Department of Critical Care Medicine, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai 200065, China.
Department of Economic Management, Yingkou Institute of Technology, Yingkou 115014, Liaoning, China.
J Healthc Eng. 2021 Jun 8;2021:9959077. doi: 10.1155/2021/9959077. eCollection 2021.
We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort study, and 319 consecutive patients aged over 65 years from January 2018 to December 2019 were screened. Demographic characteristics and clinical variables were evaluated, and a final prediction model was developed using the multivariate logistic regression analysis. In the validation stage, 108 patients were included for temporal validation between January 2020 and June 2020. The effectiveness of the model was evaluated through discrimination and calibration. As a result, the prediction model contains seven risk factors (age, anesthesia method, score of mini-mental state examination, hypoxia, major hemorrhage, level of interleukin-6, and company of family members), which had an area under the receiver operating characteristics curve of 0.82 (95% confidence interval 0.76-0.88) and was stable after bootstrapping. The temporal validation resulted in an area under the curve of 0.80 (95% confidence interval 0.67-0.93). Our prediction model had excellent discrimination power in predicting postoperative delirium in elderly patients and could assist intensive care physicians with early prevention.
我们开发了一个针对在骨科手术后入住重症监护病房的老年患者发生谵妄的预测模型,并在同一医院对其预测能力进行了临时验证。在开发阶段,我们设计了一项前瞻性队列研究,筛选了 2018 年 1 月至 2019 年 12 月期间的 319 名连续 65 岁以上的患者。评估了人口统计学特征和临床变量,并使用多变量逻辑回归分析制定了最终预测模型。在验证阶段,纳入了 2020 年 1 月至 2020 年 6 月期间的 108 名患者进行临时验证。通过判别和校准评估了模型的有效性。结果,该预测模型包含 7 个风险因素(年龄、麻醉方法、简易精神状态检查评分、缺氧、大出血、白细胞介素-6 水平和家属陪伴),其受试者工作特征曲线下面积为 0.82(95%置信区间为 0.76-0.88),且在 bootstrap 后稳定。临时验证的曲线下面积为 0.80(95%置信区间为 0.67-0.93)。我们的预测模型在预测老年患者术后谵妄方面具有出色的判别能力,可帮助重症监护医师进行早期预防。