Machine Learning-Assisted Management of Intraoperative Hypotension for Personalized Treatment

Intraoperative hypotension, commonly defined as a decrease in arterial blood pressure during surgery, is a frequent and clinically important event in patients undergoing general anesthesia. Numerous studies have shown that even brief periods of low blood pressure may be associated with impaired organ perfusion and an increased risk of postoperative complications, including acute kidney injury, myocardial injury, stroke, prolonged hospital stay, and increased mortality. Despite growing awareness of its clinical impact, the optimal management of intraoperative hypotension remains an ongoing challenge in anesthetic practice. Current approaches to intraoperative blood pressure management are largely based on general thresholds, guideline recommendations, and the individual experience of anesthesiologists. In routine clinical practice, hypotension is often treated with fluid administration, vasopressor agents, inotropic drugs, or combinations of these interventions. However, hypotension is not a single, uniform clinical entity. It may arise from different underlying physiological mechanisms, such as hypovolemia, anesthetic-induced vasodilation, myocardial depression, or heart rate abnormalities. These mechanisms may coexist or change dynamically during surgery, making clinical decision-making complex. In many cases, similar treatment strategies are applied to patients with different physiological causes of hypotension, which may lead to variable treatment responses. For example, fluid administration may be effective in patients with hypovolemia but less beneficial or even harmful in patients with predominant vasodilation or impaired cardiac function. Likewise, vasopressor therapy may rapidly restore blood pressure in vasodilatory hypotension but may not adequately address hypotension caused by low cardiac output. This highlights the need for a more individualized, physiology-based approach to intraoperative hypotension management. Recent advances in perioperative monitoring have enabled continuous, high-resolution recording of arterial blood pressure and advanced hemodynamic parameters, such as cardiac output, stroke volume, and systemic vascular resistance. At the same time, developments in machine learning and artificial intelligence have created new opportunities to analyze large and complex datasets, identify hidden patterns, and generate data-driven insights that may not be apparent through traditional statistical methods. The primary aim of this prospective observational study is to improve the understanding of intraoperative hypotension by integrating detailed hemodynamic monitoring with machine learning-based analysis. Rather than focusing on a single blood pressure threshold or isolated variables, this study seeks to characterize hypotension episodes as dynamic physiological events and to identify distinct hypotension subtypes based on underlying hemodynamic patterns. This study is designed as a non-interventional, observational investigation and does not alter routine clinical care. All anesthesia management decisions, including the prevention and treatment of hypotension, will be made by the attending anesthesiologists according to standard clinical practice and institutional protocols. The research team will not provide real-time recommendations or influence clinical decision-making during surgery. The role of the research team is limited to systematic data collection, data processing, and post hoc analysis. Adult patients (≥18 years of age) undergoing elective surgical procedures under general anesthesia will be included. Continuous invasive arterial blood pressure monitoring is required for inclusion, as it allows precise, beat-to-beat assessment of blood pressure and waveform-derived hemodynamic variables. Emergency surgeries, patients with severe circulatory shock, advanced cardiac dysfunction, or conditions that prevent reliable hemodynamic measurements will be excluded to ensure data quality and patient safety. During the intraoperative period, arterial blood pressure, heart rate, cardiac output, cardiac index, stroke volume, stroke volume index, systemic vascular resistance, and related dynamic preload variables will be continuously recorded at predefined time intervals. Hypotension will be defined as a mean arterial pressure below a clinically relevant threshold sustained for a minimum duration. When hypotension occurs, the onset time, duration, severity, and frequency of each episode will be documented. In addition to physiological data, detailed information regarding therapeutic interventions will be collected. This includes the type and amount of intravenous fluids administered, the use of vasopressor agents or inotropic drugs, dosing strategies, and the timing of interventions relative to hypotension onset. The response to treatment will be assessed by measuring the time required for blood pressure to return to predefined target values and by evaluating changes in other hemodynamic parameters following intervention. Machine learning techniques will be applied to the collected dataset to identify patterns and clusters within hypotension episodes. Unsupervised learning methods, such as clustering algorithms, will be used to classify hypotension events into subtypes based on hemodynamic profiles. These subtypes are expected to reflect different dominant physiological mechanisms, such as volume depletion, vasodilation, myocardial depression, or heart rate-related hypotension. In parallel, supervised learning models will be used to explore relationships between patient characteristics, hemodynamic patterns, treatment strategies, and treatment responses. Variables such as age, sex, body mass index, physical status classification, comorbidities, and surgical characteristics will be analyzed to determine their association with hypotension occurrence, subtype distribution, and responsiveness to specific interventions. One of the key objectives of this study is to evaluate whether certain treatments are more effective for specific hypotension subtypes or patient profiles. By analyzing treatment response times and hemodynamic recovery patterns, the study aims to generate evidence supporting more targeted and individualized treatment strategies. These analyses will not be used to guide patient care during the study but will form the basis for future hypothesis generation and interventional research. All collected data will be anonymized and stored securely in accordance with data protection regulations. Only authorized members of the research team will have access to the dataset. The study will adhere to ethical principles for clinical research, and informed consent will be obtained from all participants prior to inclusion. The ultimate goal of this research is to contribute to the development of data-driven, personalized approaches to intraoperative blood pressure management. By combining high-quality physiological data with advanced analytical methods, this study aims to provide a deeper understanding of intraoperative hypotension and its heterogeneous nature. In the long term, the findings may support the development of clinical decision-support systems that assist anesthesiologists in selecting the most appropriate treatment strategy for each patient based on real-time physiological information. Such systems have the potential to improve patient safety, reduce the burden of postoperative complications, and enhance the quality of perioperative care without replacing clinical judgment. Instead, they are intended to complement the expertise of anesthesiologists by providing objective, individualized insights derived from complex data patterns. This study represents an important step toward more precise, personalized, and physiology-guided management of intraoperative hypotension.