At a glance
ClinicalIndex Comparison RecordStandardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
Development of an Artificial Intelligence Algorithm to Predict Hypotension Risk After Induction in Cesarean Sections With Spinal Anesthesia
In Brief
An observational study evaluating Spinal Anesthesia and Blood Sampling for Spinal Anesthesia Induced Hypotension and Caeserian Section. Completed, enrolled 370 participants across 1 site.
Detailed Summary
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.
Study Details
Timeline
Interventions
After standard monitoring, the patient is placed in a sitting position for site marking and administration of anesthesia. The line connecting the upper border of the right and left iliac crests through conventional palpation of anatomical landmarks (Tuffier line) is identified as the entry point at the L3-L4 interspinous space or L2-L3 interspace. For patients in whom spinal entry is successful, spinal anesthesia is provided with the appropriate dose and types of local anesthetics (intrathecal 10-12 mg bupivacaine with the addition of 15-25 mcg fentanyl). After completing the procedure and placing the patient in the supine position, the block level is determined 10 minutes post-procedure using the ice test/pinprick test. Blocks reaching the T4-T6 level are considered successful.
Preoperatively, syndecan-1 serum levels will be investigated from the blood sample taken before cesarean section.