School of Mechanical Engineering, Sichuan University, Chengdu, China.
Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Eur Geriatr Med. 2022 Feb;13(1):173-183. doi: 10.1007/s41999-021-00562-9. Epub 2021 Sep 23.
To develop a machine learning model that predicts delirium risk in geriatric internal medicine inpatients.
A prospective cohort study of internal medicine wards in a tertiary care hospital in China. Blinded observers assessed delirium using the Confusion Assessment Method (CAM). The data set was randomly divided into a training set (70%) and a test set (30%). The model was trained on the training set using the decision tree and the five-fold cross-validation, and then the model performance was evaluated on the test set. Under-sampling was used to address the class imbalance. The discriminatory power of the model was measured by the area under the receiver operating characteristic curve (AUC) and F1 score. The data set comprised 740 patients from March 2016 to January 2017.
The training set included 518 patients; the median (IQR) age was 84 (79-87) years; 364 (70.3%) were men; 71 (13.7%) with delirium. The test set included 222 patients; the median (IQR) age was 84.5 (79-87) years; 163 (73.4%) were men; 30 (13.5%) with delirium. In total, the data set included 740 hospital admissions with a median (IQR) age of 84 (79-87) years, 527 (71.2%) were men, and 101 (13.6%) with delirium. From 32 potential predictors, we included five variables in the predictive model: depression, cognitive impairment, types of drugs, nutritional status, and activity of daily life (ADL). The mean AUC on the training set was 0.967, the AUC and F1 score on the test set was 0.950 and 0.810, respectively. The model achieved 93.3% sensitivity, 94.3% specificity, 71.8% positive predictive value, 98.9% negative predictive value, and 94.1% accuracy on the test set.
This machine learning model may allow more precise targeting of delirium prevention and could support clinical decision making in geriatric internal medicine wards.
开发一种可预测老年内科住院患者发生谵妄风险的机器学习模型。
这是一项在中国一家三级护理医院内科病房进行的前瞻性队列研究。采用意识模糊评估法（CAM）对谵妄进行盲法评估。数据集随机分为训练集（70%）和测试集（30%）。使用决策树和五重交叉验证对训练集进行模型训练，然后在测试集上评估模型性能。采用欠采样来解决类别不平衡问题。通过接受者操作特征曲线下的面积（AUC）和 F1 评分来衡量模型的判别能力。该数据集包含了 2016 年 3 月至 2017 年 1 月间的 740 名患者。
训练集纳入 518 例患者；中位数（IQR）年龄为 84（79-87）岁；364 例（70.3%）为男性；71 例（13.7%）发生谵妄。测试集纳入 222 例患者；中位数（IQR）年龄为 84.5（79-87）岁；163 例（73.4%）为男性；30 例（13.5%）发生谵妄。总共纳入 740 例住院患者，中位数（IQR）年龄为 84（79-87）岁，527 例（71.2%）为男性，101 例（13.6%）发生谵妄。从 32 个潜在预测因子中，我们纳入了预测模型中的五个变量：抑郁、认知障碍、药物种类、营养状况和日常生活活动（ADL）。训练集的平均 AUC 为 0.967，测试集的 AUC 和 F1 评分分别为 0.950 和 0.810。该模型在测试集上的灵敏度为 93.3%、特异性为 94.3%、阳性预测值为 71.8%、阴性预测值为 98.9%和准确率为 94.1%。
该机器学习模型可更精准地预测谵妄的发生，有助于为老年内科病房的临床决策提供支持。
