Prediction of Perioperative Hypotension

The Hypotension Prediction Index (HPI) is an algorithm designed for the prediction of hypotension onset in critically ill or perioperative patients, allowing clinicians to take preventative measures. Hypotension, or low blood pressure, can lead to organ damage, increased morbidity and even mortality, making its timely prediction and management critical. The HPI uses machine learning algorithms to analyze physiological data such as heart rate, blood pressure, and stroke volume to provide clinicians with real-time prediction of hypotensive events before they occur (1). Accurate prediction of perioperative hypotension has significant clinical benefits.

One of the most impactful applications of HPI is in intraoperative care, where patients are particularly vulnerable to blood pressure fluctuations due to anesthesia and other stressors. Prolonged periods of low blood pressure during surgery have been associated with an increased risk of myocardial infarction, stroke, and acute kidney injury (2). For example, Maheshwari et al. demonstrated that hypotension during non-cardiac surgery is independently associated with an increased risk of myocardial injury, suggesting that tight control of blood pressure is essential to minimize these risks (3). HPI addresses this challenge by allowing anesthesiologists to anticipate and prevent hypotension before it reaches critical levels.

A landmark study by Hatib et al. validated the efficacy of the Hypotension Prediction Index in predicting intraoperative hypotension during surgery. The study demonstrated that the HPI can predict hypotension with a sensitivity and specificity greater than 85%, making it a reliable tool in clinical practice (1). In this study, high-fidelity analysis of arterial pressure waveforms was used to train the algorithm, improving the prediction of hypotensive events.

Despite its proven benefits, widespread adoption of HPI in clinical practice has not been without challenges. A key limitation of the system is its reliance on continuous, high-quality hemodynamic monitoring, which is typically only available in well-equipped operating rooms and intensive care units. As a result, not all perioperative settings can use this prediction index to control hypotension. In addition, while the predictive accuracy of HPI is impressive, there is always the potential for false positives. In this case, clinicians may intervene unnecessarily, exposing patients to treatments that may carry their own risks, such as over-administration of fluids or vasopressors (2). Some critics have argued that while HPI is a valuable tool, it should complement, rather than replace, clinical judgment.

In addition, research is ongoing to optimize the use of HPI in different clinical settings. Currently, it is most commonly used in high-risk perioperative and critically ill patients, where real-time blood pressure monitoring is essential and where hypotension prediction is feasible. However, future iterations of the HPI algorithm may incorporate additional physiological parameters or be used in a wider range of medical settings, such as emergency rooms or general wards, where hypotension also poses a significant risk (3).

In conclusion, the Hypotension Prediction Index represents a leap forward in the management of hypotension in perioperative and critical care settings. By using machine learning algorithms

to predict hypotension before it occurs, the HPI enables timely interventions that can prevent adverse outcomes. While challenges related to data quality and clinical interpretation remain, the growing body of evidence supporting the efficacy of HPI suggests that it will play an increasingly important role in improving patient outcomes and reducing the risks associated with hypotension.

References

1. Hatib F, Jian Z, Buddi S, et al. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018;129(4):663-674. doi:10.1097/ALN.0000000000002300

2. Davies SJ, Vistisen ST, Jian Z, Hatib F, Scheeren TWL. Ability of an Arterial Waveform Analysis-Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients. Anesth Analg. 2020;130(2):352-359. doi:10.1213/ANE.0000000000004121

3. Maheshwari K, Turan A, Mao G, et al. The association of hypotension during non-cardiac surgery, before and after skin incision, with postoperative acute kidney injury: a retrospective cohort analysis. Anaesthesia. 2018;73(10):1223-1228. doi:10.1111/anae.14416