Prediction of Block Height of Spinal Anesthesia

Anesthesia; Reaction Clinical Trial

Official title:

Prediction of Block Height of Spinal Anesthesia Via Machine Learning Approach

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05024838
• Other study ID #: 2020-01-004CC
• Status: Recruiting
• Start date: October 1, 2020
• Est. completion date: July 2022

Study information

• Verified date: August 2021
• Source: Taipei Veterans General Hospital, Taiwan
• Contact: Hung-Wei Cheng, MD
• Phone: 886-2-28757549
• Email: hwc1127[@]gmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 3000
• Est. completion date: July 2022
• Est. primary completion date: July 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients receiving spinal anesthesia from July 1, 2018, to Dec 31, 2018, with available electronic medical records.

Exclusion Criteria:

• Age <18 years

Related Conditions & MeSH terms

• Anesthesia; Reaction

Intervention

Other:
• Machine learning methods
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.

Locations

Country	Name	City	State
Taiwan	Department of Anesthesiology, Taipei Veterans General Hospital	Taipei

Sponsors (1)

Lead Sponsor	Collaborator
Taipei Veterans General Hospital, Taiwan

Country where clinical trial is conducted

Taiwan, 

References & Publications (6)

Fanning N, Arzola C, Balki M, Carvalho JC. Lumbar dural sac dimensions determined by ultrasound helps predict sensory block extent during combined spinal-epidural analgesia for labor. Reg Anesth Pain Med. 2012 May-Jun;37(3):283-8. doi: 10.1097/AAP.0b013e31824b30d2. — View Citation

Greene NM. Distribution of local anesthetic solutions within the subarachnoid space. Anesth Analg. 1985 Jul;64(7):715-30. Review. — View Citation

Heng Sia AT, Tan KH, Sng BL, Lim Y, Chan ES, Siddiqui FJ. Hyperbaric versus plain bupivacaine for spinal anesthesia for cesarean delivery. Anesth Analg. 2015 Jan;120(1):132-40. doi: 10.1213/ANE.0000000000000443. Review. — View Citation

Horstman DJ, Riley ET, Carvalho B. A randomized trial of maximum cephalad sensory blockade with single-shot spinal compared with combined spinal-epidural techniques for cesarean delivery. Anesth Analg. 2009 Jan;108(1):240-5. doi: 10.1213/ane.0b013e31818e0fa6. — View Citation

Kozanhan B, Bardak O, Sami Tutar M, Ozler S, Yildiz M, Solak I. The influence of Body Roundness Index on sensorial block level of spinal anaesthesia for elective caesarean section: an observational study. J Obstet Gynaecol. 2020 Aug;40(6):772-778. doi: 10.1080/01443615.2019.1647523. Epub 2019 Aug 30. — View Citation

Kuok CH, Huang CH, Tsai PS, Ko YP, Lee WS, Hsu YW, Hung FY. Preoperative measurement of maternal abdominal circumference relates the initial sensory block level of spinal anesthesia for cesarean section: An observational study. Taiwan J Obstet Gynecol. 2016 Dec;55(6):810-814. doi: 10.1016/j.tjog.2015.04.009. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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