Con: Routine Use of the Hypotension Prediction Index in Cardiac, Thoracic, and Vascular Surgery

Intraoperative Hypotension Prediction—A Proactive Perioperative Hemodynamic Management—A Literature Review

Intraoperative hypotension (IH) is a frequent phenomenon affecting a substantial number of patients undergoing general anesthesia. The occurrence of IH is related to significant perioperative complications, including kidney failure, myocardial injury, and even increased mortality. Despite advanced hemodynamic monitoring and protocols utilizing goal directed therapy, our management is still reactive; we intervene when the episode of hypotension has already occurred. This literature review evaluated the Hypotension Prediction Index (HPI), which is designed to predict and reduce the incidence of IH. The HPI algorithm is based on a machine learning algorithm that analyzes the arterial pressure waveform as an input and the occurrence of hypotension with MAP <65 mmHg for at least 1 min as an output. There are several studies, both retrospective and prospective, showing a significant reduction in IH episodes with the use of the HPI algorithm. However, the level of evidence on the use of HPI remains very low, and further studies are needed to show the benefits of this algorithm on perioperative outcomes.

Intraoperative Hypotension-Physiologic Basis and Future Directions

Intraoperative hypotension (IOH) is a major concern to the anesthesiologist. Its appropriate identification and management require an understanding of the physiology of blood pressure regulation, prudent blood pressure monitoring, and treatment. Even short durations of low mean arterial pressure have been associated with adverse postoperative clinical outcomes. The challenge is for the clinician to respond proactively, address the specific etiology of IOH, and keep in mind any changes to the patient's physiology. Predictive technology, such as the Hypotension Prediction Index, offers the clinician new insight into IOH. It has been shown to predict hypotension up to 15 minutes before occurrence. It also calculates stroke volume variation, dynamic arterial elastance, and left ventricular contractility, which can inform the anesthesiologist of the etiology of IOH to direct management. This new technology has the potential to reduce duration or even prevent IOH. In the authors' opinion, it is an example of how human-machine interaction will contribute to future advances in medicine. Additional studies should evaluate the effects of its use on postoperative outcomes.