NCT05024838

Brief 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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
3,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2020

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

Study Start

First participant enrolled

October 1, 2020

Completed
11 months until next milestone

First Submitted

Initial submission to the registry

August 22, 2021

Completed
5 days until next milestone

First Posted

Study publicly available on registry

August 27, 2021

Completed
10 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 1, 2022

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

July 1, 2022

Completed
Last Updated

August 27, 2021

Status Verified

August 1, 2021

Enrollment Period

1.7 years

First QC Date

August 22, 2021

Last Update Submit

August 26, 2021

Conditions

Keywords

Spinal anesthesia, Machine learning, Sensory blockade

Outcome Measures

Primary Outcomes (1)

  • 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

Study Arms (1)

Spinal anesthesia

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

Interventions

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.

Spinal anesthesia

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients receiving spinal anesthesia due to the need for surgical intervention with available electronic medical records.

You may qualify if:

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

You may not qualify if:

  • Age \<18 years

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Department of Anesthesiology, Taipei Veterans General Hospital

Taipei, 112, Taiwan

RECRUITING

Related 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.

    PMID: 22476235BACKGROUND
  • Heng Sia AT, Tan KH, Sng BL, Lim Y, Chan ESY, Siddiqui FJ. Hyperbaric versus plain bupivacaine for spinal anesthesia for cesarean delivery. Anesth Analg. 2015 Jan;120(1):132-140. doi: 10.1213/ANE.0000000000000443.

    PMID: 25625258BACKGROUND
  • Greene NM. Distribution of local anesthetic solutions within the subarachnoid space. Anesth Analg. 1985 Jul;64(7):715-30. No abstract available.

    PMID: 3893222BACKGROUND
  • 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.

    PMID: 19095857BACKGROUND
  • 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.

    PMID: 31469024BACKGROUND
  • 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.

    PMID: 28040125BACKGROUND

Study Officials

  • Hung-Wei Cheng, MD

    Taipei Veteran General Hospital, Taiwan

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Hung-Wei Cheng, MD

CONTACT

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER GOV
Responsible Party
SPONSOR

Study Record Dates

First Submitted

August 22, 2021

First Posted

August 27, 2021

Study Start

October 1, 2020

Primary Completion

July 1, 2022

Study Completion

July 1, 2022

Last Updated

August 27, 2021

Record last verified: 2021-08

Locations