Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland.
Department of Anesthesiology and Intensive Therapy, Semmelweis University, 1085 Budapest, Hungary.
Medicina (Kaunas). 2023 Mar 2;59(3):491. doi: 10.3390/medicina59030491.
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.
术中低血压（IH）是一种影响大量接受全身麻醉患者的常见现象。IH 的发生与显著的围手术期并发症有关，包括肾衰竭、心肌损伤，甚至死亡率增加。尽管有先进的血流动力学监测和采用目标导向治疗的方案，但我们的管理仍然是被动的；只有在低血压发作已经发生时才进行干预。这篇文献综述评估了低血压预测指数（HPI），该指数旨在预测和降低 IH 的发生率。HPI 算法基于机器学习算法，该算法分析动脉压力波形作为输入，以 MAP<65mmHg 至少 1 分钟作为输出来预测低血压的发生。有几项回顾性和前瞻性研究表明，使用 HPI 算法可以显著减少 IH 发作次数。然而，HPI 使用的证据水平仍然很低，需要进一步的研究来证明该算法对围手术期结局的益处。
