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Building a machine learning-based evaluation model of pain intensity and treatment prognosis by analyzing epidurogram contrast patterns in patients with lumbar spinal pain.

Building a machine learning-based evaluation model of pain intensity and treatment prognosis by analyzing epidurogram contrast patterns in patients with lumbar spinal pain.

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0007931
Enrollment
1000
Registered
2022-11-24
Start date
2022-11-21
Completion date
Unknown
Last updated
2023-01-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

The Catholic University of Korea, Eunpyeong St. Mary's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients who performed transforaminal epidural steroid injection(code RSHA10202G, ELA354T) from 1st, April, 2019 until 31st, August, 2022.

Exclusion criteria

Exclusion criteria: In case of insufficient information such as VAS scores in medical records. A person who cannot take painkillers due to gastrointestinal disorders or medical underlying diseases. In the case where the instrument is identified on the previous history of surgery or PACS image by inserting the instrument in the lumbar spine. Patients whose image quality is so poor that it is difficult to check the needle.

Design outcomes

Primary

MeasureTime frame
The degree of spread of contrast medium—

Secondary

MeasureTime frame
pain score(Visual analogue scale);The dose of analgesics—

Countries

Korea, Republic of

Contacts

Public ContactEunhye Park

The Catholic University of Korea, Eunpyeong St. Mary's Hospital

hhumil@gmail.com+82-2-2030-4370

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Feb 4, 2026