Shaw Kendrick Matthew, Shao Yu-Ping, Ghanta Manohar, Junior Valdery Moura, Kimchi Eyal Y, Houle Timothy T, Akeju Oluwaseun, Westover Michael Brandon
Department of Anesthesia, Pain, and Critical care Medicine, Massachusetts General Hospital, Boston, MA, United States.
Harvard Medical School, Boston, MA, United States.
JMIR Med Inform. 2025 Apr 18;13:e60442. doi: 10.2196/60442.
Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.
This study aims to develop a machine learning algorithm to identify patients at the highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.
We developed and compared multiple machine learning models on a retrospective dataset of all hospitalized adult patients with recorded Confusion Assessment Method (CAM) screens at a major academic medical center from April 2, 2016, to January 16, 2019, comprising 23,006 patients. The patient's age, gender, and all available laboratory values, vital signs, prior CAM screens, and medication administrations were used as potential predictors. Four machine learning approaches were investigated: logistic regression with L1-regularization, multilayer perceptrons, random forests, and boosted trees. Model development used 80% of the patients; the remaining 20% was reserved for testing the final models. Laboratory values, vital signs, medications, gender, and age were used to predict a positive CAM screen in the next 24 hours.
The boosted tree model achieved the greatest predictive power, with an area under the receiver operator characteristic curve (AUROC) of 0.92 (95% CI 0.913-9.22), followed by the random forest (AUROC 0.91, 95% CI 0.909-0.918), multilayer perceptron (AUROC 0.86, 95% CI 0.850-0.861), and logistic regression (AUROC 0.85, 95% CI 0.841-0.852). These AUROCs decreased to 0.78-0.82 and 0.74-0.80 when limited to patients who currently do not or never have had delirium, respectively.
A boosted tree machine learning model was able to identify hospitalized patients at elevated risk for delirium in the next 24 hours. This may allow for automated delirium risk screening and more precise targeting of proven and investigational interventions to prevent delirium.
谵妄在住院患者中很常见，且与发病率和死亡率增加相关。尽管如此，谵妄仍未得到充分诊断，许多机构没有足够资源持续进行有效的筛查和预防。
本研究旨在开发一种机器学习算法，根据电子病历中的可用数据，每天自动识别医院中谵妄风险最高的患者，降低大规模谵妄筛查的障碍。
我们在一个回顾性数据集中开发并比较了多种机器学习模型，该数据集包含2016年4月2日至2019年1月16日在一家大型学术医疗中心所有记录了混乱评估方法（CAM）筛查结果的成年住院患者，共23006例。患者的年龄、性别以及所有可用的实验室值、生命体征、既往CAM筛查结果和用药情况被用作潜在预测因素。研究了四种机器学习方法：L1正则化逻辑回归、多层感知器、随机森林和增强树。模型开发使用了80%的患者；其余20%留作测试最终模型。使用实验室值、生命体征、药物、性别和年龄来预测未来24小时内CAM筛查结果为阳性。
增强树模型具有最大的预测能力，受试者工作特征曲线下面积（AUROC）为0.92（95%CI 0.913 - 0.922），其次是随机森林（AUROC 0.91，95%CI 0.909 - 0.918）、多层感知器（AUROC 0.86，95%CI 0.850 - 0.861）和逻辑回归（AUROC 0.85，95%CI 0.841 - 0.852）。当仅限于目前没有或从未有过谵妄的患者时，这些AUROC分别降至0.78 - 0.82和0.74 - 0.80。
增强树机器学习模型能够识别未来24小时内谵妄风险升高的住院患者。这可能实现谵妄风险的自动筛查，并更精准地针对已证实和正在研究的预防谵妄的干预措施。
