Zhou Xu-Hua, Duan Di-Fei, Zhang Meng, Liu Shuang, Lv Jing, Wang Yi, Chen Lin, Zhang Ying-Jun, Gu Bo, Chen Qian
Hemodialysis Center, Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Sichuan, People's Republic of China.
J Adv Nurs. 2025 Sep 1. doi: 10.1111/jan.70154.
To develop and validate a machine learning-based risk prediction model for delirium in older inpatients.
A prospective cohort study.
A prospective cohort study was conducted. Eighteen clinical features were prospectively collected from electronic medical records during hospitalisation to inform the model. Four machine learning algorithms were employed to develop and validate risk prediction models. The performance of all models in the training and test sets was evaluated using a combination of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, Brier score, and other metrics before selecting the best model for SHAP interpretation.
A total of 973 older inpatient data were utilised for model construction and validation. The AUC of four machine learning models in the training and test sets ranged from 0.869 to 0.992; the accuracy ranged from 0.931 to 0.962; and the sensitivity ranged from 0.564 to 0.997. Compared to other models, the Random Forest model exhibited the best overall performance with an AUC of 0.908 (95% CI, 0.848, 0.968), an accuracy of 0.935, a sensitivity of 0.992, and a Brier score of 0.053.
The machine learning model we developed and validated for predicting delirium in older inpatients demonstrated excellent predictive performance. This model has the potential to assist healthcare professionals in early diagnosis and support informed clinical decision-making.
By identifying patients at risk of delirium early, healthcare professionals can implement preventive measures and timely interventions, potentially reducing the incidence and severity of delirium. The model's ability to support informed clinical decision-making can lead to more personalised and effective care strategies, ultimately benefiting both patients and healthcare providers.
This study was reported in accordance with the TRIPOD statement.
No patient or public contribution.
开发并验证一种基于机器学习的老年住院患者谵妄风险预测模型。
一项前瞻性队列研究。
进行了一项前瞻性队列研究。前瞻性地从住院期间的电子病历中收集了18项临床特征以构建模型。采用四种机器学习算法来开发和验证风险预测模型。在选择用于SHAP解释的最佳模型之前，使用受试者工作特征曲线下面积（AUC）、准确率、灵敏度、Brier评分和其他指标来评估所有模型在训练集和测试集上的性能。
共使用973例老年住院患者数据进行模型构建和验证。四个机器学习模型在训练集和测试集上的AUC范围为0.869至0.992；准确率范围为0.931至0.962；灵敏度范围为0.564至0.997。与其他模型相比，随机森林模型表现出最佳的整体性能，AUC为0.908（95%CI，0.848，0.968），准确率为0.935，灵敏度为0.992，Brier评分为0.053。
我们开发并验证的用于预测老年住院患者谵妄的机器学习模型表现出优异的预测性能。该模型有潜力协助医疗保健专业人员进行早期诊断并支持明智的临床决策。
通过早期识别有谵妄风险的患者，医疗保健专业人员可以实施预防措施和及时干预，有可能降低谵妄的发生率和严重程度。该模型支持明智临床决策的能力可带来更个性化和有效的护理策略，最终使患者和医疗保健提供者都受益。
本研究按照TRIPOD声明进行报告。
无患者或公众贡献。
Zotero 插件