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.
Adult patients aged 18 years or older
Patients scheduled for elective surgical procedures
Procedures performed under general anesthesia
Availability of continuous invasive arterial blood pressure monitoring during the intraoperative period
Emergency surgery
Diagnosis of sepsis, septic shock, or advanced cardiogenic shock
Cardiac rhythm disturbances that prevent reliable hemodynamic measurements (e.g., atrial fibrillation)
Severe left ventricular dysfunction or advanced heart failure (ejection fraction <30%)
The study population will be drawn from adult patients undergoing elective surgical procedures under general anesthesia at a tertiary care hospital. Participants will be selected from operating rooms where continuous invasive arterial blood pressure monitoring is routinely used as part of standard anesthetic care. The study will include patients managed according to usual clinical practice by attending anesthesiologists, without any protocol-driven intervention. Data will be collected prospectively during the intraoperative period from routine monitoring systems and anesthesia records. The population reflects a real-world perioperative setting in which intraoperative hypotension is commonly encountered and managed using standard hemodynamic monitoring and treatment strategies.
Cohort
Prospective
Not listed
Intraoperative hypotension endotype classification (hemodynamic subtype) per hypotension episode
For each intraoperative hypotension episode (defined as mean arterial pressure \[MAP\] \<65 mmHg lasting ≥1 minute, or a ≥30% decrease from baseline), the episode will be assigned to a hemodynamic endotype based on invasive arterial waveform-derived parameters recorded using MostCare® (Vygon, France), including MAP, SAP, DAP, HR, CO, CI, SV, SVI, SVR, SVRI, SVV, PPV, and SPV, sampled every 30 seconds. The outcome is the endotype label assigned to each episode, reported as a categorical classification with the following levels: Hypovolemic endotype, Vasodilatory endotype, Myocardial depression endotype and Bradycardic endotype.
Time to hemodynamic stabilization after treatment of intraoperative hypotension
For each intraoperative hypotension episode (mean arterial pressure \[MAP\] \<65 mmHg lasting ≥1 minute), the time interval from initiation of therapeutic intervention (fluid administration, vasopressor, or inotropic therapy) to achievement of MAP ≥65 mmHg will be recorded. Hemodynamic data will be obtained from invasive arterial blood pressure monitoring using MostCare® (Vygon, France), with parameters sampled every 30 seconds. The timestamp of treatment initiation will be matched with arterial pressure recordings to calculate recovery time.
Correlation between patient characteristics and intraoperative hypotension burden
Correlation between patient characteristics (age, sex, BMI, ASA class, comorbidities, chronic antihypertensive use) and intraoperative hypotension burden (number of hypotension episodes per patient, total duration of hypotension in minutes, and minimum MAP during surgery) Age (years), sex (male/female), BMI (kg/m²), ASA class (I-IV), comorbidities (presence/absence of hypertension, diabetes, CAD, etc.), chronic antihypertensive medication use (yes/no); collected from the anesthesia record and hospital electronic medical record. Number of intraoperative hypotension episodes per patient (count), total duration of hypotension per patient (minutes), minimum intraoperative MAP (mmHg); derived from continuous invasive arterial pressure monitoring using MostCare® (Vygon, France), sampled every 30 seconds.
Performance of AI-based personalized hypotension treatment recommendation model
Performance of machine learning models (Decision Tree, Random Forest, XGBoost) in predicting the most effective treatment strategy (fluid, vasopressor, inotrope, or combination) for intraoperative hypotension episodes, based on patient characteristics and hemodynamic endotype, compared to observed real-world treatment response (time to MAP ≥65 mmHg and hemodynamic stabilization). Patient characteristics: age (years), sex, BMI (kg/m²), ASA class, comorbidities, chronic antihypertensive use (yes/no), collected from anesthesia record/EMR. Hemodynamic endotype per hypotension episode, derived from invasive arterial waveform monitoring with MostCare® (Vygon, France), using MAP, SAP, DAP, HR, CO, CI, SV, SVI, SVR, SVRI, SVV, PPV, SPV sampled every 30 seconds.
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