Development of an Artificial Intelligence Algorithm to Predict Hypotension Risk After Induction in Cesarean Sections With Spinal Anesthesia
Trial ID: NCT06158542
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-02-28
Completion date: 2023-12-30
Sponsor: Hacettepe University
Collaborators: Cedars-Sinai Medical Center
Conditions: Spinal Anesthesia Induced Hypotension; Caeserian Section
Interventions: Spinal Anesthesia; Blood Sampling
Locations: Hacettepe University Hospitals
Countries: Turkey (Türkiye)
Enrollment: 370
Age range: 18 Years - None
Sex: FEMALE

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.

Eligibility criteria:
Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Outcomes:
The Low Blood Pressure Measured by Non-Invasive Methods