Dynamic Prediction of 3-Month Recurrence After First-Ever Ischemic Stroke Using a Two-Stream Attention-LSTM Model -...

We aimed to develop a dynamic model for predicting 3-month recurrence after first-ever ischemic stroke using multimodal longitudinal data.

Patients with first-ever ischemic stroke admitted to a tertiary hospital in Zhengzhou, China between January 2023 and January 2025 were enrolled. Data on static baseline characteristics, discharge variables, and 1-month follow-up variables were collected. Imaging phenotypes were derived from diffusion-weighted imaging (DWI) and Fluid-Attenuated Inversion Recovery (FLAIR) using automated lesion segmentation and unsupervised clustering. The dataset was divided into training and validation sets (8∶2) using a fixed random seed. Seven models were trained and evaluated: logistic regression, random forest (RF), support vector machine (SVM), XGBoost, multi-layer perceptron (MLP), standard Long Short-Term Memory (LSTM), and two-stream attention-LSTM. External validation was conducted at three independent hospitals. The predictive performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, and Brier score.

Among 625 patients, recurrence occurred in 79 (12.64%) patients 3 months after discharge. In the validation set, the AUCs were ranked as follows: two-stream attention-LSTM (0.857); SVM (0.838); XGBoost (0.808); MLP (0.768); RF (0.761); standard LSTM (0.754); logistic regression with follow-up features (0.748); and discharge-only logistic regression (0.686). Two-stream attention LSTM identified key predictors, including dynamic changes in C-reactive protein, systolic blood pressure, and imaging phenotypes. External validation showed stable discrimination (pooled AUC=0.83) and good calibration.

Two-stream attention-LSTM improved prediction of 3-month recurrence after first-ever ischemic stroke and may support early post-discharge risk stratification.