Machine Learning-Assisted Management of Intraoperative Hypotension for Personalized Treatment

Plain-language summary

Intraoperative hypotension, defined as a drop in blood pressure during surgery, is a frequent event in patients undergoing general anesthesia. Even brief episodes of low blood pressure may reduce blood flow to vital organs such as the brain, heart, and kidneys, and have been associated with an increased risk of postoperative complications, prolonged recovery, and worse clinical outcomes. Despite its clinical importance, the management of intraoperative hypotension is often based on general guidelines and individual clinician experience rather than patient-specific physiological mechanisms. Low blood pressure during surgery can occur for different underlying reasons, including reduced circulating blood volume, excessive vasodilation caused by anesthetic agents, impaired heart contractility, or abnormalities in heart rate. In routine practice, these mechanisms are not always clearly distinguished, and similar treatment strategies may be applied to patients with different physiological causes of hypotension. As a result, the response to treatment can vary widely between patients. This prospective observational study aims to improve the understanding of intraoperative hypotension by collecting detailed hemodynamic data during surgery and analyzing these data using machine learning methods. The study is designed to observe current clinical practice without altering or interfering with routine patient care. All decisions regarding anesthesia management and treatment of hypotension will be made by the attending anesthesiologists according to standard clinical practice. The research team will not provide treatment recommendations during surgery. Adult patients undergoing elective surgery under general anesthesia with continuous invasive arterial blood pressure monitoring will be included. During the intraoperative period, blood pressure, heart rate, cardiac output, stroke volume, systemic vascular resistance, and other advanced hemodynamic parameters will be continuously recorded at regular intervals. When hypotension occurs, the onset, duration, and severity of the episode will be documented, along with the treatment applied, such as fluid administration, vasopressor agents, or inotropic medications. The time required for blood pressure to recover to an acceptable level will also be recorded. The collected data will be analyzed using machine learning techniques to identify distinct subtypes of intraoperative hypotension based on physiological patterns. These subtypes may reflect different underlying mechanisms, such as hypovolemia, vasodilation, myocardial depression, or heart rate-related causes. In addition, the study will evaluate how different treatment strategies perform across these hypotension subtypes and how quickly hemodynamic stability is restored. Patient-related factors such as age, sex, body mass index, physical status classification, and comorbid conditions will also be examined to determine their relationship with the occurrence, severity, and treatment response of hypotension episodes. By combining patient characteristics, physiological data, and treatment responses, the study aims to generate data-driven insights into personalized hypotension management. The ultimate goal of this research is to support the development of individualized treatment recommendations for intraoperative hypotension based on objective physiological data rather than a one-size-fits-all approach. The findings of this study are expected to provide a strong scientific foundation for future clinical decision-support systems that can assist anesthesiologists in selecting the most appropriate treatment strategy for each patient. By improving the precision of blood pressure management during surgery, this approach has the potential to enhance patient safety and perioperative outcomes while maintaining standard clinical workflows.

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