Artificial intelligence assessment of heart failure and mortality after acute stroke

Abstract Background/Introduction The adverse cardiovascular outcomes following a stroke have shown significant associations with post-stroke mortality. However, studies focusing on predicting heart failure and its impact on long-term mortality are scarce. Purpose To address this gap, we employed an commercially approved artificial intelligence/machine learning enabled software as a medical device (AiTiALVSD version 1.00.00) which uses only 12 lead electrocardiogram data, resulting probability score for predicting left ventricular systolic dysfunction. We derived the AiTiALVSD probability score from initial ECGs at stroke admission, and assess its implications for prediction of heart failure and long-term mortality. Methods We collected data from 335 ischemic stroke patients admitted to a cardiovascular center during acute periods between 2013 and 2019. The AiTiALVSD score was employed to predict heart failure with reduced ejection fraction and heart failure with mild reduced ejection fraction, analyzed through logistic regression. Subsequently, we used the Cox Hazard proportional model, incorporating the AiTiALVSD score and clinical factors, to predict long-term mortality. Results The model incorporating the AiTiALVSD score with clinical variables demonstrated a higher AUC for predicting heart failure (0.905 [95% CI, 0.827-0.927] vs. 0.828 [95% CI, 0.749-0.907], p<0.004) than clinical variables alone. Moreover, for long-term mortality prediction, this model outperformed the conventional mortality score (AUC, 0.884 [95% CI, 0.836-0.933] vs. 0.755 [95% CI, 0.690-0.820], p<0.001). Conclusion Multivariable models which combine the AiTiALVSD score with clinical factors, effectively predicts heart failure and long-term mortality. This model could enhance therapeutic strategy tailoring by providing acute phase prognostic predictions for individual stroke patients.Prediction performance for heart failurePrediction performance for mortality