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.
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.
Study Type
OBSERVATIONAL
Enrollment
3,000
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.
Department of Anesthesiology, Taipei Veterans General Hospital
Taipei, Taiwan
RECRUITINGSensory 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.
Time frame: From time of starting spinal anesthesia until the time of testing blockage height, assessed up to 10 minutes
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