Prediction of Hypotension Events with Physiologic Vital Sign Signatures in The Intensive Care Unit

Abstract Background. Even brief hypotension is associated with increased morbidity and mortality. We developed a machine learning model to predict the initial hypotension event among intensive care unit (ICU) patients, and designed an alert system for bedside implementation. Materials and Methods. From the Medical Information Mart for Intensive Care III (MIMIC-3) dataset minute-by-minute vital signs were extracted. A hypotension event was defined as at least 5 measurements within a 10-minute period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. A random forest (RF) classifier was used to predict hypotension, and performance was measured with area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Hypotension alerts were generated using risk score thresholds, then a stacked RF model and a lock-out time were applied for real-life implementation. Results. We identified 1307 subjects (1580 ICU stays) as the case (hypotension) group and 1619 subjects (2279 ICU stays) as the control group. The RF model showed AUROC of 0.93 and 0.88 at 15 and 60 minutes respectively before hypotension, and AUPRC of 0.77 at 60 minutes before. Risk score trajectories revealed 80% and > 60% of cases predicted at 15 and 60 minutes before the hypotension, respectively. The stacked model with 15-minute lock-out produced on average 0.79 alerts/subject/hour (sensitivity 92.4%). Conclusion. Clinically significant hypotension events in the ICU can be predicted at least 1 hour before the initial hypotension episode. Developing a high-sensitive and reliable practical alert system is feasible, with low rate of alerts.