentanyl dosage during operation, operation time, and genotype. With an area under the ROC curve of 0.877 (95% CI, 0.6874-1.0000), the model showed good discrimination. The nomogram still had good discrimination. Decision curve analysis demonstrated that the nomogram was clinically useful.
The nomogram presented in this study incorporates age, gender, weight, height, fentanyl dosage during operation, operation time, and genotype and can be conveniently used to facilitate the individualized prediction of PACU VAS in patients undergoing laparoscopic radical resection of colorectal cancer with fentanyl.
为了在手术前对患者麻醉后恢复室（PACU）疼痛视觉模拟评分（VAS）进行早期预测，我们创建了一种新型列线图，用于早期预测接受腹腔镜下结直肠癌根治术并使用芬太尼的患者的PACU VAS。
2018年7月至2020年12月，复旦大学附属中山医院共有101例行腹腔镜下结直肠癌根治术的患者纳入本研究。为进行特征选择，采用逐步回归模型。使用多变量逻辑回归分析建立预测模型。我们纳入了年龄、性别、体重、身高、术中芬太尼剂量、手术时间和基因型，并以列线图呈现。通过区分度和临床实用性对列线图的性能进行评估。
该特征集由七个精心挑选的特征组成，与开发数据集中的PACU VAS相关。个性化预测列线图中的预测因素包括年龄、性别、体重、身高、术中芬太尼剂量、手术时间和基因型。该模型的受试者工作特征曲线（ROC）下面积为0.877（95%可信区间，0.6874 - 1.0000），显示出良好的区分度。列线图仍具有良好的区分度。决策曲线分析表明该列线图具有临床实用性。
本研究中呈现的列线图纳入了年龄、性别、体重、身高、术中芬太尼剂量、手术时间和基因型，可方便地用于促进对接受腹腔镜下结直肠癌根治术并使用芬太尼的患者的PACU VAS进行个性化预测。
