地西泮致患者嗜睡的危险因素分析及风险预测模型构建

Risk factor analysis and risk prediction model construction for diazepam-induced somnolence in patients

关键词:  地西泮  不良反应  风险预测模型  列线图

Key words:  diazepam  adverse reaction  risk prediction model  nomogram

摘要:

目的：探讨地西泮致使患者长时间嗜睡的危险因素并建立相关风险模型并评价，为地西泮的安全用药提供参考。方法：通过合理用药监测系统结合医院信息系统回顾性收集2020年7月至2021年12月柳州市人民医院住院静脉注射地西泮后使用氟马西尼促醒的患者的临床资料，对纳入的相关变量进行Logistic回归分析，筛选地西泮致患者嗜睡的独立影响因素。纳入独立影响因素建立列线图预测模型，采用受试者工作特征的曲线下面积和校准曲线分别评估模型的区分度、校准度，采用决策曲线分析用于评估模型的临床实用性，并通过Bootstrap法对模型进行内部验证。结果: 共纳入244例患者，其中出现嗜睡不良反应的患者有118例。通过二元Logistic逐步回归分析发现，患者的白蛋白小于30 g?L-1（OR=3.241, 95%CI: 1.364~7.701）、天冬氨酸氨基转移酶大于40 U?L-1（OR=2.589, 95%CI: 1.062~6.307）、凝血酶原时间大于14秒（OR=2.180, 95%CI: 1.138~4.175）是地西泮致患者产生嗜睡不良反应的独立危险因素，并建立发生不良反应风险预测列线图模型。该模型AUC = 0.688，特异度为0.651，灵敏度为0.653，Bootstrap法准确率为63.5%，Kappa值为0.26，校准曲线显示列线图预测的概率与实际概率之间存在较好的一致性，决策分析显示风险阈值在38% ~ 83%之间时该列线图具有临床应用价值。结论：此模型具有较好的拟合度、区分度、校准度和临床预测效能，可帮助医务人员预测使用地西泮可能发生的不良反应风险。

Abstract:

OBJECTIVE To investigate the risk factors for prolonged somnolence of patients caused by diazepam and to establish and evaluate the risk prediction model for the safe use of diazepam. METHODS The Prescription Automatic Screening System combined with the Hospital Information System were used to retrospectively collect the clinical data of patients who used diazepam injection followed by flumazenil injection in Liuzhou People's Hospital from July 2020 to December 2021. Logistic stepwise regression analysis was performed on the included relevant variables to screen the independent influencing factors of diazepam causing somnolence in patients. The included independent influences were modeled as risk prediction nomogram and its discrimination, calibration, prediction accuracy and clinical application value were assess by area under the curve of receiver operating characteristic, calibration curve, boostrap method, and decision curve analysis, respectively. RESULTS A total of 244 patients were included, of which 118 patients had drowsy adverse reactions. Binary logistic regression analysis revealed that patients with albumin less than 30 g?L-1 (OR=3.241, 95%CI: 1.364~7.701), aspartate aminotransferase greater than 40 U?L-1 (OR=2.589, 95%CI: 1.062~6.307), and prothrombin time greater than 14 seconds (OR=2.180, 95%CI: 1.138~4.175) were independent risk factors for diazepam-induced somnolence in patients, and a nomogram model was developed to predict the risk of diazepam adverse reactions. The AUC value of the model was 0.688, the specificity of the model was 0.651, the sensitivity of the model was 0.653, and the prediction accuracy of the model was 0.635 with a Kappa value of 0.26. The calibration curve showed good consistency between the prediction probability and the observation probability of nomogram. The decision curve analysis indicated that the nomogram could be applied clinically if the risk threshold was between 38% and 83%. CONCLUSION This model has good discrimination, calibration and certain predictive ability to help medical professionals predict the risk of possible adverse reactions with diazepam.