Abstract
Background and PurposeElevated blood pressure (BP) in acute ischemic stroke is common. A raised BP is related to mortality and disability, yet excessive BP lowering can be detrimental. The optimal BP management in acute ischemic stroke remains insufficient and relies on expert consensus statements. Permissive hypertension is recommended during the first 24-h after stroke onset, yet there is ongoing uncertainty regarding the most appropriate blood BP management in the acute phase of ischemic stroke. This study aims to develop a decision support tool for improving the management of extremely high BP during the first 24 h after acute ischemic stroke by using machine learning (ML) tools.MethodsThis diagnostic accuracy study used retrospective data from MIMIC-III and eICU databases. Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10–30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. Regression trees were used to predict the time-weighted average BP. Implementation of synthetic minority oversampling technique was used to balance the dataset according to different antihypertensive treatments. The model performance of the decision tree was compared to the performance of neural networks, random forest, and logistic regression models.ResultsIn total, 7,265 acute ischemic stroke patients were identified. Diastolic BP (DBP) is the main variable for predicting BP reduction in the first 24 h after a stroke. For patients receiving thrombolysis with DBP <120 mmHg, Labetalol and Amlodipine are effective treatments. Above DBP of 120 mmHg, Amlodipine, Lisinopril, and Nicardipine are the most effective treatments. However, successful treatment depends on avoiding hyponatremia and on kidney functions.ConclusionThis is the first study to address BP management in the acute phase of ischemic stroke using ML techniques. The results indicate that the treatment choice should be adjusted to different clinical and BP parameters, thus, providing a better decision-making approach.
Highlights
Machine learning (ML) applications in healthcare have significant potential for improving clinical decision-making diagnoses, treatment effectiveness, and healthcare management, including lowering the costs for both healthcare providers and patients [1]
In comparison to randomized clinical trials that were tested in conventional methods, one or two medications in each clinical trial and compare the results to a placebo group, our study simultaneously examined over 100 antihypertensive medications using ML techniques
In accordance with the AHA/ASA recommendations to use CCBs and the Intravenous Nimodipine West European Stroke Trial (INWEST) trial that showed a significant decrease in systolic blood pressure (SBP) and diastolic BP (DBP) with nimodipine [18], we found that nircadipine and amlodipine are efficient in lowering blood pressure (BP) to the target interval under certain conditions
Summary
Machine learning (ML) applications in healthcare have significant potential for improving clinical decision-making diagnoses, treatment effectiveness, and healthcare management, including lowering the costs for both healthcare providers and patients [1]. We applied the KDD process by using ML techniques to conduct a robust interrogation to identify predictors of blood pressure (BP) management after acute ischemic stroke, having the potential to aid clinicians in improving treatment regimens. Acute and aggressive BP lowering within 24 h of stroke onset could jeopardize the outcome [5] Both elevated and low BP are independent factors that predict poor outcomes among patients with acute ischemic stroke and present a U-shaped relationship between BP and death or disability [6, 7]. This study aims to develop a decision support tool for improving the management of extremely high BP during the first 24 h after acute ischemic stroke by using machine learning (ML) tools
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
