Prediction of upcoming urinary tract infection after intracerebral hemorrhage: a machine learning approach based on statistics collected at multiple time points

Frontiers in Neurology

Neurology

Xin Tie¹  Haoxiang Wang²  Jinhao Yang²  Zhouyang Huang²  Wenyao Cui³  Jianguo Xu³  Chaoyue Chen³  Yanjie Zhao³ 

[1] Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China;Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China;Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China;Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China;

关键词: urinary tract infection;    intracerebral hemorrhage;    stroke;    critical care;    machine learning;   

DOI  :  10.3389/fneur.2023.1223680

 received in 2023-05-30, accepted in 2023-08-18,  发布年份 2023

来源: Frontiers

【 摘 要 】

PurposeAccurate prediction of urinary tract infection (UTI) following intracerebral hemorrhage (ICH) can significantly facilitate both timely medical interventions and therapeutic decisions in neurocritical care. Our study aimed to propose a machine learning method to predict an upcoming UTI by using multi-time-point statistics.MethodsA total of 110 patients were identified from a neuro-intensive care unit in this research. Laboratory test results at two time points were chosen: Lab 1 collected at the time of admission and Lab 2 collected at the time of 48 h after admission. Univariate analysis was performed to investigate if there were statistical differences between the UTI group and the non-UTI group. Machine learning models were built with various combinations of selected features and evaluated with accuracy (ACC), sensitivity, specificity, and area under the curve (AUC) values.ResultsCorticosteroid usage (p < 0.001) and daily urinary volume (p < 0.001) were statistically significant risk factors for UTI. Moreover, there were statistical differences in laboratory test results between the UTI group and the non-UTI group at the two time points, as suggested by the univariate analysis. Among the machine learning models, the one incorporating clinical information and the rate of change in laboratory parameters outperformed the others. This model achieved ACC = 0.773, sensitivity = 0.785, specificity = 0.762, and AUC = 0.868 during training and 0.682, 0.685, 0.673, and 0.751 in the model test, respectively.ConclusionThe combination of clinical information and multi-time-point laboratory data can effectively predict upcoming UTIs after ICH in neurocritical care.

【 授权许可】

Unknown   
Copyright © 2023 Zhao, Chen, Huang, Wang, Tie, Yang, Cui and Xu.