Letao Zhu,1 Yanfeng Li,1 Qingshi Zhao,1 Changyu Li,1 Zongbi Wu,2 Youli Jiang1 

1Department of Neurology, People’s Hospital of Longhua, Shenzhen, 518109, People’s Republic of China; 2Nursing Department, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical School of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, 518033, People’s Republic of China

Correspondence: Youli Jiang, Department of Neurology, People’s Hospital of Longhua, No. 38, Jinglong Jianshe Road, Longhua District, Shenzhen, Tel +8618545403739, Fax +86 27741585-8500, Email h2362120381@163.com Zongbi Wu, Nursing Department, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical School of Guangzhou University of Chinese Medicine, No. 1, Fuhua Road, Futian District, Shenzhen, People’s Republic of China, Tel +8615384435576, Fax +86 0755-88359899, Email wuzongbi2016@126.com

Background: Acute ischemic stroke (AIS) is increasingly affecting younger populations, necessitating prompt thrombolytic therapy within a narrow therapeutic window. Pre-hospital delays are prevalent, particularly in China, yet targeted research on the youth population remains scarce.
Methods: In this retrospective cohort study, data from AIS patients aged 18– 50 admitted to Longhua District People’s Hospital, Shenzhen from December 2021 to December 2023 were analyzed using XGBoost and Random Forest machine learning algorithms, coupled with SHAP visualization, to identify factors contributing to pre-hospital delays.
Results: Among 1954 AIS patients, 528 young patients were analyzed. The median time to hospital arrival was 8.34 hours, with 82.0% experiencing delays. Analysis of different age subgroups showed that young patients aged 36– 50 years old had a higher delay rate than patients under 36 years old. Machine learning algorithms identified stroke awareness, age, TOAST classification, ambulance arrival, dysarthria, mRS on admission, dizziness, wake-up stroke, etc. as important determinants of delay.
Conclusion: This study highlights the necessity of machine learning in identifying delay risk factors in young stroke patients. Enhanced public education, particularly regarding stroke symptoms and the use of emergency services, is crucial for reducing pre-hospital delays and improving patient outcomes.

Keywords: acute ischemic stroke, onset-to-door time, young adults, machine learning, emergency medical services