ABSTRACT
The Hypotension Prediction Index (HPI) is a validated algorithm developed by applying machine learning for predicting intraoperative arterial hypotension (IOH). We evaluated whether the HPI, combined with a personalized treatment protocol, helps to reduce IOH (depth and duration) and perioperative events in real practice. This was a single-center retrospective study including 104 consecutive adults undergoing urgent or elective non-cardiac surgery with moderate-to-high risk of bleeding, requiring invasive blood pressure and continuous cardiac output monitoring. Depending on the sensor, two comparable groups were identified: patients managed following the institutional protocol of personalized goal-directed fluid therapy (GDFT, n = 52), or this GDFT supported by the HPI (HPI, n = 52). The time-weighted average of hypotension for a mean arterial pressure < 65 mmHg (TWAMAP<65), postoperative complications and length of hospital stay (LOS) were automatically downloaded from medical records and revised by clinicians blinded to the management received by patients. Differences in preoperative variables (i.e. physical status -ASA class-, acute kidney Injury-AKI- risk) and outcomes were analyzed using non-parametric tests with Hodges-Lehmann estimator for the median of differences. ASA class and AKI risk were similar (p = 0.749 and p = 0.837, respectively). Blood loss was also comparable (p = 0.279). HPI patients had a lower TWAMAP<65 [0.09 mmHg (0-0.48 mmHg)] vs [0.23 mmHg (0.01 to 0.97 mmHg)], p = 0.037. Postoperative complications were less prevalent in the HPI patients (0.46 ± 0.98 vs. 0.88 ± 1.20), p = 0.035. Finally, LOS was significantly shorter among HPI patients with a median difference of 2 days (p = 0.019). The HPI combined with a GDFT protocol may help to minimize the severity of IOH during non-cardiac surgery.
