Development of an Artificial Intelligence Algorithm to Predict Hypotension Risk After Induction in Cesarean Sections With Spinal Anesthesia
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 370
- 试验地点
- 1
- 主要终点
- The Low Blood Pressure Measured by Non-Invasive Methods
研究概览
简要总结
The cesarean section, medically necessary for both the mother and the baby in certain cases, is a life-saving operation.The most commonly used anesthesia method worldwide is spinal anesthesia. While spinal anesthesia has many advantages, it also has disadvantages. One of the most commonly encountered disadvantages is the development of hypotension due to the unopposed parasympathetic response after induction. Determining which patient will develop hypotension and which patient will not remains an important question for anesthesiologists before surgery. Identifying high-risk patients for hypotension before starting spinal anesthesia and even knowing the percentage of patients who will develop hypotension undoubtedly saves time in problem-solving. From this perspective, the idea for this study emerged: identifying parameters with the potential for use in prediction based on the literature, collecting data, then testing the relationship between them using machine learning methods, and developing an algorithm capable of predictive analysis.
At the end of the study, an artificial intelligence algorithm for predicting hypotension after induction will be developed, and its performance will be tested.
The main goals of the study:
i)Create a dataset including the clinical characteristics, demographic data, and blood test results of patients who develop and do not develop hypotension after spinal anesthesia.
ii) Develop an artificial intelligence algorithm using the dataset and determine the most accurate algorithm for predicting hypotension.
iii) To test the accuracy of the developed algorithm, create a test dataset, measure and optimize the algorithm's performance. Accuracy, sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves will be used for performance measurement.
iv) Create a suitable interface (a surface for interaction with the software) to make the developed algorithm usable in clinical practice.
详细描述
Rationale: Cesarean section, when indicated correctly, is a childbirth method that preserves the health of both the mother and the baby. The rates of births by cesarean section have been increasing worldwide for years. Between 2010 and 2018, 21.1% of globally tracked births were performed by cesarean section. According to the World Health Organization (WHO), it is expected that this rate will increase, reaching 29% by the year 2030.
The anesthesia for cesarean section is fundamentally influenced by both the physiological and pathological changes induced by pregnancy in the mother's body. Changes occurring during pregnancy and childbirth, and the resulting differences, can present challenges for anesthesiologists.
Spinal anesthesia induces iatrogenic sympathetic blockade, reducing systemic vascular resistance along with arterial and venous vasodilation, leading to hypotension. The incidence of hypotension after spinal anesthesia in pregnant women ranges from 7.4% to 74%. The frequency of hypotension is higher in pregnant women due to factors such as supine hypotension syndrome caused by fetal inferior vena cava compression and the development of collateral venous plexus in the epidural area, leading to the ascent of intrathecal local anesthetic in the cerebrospinal fluid. The deepening of hypotension and bradycardia in the patient can result in cardiac collapse, fetal hypoxia, and acidosis, posing an unpredictable risk to both maternal and fetal health. The role of anesthesiologists is to prevent or manage this risky condition effectively.
Recent advances in deep learning and artificial intelligence (AI) have found their place in the field of anesthesia. AI applications in anesthesia can be categorized into five main areas: 1) Monitoring the depth of anesthesia (e.g., techniques analyzing EEG data during anesthesia), 2) Control of anesthetic drug delivery based on depth of anesthesia, 3) Event prediction, 4) Ultrasound guidance, and 5) Pain management.
Among these applications, event prediction is particularly critical for anesthesiologists. Knowing about an event before it occurs contributes to its prevention or enables more accurate management. There have been 53 studies in the literature using AI for event prediction, including studies developing algorithms to predict hypotension during surgery and validating these algorithms. What sets this project apart from existing studies are:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Being 18 years or older
- •Having an American Society of Anesthesiologists (ASA) physical status of I, II, or III
- •Gestational age of 37 weeks or more
- •Having undergone spinal or combined spinal-epidural anesthesia
排除标准
- •Patient's unwillingness to participate in the study
- •Multiple pregnancies
- •Emergency cesarean section
- •Preeclampsia
- •Preoperatively measured systolic blood pressure equal to or greater than 140mmHg (hypertensive pregnant woman)
- •Having a contraindication to spinal anesthesia or experiencing spinal anesthesia failure
结局指标
主要结局
The Low Blood Pressure Measured by Non-Invasive Methods
时间窗: The first 15 minutes after the administration of spinal anesthesia
Mean arterial pressure falling below 65 mmHg • Systolic blood pressure dropping below 80 mmHg • Systolic blood pressure falling below 75% of baseline • Onset of hypotension symptoms such as dizziness, increased salivation, shortness of breath, nausea, and vomiting.
次要结局
未报告次要终点