Forecasting Hypotension during Vasopressor Infusion via Time Series Analysis.

ABSTRACT

For optimal management of hypotension during continuous vasopressor infusion, this study investigated two forecasting models, logistic regression (LR) and auto-regressive (AR) models, to predict sustained hypotension episodes (SHEs) in the ICU, before the SHE occurred. Two investigational models were compared to a simple threshold detector, which alerts whenever the BP is less than the specific hypotension threshold. Datasets were collected from 207 patients treated for a variety of clinical indications in two different hospitals (Hospital 1 & 2). For the 60 mmHg hypotension threshold, LR model predicted SHEs an average of 7.0 min before (Hospital 1) and 2.5 min before (Hospital 2), and the AR model predicted SHEs 10.5 min and 2.0 min before (Hospital 1 and 2 respectively). Both were significantly better than the threshold method and without higher false alarm rates. The AR model offered the flexibility to predict for different hypotension thresholds.