Development of a Predictive Model for Intraoperative Blood Pressure Variability: a Retrospective Cohort Study

1. Introduction Blood pressure variability (BPV) is a description of the degree of fluctuation of a patient's blood pressure， such as standard deviation、average real variability, and variability independent of the mean， The above terms all describe the situation of BPV to varying degrees。BPV is divided into different types, and the short-term BPV of the patient is related to other terms BPV。In addition to the numerical value of blood pressure itself, high blood pressure variability will bring a series of complications, such as Target organ damage、cardiovascular events, and even death. Relevant literature and guidelines also recommend that the increase or decrease of blood pressure should not exceed 20% of the basic blood pressure value, and reducing the risk of intraoperative high blood pressure variability (IHBPV) has become one of the important tasks of anesthesiologists。 The regular vital signs recorded during surgery include blood pressure, heart rate, and respiratory rate, which may cause BPV to be overlooked by anesthesiologists； The factors that cause IHBPV are complex, and the patient's own vascular status,sympathetic nervous excitability,drug use,fluid balance and surgical stimulation can cause IHBPV and increase its perioperative risk, affecting the patient's prognosis. Multidimensional, time-varying changes make it difficult for anesthesiologists to make judgments, and there is currently a lack of effective prediction and explanation of IHBPV.

Machine learning（ML）is an interdisciplinary field that studies how computers simulate human learning behavior and reorganize existing knowledge structures to improve their performance and acquire new knowledge and skills. Recent research has found that ML has advantages in predicting and explaining complex systems, ML is suitable for analyzing complex real-world data and processing time series data. The purpose of this study was to construct machine learning models to predict the occurrence of IHBPV during overall surgery in patients, and to clarify the baseline information and intraoperative factors that lead to IHBPV, in order to assist anesthesiologists in improving the management of BPV during surgery. 2. Materials and methods 2.1 Study Design This retrospective study collected data from the Beijing Tsinghua Chang Gung Hospital affiliated with Tsinghua University from March 2016 to April 2022, all patients who underwent surgery at the center operating room with the approval of the ethics committee were included in the study. The study mainly obtained patients' information from the HIS system and the Medtronic system and aimed to predict the possibility of IHBPV in patients based on baseline information and intraoperative factors through ML models and to explain the increase in the risk of IHBPV among patients through interpretable ML models.

The original database consisted of 52250 cases of general anesthesia surgery. The sample size was based on the scale of the existing database, of which 47520 surgeries met the criteria of this study. The inclusion criteria were patients who received general anesthesia, intravenous anesthesia, or intravenous-inhalation anesthesia, and ASA1-5 grade. And the exclusion criteria were surgeries with missing key information and surgeries that were not monitored for blood pressure throughout the operation. For surgeries with missing or ambiguous information, investigators used a deletion method to ensure the authenticity of the data. Important variables included time records, vital signs, and fluid balance. The Ethics Committee of Beijing Tsinghua Chang Gung Hospital has approved the study and waived the requirement for personal informed consent,as it is a retrospective study,this research has also been approved by the China Clinical Trial Registration Center.This study followed the relevant part of the "Transparent Reportingof a Multivariable Prediction Model for Individual Prognosis or Diagnosis" (TRIPOD) reporting guidelines for clinical prediction models, which includes commonly used reference items for building clinical prediction models.

2.2 Data collection This study mainly obtained the baseline information of patients from the HIS system, including height, weight, gender, disease diagnosis, and surgical method, and extracted intraoperative factors from the Medtronic surgical anesthesia system as independent variables, including intraoperative blood pressure, intraoperative drug data, fluid balance, and key surgical operations recorded during surgery. investigators selected some commonly used drugs, and the criteria for commonly used drugs were that the usage frequency of the drug in various surgeries exceeded one-tenth. The commonly used drugs are sevoflurane, propofol, dexmedetomidine, midazolam, remifentanil, sufentanil, methoxamine, and rocuronium; For preoperative diagnoses, considering their impact on blood pressure variability, investigators prioritized the extraction of binary variables related to blood pressure, such as cardiac dysfunction, and renal dysfunction.

2.3 Diagnostic criteria for IHBPV The primary endpoint of this study is blood pressure variability (BPV) which has been studied in several previous papers， BPV has different definitions and calculation methods， each with its own advantages. The coefficient of variation (CV) of intraoperative mean arterial pressure (MAP) was used as a quantitative measure of BPV, It takes into account the patient's blood pressure fluctuations while also considering their baseline blood pressure level. In this study, investigators calculated the coefficient of variation (CV) of the intraoperative MAP as a quantitative indicator of blood pressure variability (BPV), CV of MAP is calculated by dividing the standard deviation of MAP by the mean value of MAP。 According to previous literature and guideline recommendations, the increase and decrease in patient blood pressure during surgery should not exceed 20% of the baseline blood pressure value. Therefore, investigators define cases of IHBPV as situations where the CV of MAP exceeds 20% during the surgery, regardless of whether the increase in blood pressure variability is due to a decrease or an increase in blood pressure.