Prognostic prediction of thrombolytic therapy with rt-PA in acute ischemic stroke patients using an artificial intelligence model

Objective: Through the comparison of different prediction models, we hope to find a promising statistical method to evaluate the prognosis of patients with acute ischemic stroke (AIS) after thrombolytic therapy. Methods: Data of 518 patients who received thrombolytic therapy were retrospectively collected in this study. Among them, 362 patients met the eligibility criteria, so their data such as age, sex, smoking history, previous medical history, clinical and laboratory indicators were analyzed. According to the 3 month follow-up results, 266 patients were included in a good prognosis group (modified Rankin Scale (mRS) score =2) and 96 in a poor prognosis group (3=mRS=6). All variables with P<0.05 in univariant and multivariant analyses were assigned in logistic regression model and artificial neural network (ANN) model to predict neurological prognosis, and the performance of the two models were compared. Results: Age, NIHSS scores, the serum concentration of immediate glucose, APTT and MBP at admission were found to be the predictive factors through ANN and logistic regression analysis. The binary logistic regression model revealed that the percentage correction, the precision, recall and F1 score of the regression model were 79%, 69.23%, 37.50% and 48.65%, respectively. While those of ANN were 79.98%, 69.70%, 37.25%, and 49.66%, correspondingly. Conclusions: ANN model is as effective as a logistic regression model in predicting the prognosis of AIS after thrombolytic therapy with rt-PA. Moreover, ANN is slightly superior to logistic regression in accuracy, precision and F1 score.