Chang Chun-Wei, Chang Chien-Hung, Chien Chia-Yin, Jiang Jian-Lin, Liu Tsai-Wei, Wu Hsiu-Chuan, Chang Kuo-Hsuan
Department of Neurology, Chang Gung Memorial Hospital-Linkou Medical Center, No.5, Fusing St., Guishan Dist., Taoyuan City, 333423, Taiwan.
Collage of Medicine, Chang Gung University, Taoyuan, Taiwan.
Sci Rep. 2024 Dec 28;14(1):31066. doi: 10.1038/s41598-024-82280-3.
Hospital-acquired infections (HAIs) are serious complication for patients with acute ischemic stroke (AIS), often resulting in poor functional outcomes. However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed the Gradient Boosting matching learning algorithm to establish predictive models for HAI occurrence in AIS patients and poor 30-day functional outcomes (modified Rankin Scale > 2) in AIS patients with HAI by analyzing electronic health records from 6560 AIS patients. Model performance was evaluated through internal cross-validation and external validation using an independent cohort of 3521 AIS patients. The established models demonstrated robust predictive performance for HAI in AIS patients, achieving area under the receiver operating characteristic curves (AUROCs) of 0.857 ± 0.008 during internal validation and 0.825 ± 0.002 during external validation. For AIS patients with HAI, the second model effectively predict poor 30-day functional outcomes, with AUROCs of 0.905 ± 0.009 during internal validation and 0.907 ± 0.002 during external validation. In conclusion, machine learning models effectively identify the HAI occurrence and predict poor 30-day functional outcomes in AIS patients with HAI. Future prospective studies are crucial for validating and refining these models for clinical application, as well as for developing an accessible flowchart or scoring system to enhance clinical practices.
医院获得性感染（HAIs）是急性缺血性卒中（AIS）患者的严重并发症，常导致功能预后不良。然而，现有的模型均无法特异性预测AIS患者的医院获得性感染。因此，我们采用梯度提升匹配学习算法，通过分析6560例AIS患者的电子健康记录，建立AIS患者发生医院获得性感染以及发生医院获得性感染的AIS患者30天功能预后不良（改良Rankin量表评分>2）的预测模型。使用3521例AIS患者的独立队列，通过内部交叉验证和外部验证对模型性能进行评估。所建立的模型在预测AIS患者发生医院获得性感染方面表现出强大的预测性能，内部验证期间受试者操作特征曲线下面积（AUROCs）为0.857±0.008，外部验证期间为0.825±0.002。对于发生医院获得性感染的AIS患者，第二个模型能有效预测30天功能预后不良，内部验证期间AUROCs为0.905±0.009，外部验证期间为0.907±0.002。总之，机器学习模型能有效识别AIS患者发生医院获得性感染的情况，并预测发生医院获得性感染的AIS患者30天功能预后不良。未来的前瞻性研究对于验证和完善这些模型以用于临床应用，以及开发易于使用的流程图或评分系统以改进临床实践至关重要。
