Predicting hypotension in the ICU using noninvasive physiological signals

Hypotension frequently occurs in Intensive Care Units (ICU), and its early prediction can improve the outcome of patient care. Trends observed in signals related to blood pressure (BP) are critical in predicting future events. Unfortunately, the invasive measurement of BP signals is neither comfortable nor feasible in all bed settings. In this study, we investigate the performance of machine-learning techniques in predicting hypotensive events in ICU settings using physiological signals that can be obtained noninvasively. We show that noninvasive mean arterial pressure (NIMAP) can be simulated by down-sampling the invasively measured MAP. This enables us to investigate the effect of BP measurement frequency on the algorithm's performance by training and testing the algorithm on a large dataset provided by the MIMIC III database. This study shows that having NIMAP information is essential for adequate predictive performance. The proposed predictive algorithm can flag hypotension with a sensitivity of 84%, positive predictive value (PPV) of 73%, and F1-score of 78%. Furthermore, the predictive performance of the algorithm improves by increasing the frequency of BP sampling.