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 five measurements within a 10-min period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. Using time series data from 30-min overlapping time windows, a random forest (RF) classifier was used to predict risk of hypotension every minute. Chronologically, the first half of extracted data was used to train the model, and the second half was used to validate the trained model. The model's 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 time series, a stacked RF model. A lockout time were applied for real-life implementation.

Results: We identified 1307 subjects (1580 ICU stays) as the hypotension group and 1619 subjects (2279 ICU stays) as the non-hypotension group. The RF model showed AUROC of 0.93 and 0.88 at 15 and 60 min, respectively, before hypotension, and AUPRC of 0.77 at 60 min before. Risk score trajectories revealed 80% and > 60% of hypotension predicted at 15 and 60 min before the hypotension, respectively. The stacked model with 15-min lockout 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 h before the initial hypotension episode. With a highly sensitive and reliable practical alert system, a vast majority of future hypotension could be captured, suggesting potential real-life utility.

Keywords: Artificial intelligence; Hypotension; Machine learning; Prediction.

Fig. 1

Schematic illustration to assess the performance of the hypotension prediction model on a finite time horizon (2 h). From the developmental cohort, the last 5 min before hypotension episode for the hypotension group was labeled and trained as positive, and 15-min long data before the last 2 h and 15 min were labeled and trained as negative

Fig. 2

Data extraction pipeline. From the initial MIMIC3 database, inclusion and exclusion criteria were applied, along with the definition of hypotension event. Then, feature selection was performed to derive the hypotension (subjects experienced hypotension events during the ICU stay) and the non-hypotension (those without the hypotension event) groups

Fig. 3

Performance evaluation for various supervised machine learning algorithms, with the evolution of area under the receiver operating characteristics (AUROC) over time (left), calibration plot with the Brier’s scores (center), and the evolution of the area under the precision–recall curve (AUPRC) over time (right)

Fig. 4

Average evolution of the risk scores for hypotension (red) and non-hypotension (blue) groups is projected as trajectories, extending to 4 h preceding hypotension events. Shaded areas represent 95% confidence intervals. Dotted lines indicate the number of hypotension and non-hypotension subjects used to derive the risk score trajectory points at a given horizon

Fig. 5

Relationship between the detected hypotension subjects (%) and the probability of hypotension after an alert between the stacked model (orange line) and single random forest model (blue line). Detected cases indicate the percentage of hypotension subjects our model successfully predicted as an alert before hypotension. At the risk score threshold of 0.5, the probability of hypotension in the future (positive predictive value) was approximately 0.65 (red dashed arrow), with 92.37% of hypotension events were captured (vertical blue dashed arrow)