A Dynamic Machine Learning Model to Predict Angiographic Vasospasm After Aneurysmal Subarachnoid Hemorrhage

The goal of this study was to develop a highly precise, dynamic machine learning model centered on daily transcranial Doppler ultrasound (TCD) data to predict angiographic vasospasm (AV) in the context of aneurysmal subarachnoid hemorrhage (aSAH).

A retrospective review of patients with aSAH treated at a single institution was performed. The primary outcome was AV, defined as angiographic narrowing of any intracranial artery at any time point during admission from risk assessment. Standard demographic, clinical, and radiographic data were collected. Quantitative data including mean arterial pressure, cerebral perfusion pressure, daily serum sodium, and hourly ventriculostomy output were collected. Detailed daily TCD data of intracranial arteries including maximum velocities, pulsatility indices, and Lindegaard ratios were collected. Three predictive machine learning models were created and compared: A static multivariate logistics regression model based on data collected on the date of admission (Baseline Model; BM), a standard TCD model using middle cerebral artery flow velocity and Lindegaard ratio measurements (SM), and a machine learning long short term memory (LSTM) model using all data trended through the hospitalization.

A total of 424 patients with aSAH were reviewed, 78 of whom developed AV. In predicting AV at any time point in the future, the LSTM model had the highest precision (0.571) and accuracy (0.776), whereas the SM model had the highest overall performance with an F1 score of 0.566. In predicting AV within 5 days, the LSTM continued to have the highest precision (0.488) and accuracy (0.803). After an ablation test removing all non-TCD elements, the LSTM model improved to a precision of 0.824.

Longitudinal TCD data can be used to create a dynamic machine learning model with higher precision than static TCD measurements for predicting AV after aSAH.