神经外科ICU患者谵妄风险预测模型的构建与验证

Development and Validation of Delirium Prediction Model for Neurosurgical ICU Patients

目的构建并验证神经外科ICU患者谵妄风险预测模型,探讨其在神经外科ICU患者谵妄风险预测中的应用价值。方法选取2018年11月—2019年5月入住南京市某三级甲等医院神经外科ICU的665例患者作为建模组,将谵妄组(n=229)和非谵妄组(n=436)各项危险因素进行对比,采用Logistic回归构建模型,应用受试者操作特征曲线下面积检验模型区分度,利用Hosmer-Lemeshow拟合优度检验评价模型校准度。于2019年6—9月选取285例神经外科ICU患者对模型进行验证。结果最终进入模型的预测因子为性别(OR=2.075)、年龄(OR=1.047)、C反应蛋白浓度(OR=3.551)、身体约束(OR=1.011)、使用镇静药物(OR=9.768)、使用利尿或脱水类药物(OR=3.123)和格拉斯哥昏迷评分(OR=0.616)。Hosmer-Lemeshow拟合优度检验P=0.398,受试者操作特征曲线下面积为0.919,约登指数为0.705,灵敏度为0.847,特异度为0.858。模型验证结果:灵敏度65.5%、特异度96.5%、预测准确率87.4%。结论本模型在预测神经外科ICU患者谵妄发生风险中具有较好的效能,可为医护人员早期采取预防性管理措施提供参考。

Objective To develop and validate a delirium prediction model for neurosurgical ICU patients and determine its clinical value.Methods A total of 665 neurosurgical ICU patients in a tertiary grade-A Hospital in Nanjing were recruited from November 2018 to May 2019.Data of delirious patients(229 cases)were compared with those of non-delirious patients(436 cases)to identify the predictors of neurosurgical ICU delirium,and the logistic regression was used to develop the model.The discrimination of the model was measured using the area under the receiver operating characteristic curve(AUROC).Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration ability of the model.A total of 285 patients in neurosurgical ICU from June 2019 to September 2019 were involved to validate the developed model.Results Seven independent predictors of neurosurgical ICU delirium were identified,including gender(OR=2.075),age(OR=1.047),C-reactive protein concentration(OR=3.551),use of physical restriction(OR=1.011),use of sedatives(OR=9.768),use of diuretic or dehydrating drugs(OR=3.123),and Glasgow Coma Scale(OR=0.616).Hosmer-Lemeshow goodness-of-fit test showed that the consistency between the predicted delirium occurrence probability and the actual delirium occurrence probability was good(P=0.398).The AUROC of the model was 0.919,and the Youden’s index 0.705,with sensitivity and specificity of 0.847 and 0.858,respectively.For model validation,the sensitivity and specificity was 65.5%and 96.5%,respectively,and the predictive accuracy of the model 87.4%.Conclusion The developed model has good predictive power in predicting the risk of delirium in neurosurgical ICU,and provides reference for health care providers to prevent delirium at an early stage.