Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke

Periprocedure Management of Blood Pressure After Acute Ischemic Stroke

The management of acute ischemic stroke primarily revolves around the timely restoration of blood flow (recanalization/reperfusion) in the occluded vessel and maintenance of cerebral perfusion through collaterals before reperfusion. Mechanical thrombectomy is the most effective treatment for acute ischemic stroke due to large vessel occlusions in appropriately selected patients. Judicious management of blood pressure before, during, and after mechanical thrombectomy is critical to ensure good outcomes by preventing progression of cerebral ischemia as well hemorrhagic conversion, in addition to optimizing systemic perfusion. While direct evidence to support specific hemodynamic targets around mechanical thrombectomy is limited, there is increasing interest in this area. Newer approaches to blood pressure management utilizing individualized cerebral autoregulation-based targets are being explored. Early efforts at utilizing machine learning to predict blood pressure treatment thresholds and therapies also seem promising; this focused review aims to provide an update on recent evidence around periprocedural blood pressure management after acute ischemic stroke, highlighting its implications for clinical practice while identifying gaps in current literature.

Using machine learning to modify and enhance the daily living questionnaire

The Daily Living Questionnaire (DLQ) constitutes one of a number of functional cognitive measures, commonly employed in a range of medical and rehabilitation settings. One of the drawbacks of the DLQ is its length which poses an obstacle to conducting efficient and widespread screening of the public and which incurs inaccuracies due to the length and fatigue of the subjects.

Objective

This study aims to use Machine Learning (ML) to modify and abridge the DLQ without compromising its fidelity and accuracy.

Method

Participants were interviewed in two separate research studies conducted in the United States of America and Israel, and one unified file was created for ML analysis. An ML-based Computerized Adaptive Testing (ML-CAT) algorithm was applied to the DLQ database to create an adaptive testing instrument-with a shortened test form adapted to individual test scores.

Results

The ML-CAT approach was shown to reduce the number of tests required on average by 25% per individual when predicting each of the seven DLQ output scores independently and reduce by over 50% when predicting all seven scores concurrently using a single model. These results maintained an accuracy of 95% (5% error) across subject scores. The study pinpoints which DLQ items are more informative in predicting DLQ scores.

Conclusions

Applying the ML-CAT model can thus serve to modify, refine and even abridge the current DLQ, thereby enabling wider community screening while also enhancing clinical and research utility.