Clinical Report: External Assessment of Machine Learning Algorithms for Anticipating Prehospital Delays in Acute Ischemic Stroke

Overview

This study externally validated eight prediction algorithms for prehospital delays in acute ischemic stroke (AIS) and compared their performance against logistic regression models.

Background

Acute ischemic stroke (AIS) is a leading cause of mortality and disability, with timely reperfusion therapy being critical for patient outcomes. Prehospital delays significantly hinder the administration of such therapies, making it essential to identify predictive factors.

Data Highlights

Key Findings

• Prehospital delay affected 80.9% of patients in Hospital 1 and 83.2% in Hospital 2.
• The logistic regression model without SMOTE achieved an AUC of 0.774, compared to 0.756 with SMOTE.
• XGBoost produced the highest validation AUC of 0.771, but the difference from logistic regression was not statistically significant (p = 0.107).
• Absolute risk differences for factors such as rural residence and non-emergency-channel presentation were significant.
• Complex algorithms did not consistently show improvement over simpler logistic regression models.

Clinical Implications

The findings suggest that while machine learning models can be explored for predicting prehospital delays, logistic regression remains a robust option. Clinicians should focus on modifiable factors contributing to delays to improve stroke care delivery.

Conclusion

The study highlights the need for ongoing evaluation of predictive models in stroke care.

Related Resources & Content

1. JMIR Medical Informatics, 2026 -- Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China
2. Frontiers in Cardiovascular Medicine, 2026 -- Artificial intelligence applied to post-resuscitation ECGs for early prognostication after out-of-hospital cardiac arrest
3. Frontiers in Medicine, 2026 -- Development and assessment of interpretable machine learning models for forecasting in-hospital mortality in patients undergoing surgery for acute type A aortic dissection
4. Brain, 2026 -- Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning
5. 2026 Guideline for the Early Management of Patients With Acute Ischemic Stroke: A Guideline From the American Heart Association/American Stroke Association | Stroke
6. Prehospital/EMS | American Stroke Association
7. Prospective, Multicenter, Controlled Trial of Mobile Stroke Units | New England Journal of Medicine
8. Effect of Direct Transportation to Thrombectomy-Capable Center vs Local Stroke Center on Neurological Outcomes in Patients With Suspected Large-Vessel Occlusion Stroke in Nonurban Areas: The RACECAT Randomized Clinical Trial | Trials | JAMA | JAMA Network
9. Creating Virtual Stroke Networks: Current and Future Role of Artificial Intelligence, Mobile Imaging Applications, and Telehealth in Triage and Treatment of Acute Ischemic Stroke: A Scientific Statement From the American Heart Association
10. Frontiers | Development and validation of a risk prediction model of prehospital delay in patients with acute ischemic stroke
11. 2026 Guideline for the Early Management of Patients With Acute Ischemic Stroke: A Guideline From the American Heart Association/American Stroke Association | Stroke
12. Prehospital/EMS | American Stroke Association
13. Prospective, Multicenter, Controlled Trial of Mobile Stroke Units | New England Journal of Medicine
14. Effect of Direct Transportation to Thrombectomy-Capable Center vs Local Stroke Center on Neurological Outcomes in Patients With Suspected Large-Vessel Occlusion Stroke in Nonurban Areas: The RACECAT Randomized Clinical Trial | Trials | JAMA | JAMA Network
15. Creating Virtual Stroke Networks: Current and Future Role of Artificial Intelligence, Mobile Imaging Applications, and Telehealth in Triage and Treatment of Acute Ischemic Stroke: A Scientific Statement From the American Heart Association
16. Frontiers | Development and validation of a risk prediction model of prehospital delay in patients with acute ischemic stroke