Prediction of Block Height of Spinal Anesthesia

Study Overview
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Brief Summary
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Spinal anesthesia is one of the most used techniques for surgery. Anesthesiologists usually check the block height (dermatome) of spinal anesthesia before surgery start. More than 20 factors have been postulated to alter spinal anesthetic block height. We would like to use machine learning to comprehensively consider various factors such as physiological parameters and different drug characteristics to establish a predictive model to evaluate the sensory blockade of spinal anesthesia.

Detailed Description
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This is an observational study of the retrospective collection of patient data. The investigators retrospectively collected the electronic medical record of patients receiving spinal anesthesia from July 1, 2018, to Dec 31, 2018. Anesthesia-related factors such as anesthesiologist's expertise, injection site, patient position, the dosage of local anesthetics, needle size, the direction of needle bevel, and basic demographic information of the patients were used for data analysis. Patients less than 18 years old were excluded from this study. Twenty percent of the dataset was used as a testing dataset, and the remaining were used for model training. The investigators will utilize four machine learning algorithms as XGBoost (Extreme Gradient Boosting), AdaBoost (Adaptive Boosting), Random Forest (RF), and support vector machine (SVM). Model performances were evaluated visually with a confusion matrix.

Official Title
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Prediction of Block Height of Spinal Anesthesia Via Machine Learning Approach

Conditions
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Anesthesia; Reaction

Intervention / Treatment
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* Other: Machine learning methods


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients receiving spinal anesthesia from July 1, 2018, to Dec 31, 2018, with available electronic medical records. Exclusion Criteria: Age <18 years

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Spinal anesthesia<br>The investigators retrospectively collected the electronic medical record of patients receiving spinal anesthesia from July 1, 2018, to Dec 31, 2018. Patients less than 18 years old were excluded from this study. | Other: Machine learning methods<br>* This is an observational study of the retrospective collection of patient data. Anesthesia-related factors such as anesthesiologist's expertise, injection site, patient position, the dosage of local anesthetics, needle size, the direction of needle bevel, and basic demographic information of the patients were used for data analysis. Patients less than 18 years old were excluded from this study. Twenty percent of the dataset was used as a testing dataset, and the remaining were used for model training. The investigators will utilize four machine learning algorithms as XGBoost (Extreme Gradient Boosting), AdaBoost (Adaptive Boosting), Random Forest (RF), and support vector machine (SVM). Model performances were evaluated visually with a confusion matrix.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensory blockade height of spinal anesthesia | The record of sensory blockade level was extracted from retrospective electronic medical records as the primary outcome. The investigators would like to use machine learning methods to consider various factors such as physiological parameters of patients, different drug characteristics, and different anesthesia providers to establish a predictive model to evaluate the sensory blockade of spinal anesthesia. | From time of starting spinal anesthesia until the time of testing blockage height, assessed up to 10 minutes | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Spinal anesthesia, Machine learning, Sensory blockade