Recurrent Lumbar Disc HerniationNCT06833099
The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach
An observational study of Recurrent Lumbar Disc Herniation, run by Jinyu Chen at 1 site in China.
Trial summary
What Was the Study About? This study focused on improving the care of patients with a specific type of back problem called lumbar disc herniation at the L5-S1 level. Doctors often treat this condition with a minimally invasive surgery known as percutaneous endoscopic interlaminar discectomy (PEID). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Recurrent Lumbar Disc Herniation
- What is being tested?
- VAS Point and Imaging Examination
- How many people
- 309 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 1 Nov 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Study Population Description: The study population consisted of 309 patients who underwent percutaneous endoscopic interlaminar discectomy (PEID) for L5-S1 lumbar disc herniation between January 2020 and June 2024 at Nantong First People's Hospital. All patients had at least 6 months of follow-up post-surgery. The study focused on identifying factors that predict recurrent lumbar disc herniation (rLDH) after the procedure.
Who may be able to join
- Inclusion Criteria for rLDH: (A) Patients with L5-S1 lumbar disc herniation who underwent single level PEID. (B) Completed comprehensive imaging examinations within one month before surgery.
- Postoperative VAS scores decreased by ≥60%, followed by an increase, confirmed by imaging.
- No other abnormalities detected in imaging. (E) Minimum follow-up period of 6 months.
- Inclusion Criteria for Non-rLDH: (A) Patients with L5-S1 lumbar disc herniation who underwent single-level PEID. (B) Completed comprehensive imaging examinations within one month before surgery. (C) Postoperative VAS scores decreased by ≥60% without recurrence. (D) No other abnormalities detected in imaging. (E) Minimum follow-up period of 6 months.
Who cannot join
- Presence of other pathological conditions causing lower back pain, such as disc infections, spinal tumors, metabolic bone disease, or osteoporosis. (B) History of prior lumbar disc or other spinal surgeries. (C) Poor imaging quality or incomplete examination data. (D) Patients lost to follow-up.
Trial locations and contacts
1 site in 1 country.
About this study
What Was the Study About? This study focused on improving the care of patients with a specific type of back problem called lumbar disc herniation at the L5-S1 level. Doctors often treat this condition with a minimally invasive surgery known as percutaneous endoscopic interlaminar discectomy (PEID). However, sometimes the herniation (the damaged disc) can come back after surgery. The goal of this study was to develop computer models that help predict which patients might experience a recurrence of their herniated disc.
Who Participated? The study reviewed the medical records of 309 patients who had undergone the PEID surgery. Out of these, 33 patients experienced a recurrence of their herniation, while 276 patients did not.
Read more
What Did the Researchers Do?
Data Collection:
They gathered information from each patient before the surgery, including clinical details (like body weight and any health conditions such as diabetes) and imaging studies (like X-rays, CT scans, or MRIs) that show the condition of the spine.
Identifying Key Risk Factors:
Using a statistical method called LASSO regression, the researchers identified eight important factors that could influence whether the herniation might come back. These included factors such as body mass index (BMI), a measure related to disc height (posterior disc height index), signs of spinal canal narrowing, how long the patient had symptoms before surgery, and other health conditions.
Developing Prediction Models:
They then used several machine learning techniques (advanced computer methods that learn from data) to build prediction models. Two of the best-performing models were based on methods called Random Forest and Extreme Gradient Boosting (XGB).
What Were the Main Findings?
Key Predictors: Higher BMI and changes in the disc (as measured by the posterior disc height index) were found to be the strongest predictors of a herniation coming back after surgery. Other factors, like spinal canal narrowing and longer duration of symptoms before surgery, also played significant roles.
Practical Implication: These models can help doctors identify which patients are at higher risk for recurrence. With this information, they can adjust treatment plans and follow-up care to better manage and potentially reduce the risk of the herniation coming back.
Why Is This Important? For patients and their families, this study offers hope for more personalized and effective treatment plans, reducing the chances of needing additional surgeries in the future. For healthcare providers, the findings provide useful tools to improve decision-making before surgery, ensuring better long-term outcomes for patients with L5-S1 lumbar disc herniation.
In summary, this research uses modern computer methods to predict the risk of recurrent disc herniation after a common minimally invasive back surgery, aiming to enhance patient care and improve surgical outcomes.
Read the detailed description
Detailed Description
This study aimed to enhance care for patients undergoing a minimally invasive spine surgery known as percutaneous endoscopic interlaminar discectomy (PEID), which is used to treat herniated discs at the L5-S1 level. Recurrent disc herniation-where the disc problem returns after surgery-can lead to additional pain and the need for further treatment. To address this issue, the research team conducted an in-depth review of patient data gathered at a single hospital.
How the Study Was Conducted Researchers collected comprehensive information from 309 patients who had undergone PEID. This information included clinical details (such as age, body mass index, and existing conditions like diabetes) and imaging data (from X-rays, CT scans, and MRIs) that provided insights into the structure and condition of the spine. Rather than relying on a single factor, the study examined a wide range of variables to understand which ones might predict a recurrence of the herniated disc.
Advanced Data Analysis and Prediction Methods To sift through the large amount of collected data, the team used a statistical technique called LASSO regression. This method helped identify the most influential factors from many possible measurements. Eight key factors emerged, including body mass index (BMI) and specific measurements related to the spinal disc's structure.
Building on this foundation, the study employed several machine learning techniques-advanced computer methods that detect patterns in data-to create models capable of predicting the risk of recurrence. Among the various models tested, two (Random Forest and Extreme Gradient Boosting) stood out for their strong performance. These models not only highlighted the significance of factors like BMI and certain spinal measurements but also provided a promising tool for clinicians to assess risk before surgery.
Why This Matters For healthcare providers, having a reliable predictive model means they can better tailor surgical techniques and postoperative care to individual patients. By understanding a patient's risk profile, surgeons can take additional precautions or consider alternative approaches to reduce the chance of recurrence. For patients and their families, this translates into more personalized treatment plans and potentially fewer complications or repeat surgeries in the future.
In Summary This study represents an important step toward personalized medicine in spinal care. By integrating detailed clinical and imaging data with state-of-the-art machine learning techniques, the researchers developed a model that can forecast the likelihood of a recurrent herniated disc after PEID surgery. The insights gained not only improve the understanding of key risk factors but also pave the way for more targeted and effective treatment strategies, ultimately aiming to enhance long-term patient outcomes.
- Conditions
- Recurrent Lumbar Disc Herniation
- Being tested
- VAS Point and Imaging Examination
- Keywords
- Machine Learning, Ensemble Learning
- Medical subject headings
- Intervertebral Disc Displacement
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
VAS Point and Imaging ExaminationDiagnostic test
This intervention uses a machine learning model to predict the risk of recurrent lumbar disc herniation (rLDH) in patients who have had percutaneous endoscopic interlaminar discectomy (PEID) at the L5-S1 level. The model combines clinical data (e.g., BMI, disease duration, diabetes) and imaging metrics (e.g., posterior disc height index, spinal canal stenosis) to create a personalized risk score, unlike traditional methods that rely on clinical judgment or imaging alone.
Key Features:
Data-Driven Approach: Developed using data from 309 patients for real-world relevance.
Advanced Variable Selection: Identifies eight key predictors using LASSO regression.
Multiple Machine Learning Techniques: Uses algorithms like support vector machine, random forest, and extreme gradient boosting.
Optimized for Clinical Decision-Making: Assists surgeons in personalizing treatment plans to reduce recurrence risk.
| Study group | Role | What they receive |
|---|---|---|
| Recurrent rLDH | : Patients who experienced recurrent lumbar disc herniation following L5-S1 PEID. Receives: Diagnostic Test: VAS Point and Imaging Examination | |
| Non-Recurrent rLDH | Patients who did not experience recurrent lumbar disc herniation following L5-S1 PEID. Receives: Diagnostic Test: VAS Point and Imaging Examination |
What is being measured
The primary outcome is the main question the study is designed to answer.
Recurrence of Lumbar Disc Herniation (rLDH) Following Percutaneous Endoscopic Interlaminar Discectomy (PEID) at the L5-S1 Level
The primary outcome measure will assess the recurrence of lumbar disc herniation (rLDH) in patients who have undergone percutaneous endoscopic interlaminar discectomy (PEID) at the L5-S1 level. The occurrence of rLDH will be evaluated based on clinical symptoms and imaging findings, including MRI or CT scans, within a specified follow-up period post-surgery. This measure aims to develop a predictive model to estimate the likelihood of recurrence of disc herniation following PEID at the L5-S1 level.
Measured: The recurrence will be monitored and documented during follow-up visits at least 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Feb 2025
- Study start1 Jan 2020
- Primary completion31 May 2024
- Study completion1 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (21)Papers the sponsor has linked to this study.
- Other trials for Recurrent Lumbar Disc HerniationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 309 participants (actual)
Sponsor and investigators
- Lead sponsor
- Jinyu Chen
- Sponsor type
- University, hospital or other
- Collaborators
- Nantong First People's Hospital
- Responsible party
- Sponsor-investigator, Jinyu Chen, Nantong First People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06833099
- Sponsor's protocol ID
- JiajiaChen
- Status last verified
- Oct 2024
- First submitted
- 12 Feb 2025
- Last updated
- 18 Feb 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to the sensitive nature of patient clinical and imaging data, and to protect patient privacy and data security, we do not plan to share individual participant data (IPD) with other researchers. Sharing such data could potentially lead to the leakage of personal information, so we have decided not to make these data publicly available. All data will be handled in strict accordance with privacy protection regulations set by the hospital's ethics committee and will be used exclusively within the research team.
Publications
Shi H, Zhu L, Jiang ZL, Wu XT. Radiological risk factors for recurrent lumbar disc herniation after percutaneous transforaminal endoscopic discectomy: a retrospective matched case-control study. Eur Spine J. 2021 Apr;30(4):886-892. doi: 10.1007/s00586-020-06674-3. Epub 2021 Jan 1.
PubMed 33386474Yu C, Zhan X, Liu C, Liao S, Xu J, Liang T, Zhang Z, Chen J. Risk Factors for Recurrent L5-S1 Disc Herniation After Percutaneous Endoscopic Transforaminal Discectomy: A Retrospective Study. Med Sci Monit. 2020 Mar 25;26:e919888. doi: 10.12659/MSM.919888.
PubMed 32210223Choi G, Lee SH, Raiturker PP, Lee S, Chae YS. Percutaneous endoscopic interlaminar discectomy for intracanalicular disc herniations at L5-S1 using a rigid working channel endoscope. Neurosurgery. 2006 Feb;58(1 Suppl):ONS59-68; discussion ONS59-68. doi: 10.1227/01.neu.0000192713.95921.4a.
PubMed 16479630Siemionow K, An H, Masuda K, Andersson G, Cs-Szabo G. The effects of age, sex, ethnicity, and spinal level on the rate of intervertebral disc degeneration: a review of 1712 intervertebral discs. Spine (Phila Pa 1976). 2011 Aug 1;36(17):1333-9. doi: 10.1097/BRS.0b013e3181f2a177.
PubMed 21217432Li Y, Wang B, Li H, Chang X, Wu Y, Hu Z, Liu C, Gao X, Zhang Y, Liu H, Li Y, Li C. Adjuvant surgical decision-making system for lumbar intervertebral disc herniation after percutaneous endoscopic lumber discectomy: a retrospective nonlinear multiple logistic regression prediction model based on a large sample. Spine J. 2021 Dec;21(12):2035-2048. doi: 10.1016/j.spinee.2021.07.012. Epub 2021 Jul 20.
PubMed 34298160Jia M, Sheng Y, Chen G, Zhang W, Lin J, Lu S, Li F, Ying J, Teng H. Development and validation of a nomogram predicting the risk of recurrent lumbar disk herniation within 6 months after percutaneous endoscopic lumbar discectomy. J Orthop Surg Res. 2021 Apr 21;16(1):274. doi: 10.1186/s13018-021-02425-2.
PubMed 33882995Han M, Liu L, Hu M, Liu G, Li P. Medical expert and machine learning analysis of lumbar disc herniation based on magnetic resonance imaging. Comput Methods Programs Biomed. 2022 Jan;213:106498. doi: 10.1016/j.cmpb.2021.106498. Epub 2021 Oct 29.
PubMed 34758430Li R, Fu D, Han H, Zhan Z, Wu Y, Meng B. Comparative analysis of percutaneous endoscopic interlaminar discectomy for highly downward-migrated disc herniation. J Orthop Surg Res. 2023 Aug 14;18(1):602. doi: 10.1186/s13018-023-04090-z.
PubMed 37580753Berg B, Gorosito MA, Fjeld O, Haugerud H, Storheim K, Solberg TK, Grotle M. Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery. JAMA Netw Open. 2024 Feb 5;7(2):e2355024. doi: 10.1001/jamanetworkopen.2023.55024.
PubMed 38324310Harada GK, Siyaji ZK, Mallow GM, Hornung AL, Hassan F, Basques BA, Mohammed HA, Sayari AJ, Samartzis D, An HS. Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile. Eur Spine J. 2021 Aug;30(8):2167-2175. doi: 10.1007/s00586-021-06866-5. Epub 2021 Jun 7.
PubMed 34100112Wang H, Zhou Y, Li C, Liu J, Xiang L. Risk factors for failure of single-level percutaneous endoscopic lumbar discectomy. J Neurosurg Spine. 2015 Sep;23(3):320-5. doi: 10.3171/2014.10.SPINE1442. Epub 2015 Jun 12.
PubMed 26068272Huang W, Han Z, Liu J, Yu L, Yu X. Risk Factors for Recurrent Lumbar Disc Herniation: A Systematic Review and Meta-Analysis. Medicine (Baltimore). 2016 Jan;95(2):e2378. doi: 10.1097/MD.0000000000002378.
PubMed 26765413Li H, Deng W, Wei F, Zhang L, Chen F. Factors related to the postoperative recurrence of lumbar disc herniation treated by percutaneous transforaminal endoscopy: A meta-analysis. Front Surg. 2023 Jan 19;9:1049779. doi: 10.3389/fsurg.2022.1049779. eCollection 2022.
PubMed 36743903Ren G, Liu L, Zhang P, Xie Z, Wang P, Zhang W, Wang H, Shen M, Deng L, Tao Y, Li X, Wang J, Wang Y, Wu X. Machine Learning Predicts Recurrent Lumbar Disc Herniation Following Percutaneous Endoscopic Lumbar Discectomy. Global Spine J. 2024 Jan;14(1):146-152. doi: 10.1177/21925682221097650. Epub 2022 May 2.
PubMed 35499394Modic MT, Ross JS. Lumbar degenerative disk disease. Radiology. 2007 Oct;245(1):43-61. doi: 10.1148/radiol.2451051706.
PubMed 17885180
Study at a glance
From the registry record, last updated by the sponsor on 18 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach
- ClinicalTrials.gov ID
- NCT06833099
- Acronym / sponsor ID
- JiajiaChen
- Conditions
- Recurrent Lumbar Disc Herniation
- Keywords
- Machine Learning, Ensemble Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 309 participants (actual)
- Lead sponsor
- Jinyu ChenUniversity, hospital or other
- Collaborators
- Nantong First People's Hospital
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 31 May 2024 (actual)
- Study completion
- 1 Nov 2024 (actual)
- First posted
- 18 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
What Was the Study About? This study focused on improving the care of patients with a specific type of back problem called lumbar disc herniation at the L5-S1 level. Doctors often treat this condition with a minimally invasive surgery known as percutaneous endoscopic interlaminar discectomy (PEID). However, sometimes the herniation (the damaged disc) can come back after surgery. The goal of this study was to develop computer models that help predict which patients might experience a recurrence of their herniated disc.
Who Participated? The study reviewed the medical records of 309 patients who had undergone the PEID surgery. Out of these, 33 patients experienced a recurrence of their herniation, while 276 patients did not.
What Did the Researchers Do?
Data Collection:
They gathered information from each patient before the surgery, including clinical details (like body weight and any health conditions such as diabetes) and imaging studies (like X-rays, CT scans, or MRIs) that show the condition of the spine.
Identifying Key Risk Factors:
Using a statistical method called LASSO regression, the researchers identified eight important factors that could influence whether the herniation might come back. These included factors such as body mass index (BMI), a measure related to disc height (posterior disc height index), signs of spinal canal narrowing, how long the patient had symptoms before surgery, and other health conditions.
Developing Prediction Models:
They then used several machine learning techniques (advanced computer methods that learn from data) to build prediction models. Two of the best-performing models were based on methods called Random Forest and Extreme Gradient Boosting (XGB).
What Were the Main Findings?
Key Predictors: Higher BMI and changes in the disc (as measured by the posterior disc height index) were found to be the strongest predictors of a herniation coming back after surgery. Other factors, like spinal canal narrowing and longer duration of symptoms before surgery, also played significant roles.
Practical Implication: These models can help doctors identify which patients are at higher risk for recurrence. With this information, they can adjust treatment plans and follow-up care to better manage and potentially reduce the risk of the herniation coming back.
Why Is This Important? For patients and their families, this study offers hope for more personalized and effective treatment plans, reducing the chances of needing additional surgeries in the future. For healthcare providers, the findings provide useful tools to improve decision-making before surgery, ensuring better long-term outcomes for patients with L5-S1 lumbar disc herniation.
In summary, this research uses modern computer methods to predict the risk of recurrent disc herniation after a common minimally invasive back surgery, aiming to enhance patient care and improve surgical outcomes.
Detailed description
Detailed Description
This study aimed to enhance care for patients undergoing a minimally invasive spine surgery known as percutaneous endoscopic interlaminar discectomy (PEID), which is used to treat herniated discs at the L5-S1 level. Recurrent disc herniation-where the disc problem returns after surgery-can lead to additional pain and the need for further treatment. To address this issue, the research team conducted an in-depth review of patient data gathered at a single hospital.
How the Study Was Conducted Researchers collected comprehensive information from 309 patients who had undergone PEID. This information included clinical details (such as age, body mass index, and existing conditions like diabetes) and imaging data (from X-rays, CT scans, and MRIs) that provided insights into the structure and condition of the spine. Rather than relying on a single factor, the study examined a wide range of variables to understand which ones might predict a recurrence of the herniated disc.
Advanced Data Analysis and Prediction Methods To sift through the large amount of collected data, the team used a statistical technique called LASSO regression. This method helped identify the most influential factors from many possible measurements. Eight key factors emerged, including body mass index (BMI) and specific measurements related to the spinal disc's structure.
Building on this foundation, the study employed several machine learning techniques-advanced computer methods that detect patterns in data-to create models capable of predicting the risk of recurrence. Among the various models tested, two (Random Forest and Extreme Gradient Boosting) stood out for their strong performance. These models not only highlighted the significance of factors like BMI and certain spinal measurements but also provided a promising tool for clinicians to assess risk before surgery.
Why This Matters For healthcare providers, having a reliable predictive model means they can better tailor surgical techniques and postoperative care to individual patients. By understanding a patient's risk profile, surgeons can take additional precautions or consider alternative approaches to reduce the chance of recurrence. For patients and their families, this translates into more personalized treatment plans and potentially fewer complications or repeat surgeries in the future.
In Summary This study represents an important step toward personalized medicine in spinal care. By integrating detailed clinical and imaging data with state-of-the-art machine learning techniques, the researchers developed a model that can forecast the likelihood of a recurrent herniated disc after PEID surgery. The insights gained not only improve the understanding of key risk factors but also pave the way for more targeted and effective treatment strategies, ultimately aiming to enhance long-term patient outcomes.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Recurrent rLDH | – | Diagnostic Test: VAS Point and Imaging Examination: Patients who experienced recurrent lumbar disc herniation following L5-S1 PEID. |
| Non-Recurrent rLDH | – | Diagnostic Test: VAS Point and Imaging ExaminationPatients who did not experience recurrent lumbar disc herniation following L5-S1 PEID. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Study Population Description: The study population consisted of 309 patients who underwent percutaneous endoscopic interlaminar discectomy (PEID) for L5-S1 lumbar disc herniation between January 2020 and June 2024 at Nantong First People's Hospital. All patients had at least 6 months of follow-up post-surgery. The study focused on identifying factors that predict recurrent lumbar disc herniation (rLDH) after the procedure.
Key inclusion criteria (4 listed)
- Inclusion Criteria for rLDH: (A) Patients with L5-S1 lumbar disc herniation who underwent single level PEID. (B) Completed comprehensive imaging examinations within one month before surgery.
- Postoperative VAS scores decreased by ≥60%, followed by an increase, confirmed by imaging.
- No other abnormalities detected in imaging. (E) Minimum follow-up period of 6 months.
- Inclusion Criteria for Non-rLDH: (A) Patients with L5-S1 lumbar disc herniation who underwent single-level PEID. (B) Completed comprehensive imaging examinations within one month before surgery. (C) Postoperative VAS scores decreased by ≥60% without recurrence. (D) No other abnormalities detected in imaging. (E) Minimum follow-up period of 6 months.
Key exclusion criteria (1 listed)
- Presence of other pathological conditions causing lower back pain, such as disc infections, spinal tumors, metabolic bone disease, or osteoporosis. (B) History of prior lumbar disc or other spinal surgeries. (C) Poor imaging quality or incomplete examination data. (D) Patients lost to follow-up.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| VAS Point and Imaging ExaminationThis intervention uses a machine learning model to predict the risk of recurrent lumbar disc herniation (rLDH) in patients who have had percutaneous endoscopic interlaminar discectomy (PEID) at the L5-S1 level. The model combines clinical data (e.g., BMI, disease duration, diabetes) and imaging metrics (e.g., posterior disc height index, spinal canal stenosis) to create a personalized risk score, unlike traditional methods that rely on clinical judgment or imaging alone. Key Features: Data-Driven Approach: Developed using data from 309 patients for real-world relevance. Advanced Variable Selection: Identifies eight key predictors using LASSO regression. Multiple Machine Learning Techniques: Uses algorithms like support vector machine, random forest, and extreme gradient boosting. Optimized for Clinical Decision-Making: Assists surgeons in personalizing treatment plans to reduce recurrence risk. | Diagnostic test | Non-Recurrent rLDH, Recurrent rLDH |
Primary outcome
Recurrence of Lumbar Disc Herniation (rLDH) Following Percutaneous Endoscopic Interlaminar Discectomy (PEID) at the L5-S1 Level
Time frame: The recurrence will be monitored and documented during follow-up visits at least 6 months
The primary outcome measure will assess the recurrence of lumbar disc herniation (rLDH) in patients who have undergone percutaneous endoscopic interlaminar discectomy (PEID) at the L5-S1 level. The occurrence of rLDH will be evaluated based on clinical symptoms and imaging findings, including MRI or CT scans, within a specified follow-up period post-surgery. This measure aims to develop a predictive model to estimate the likelihood of recurrence of disc herniation following PEID at the L5-S1 level.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Feb 2025.
- Background: PubMed 33386474Shi H, Zhu L, Jiang ZL, Wu XT. Radiological risk factors for recurrent lumbar disc herniation after percutaneous transforaminal endoscopic discectomy: a retrospective matched case-control study. Eur Spine J. 2021 Apr;30(4):886-892. doi: 10.1007/s00586-020-06674-3. Epub 2021 Jan 1.
- Background: PubMed 32210223Yu C, Zhan X, Liu C, Liao S, Xu J, Liang T, Zhang Z, Chen J. Risk Factors for Recurrent L5-S1 Disc Herniation After Percutaneous Endoscopic Transforaminal Discectomy: A Retrospective Study. Med Sci Monit. 2020 Mar 25;26:e919888. doi: 10.12659/MSM.919888.
- Background: PubMed 16479630Choi G, Lee SH, Raiturker PP, Lee S, Chae YS. Percutaneous endoscopic interlaminar discectomy for intracanalicular disc herniations at L5-S1 using a rigid working channel endoscope. Neurosurgery. 2006 Feb;58(1 Suppl):ONS59-68; discussion ONS59-68. doi: 10.1227/01.neu.0000192713.95921.4a.
- Background: PubMed 21217432Siemionow K, An H, Masuda K, Andersson G, Cs-Szabo G. The effects of age, sex, ethnicity, and spinal level on the rate of intervertebral disc degeneration: a review of 1712 intervertebral discs. Spine (Phila Pa 1976). 2011 Aug 1;36(17):1333-9. doi: 10.1097/BRS.0b013e3181f2a177.
- Background: PubMed 34298160Li Y, Wang B, Li H, Chang X, Wu Y, Hu Z, Liu C, Gao X, Zhang Y, Liu H, Li Y, Li C. Adjuvant surgical decision-making system for lumbar intervertebral disc herniation after percutaneous endoscopic lumber discectomy: a retrospective nonlinear multiple logistic regression prediction model based on a large sample. Spine J. 2021 Dec;21(12):2035-2048. doi: 10.1016/j.spinee.2021.07.012. Epub 2021 Jul 20.
- Background: PubMed 33882995Jia M, Sheng Y, Chen G, Zhang W, Lin J, Lu S, Li F, Ying J, Teng H. Development and validation of a nomogram predicting the risk of recurrent lumbar disk herniation within 6 months after percutaneous endoscopic lumbar discectomy. J Orthop Surg Res. 2021 Apr 21;16(1):274. doi: 10.1186/s13018-021-02425-2.
- Background: PubMed 34758430Han M, Liu L, Hu M, Liu G, Li P. Medical expert and machine learning analysis of lumbar disc herniation based on magnetic resonance imaging. Comput Methods Programs Biomed. 2022 Jan;213:106498. doi: 10.1016/j.cmpb.2021.106498. Epub 2021 Oct 29.
- Background: PubMed 37580753Li R, Fu D, Han H, Zhan Z, Wu Y, Meng B. Comparative analysis of percutaneous endoscopic interlaminar discectomy for highly downward-migrated disc herniation. J Orthop Surg Res. 2023 Aug 14;18(1):602. doi: 10.1186/s13018-023-04090-z.
- Background: PubMed 38324310Berg B, Gorosito MA, Fjeld O, Haugerud H, Storheim K, Solberg TK, Grotle M. Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery. JAMA Netw Open. 2024 Feb 5;7(2):e2355024. doi: 10.1001/jamanetworkopen.2023.55024.
- Background: PubMed 34100112Harada GK, Siyaji ZK, Mallow GM, Hornung AL, Hassan F, Basques BA, Mohammed HA, Sayari AJ, Samartzis D, An HS. Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile. Eur Spine J. 2021 Aug;30(8):2167-2175. doi: 10.1007/s00586-021-06866-5. Epub 2021 Jun 7.
- Background: PubMed 26068272Wang H, Zhou Y, Li C, Liu J, Xiang L. Risk factors for failure of single-level percutaneous endoscopic lumbar discectomy. J Neurosurg Spine. 2015 Sep;23(3):320-5. doi: 10.3171/2014.10.SPINE1442. Epub 2015 Jun 12.
- Background: PubMed 26765413Huang W, Han Z, Liu J, Yu L, Yu X. Risk Factors for Recurrent Lumbar Disc Herniation: A Systematic Review and Meta-Analysis. Medicine (Baltimore). 2016 Jan;95(2):e2378. doi: 10.1097/MD.0000000000002378.
- Individual participant dataSharing planned: No. Due to the sensitive nature of patient clinical and imaging data, and to protect patient privacy and data security, we do not plan to share individual participant data (IPD) with other researchers. Sharing such data could potentially lead to the leakage of personal information, so we have decided not to make these data publicly available. All data will be handled in strict accordance with privacy protection regulations set by the hospital's ethics committee and will be used exclusively within the research team.
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Disc Herniation, Disk Herniated Lumbar
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Recurrent Lumbar Disc Herniation, run by Jinyu Chen at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Recurrence of Lumbar Disc Herniation (rLDH) Following Percutaneous Endoscopic Interlaminar Discectomy (PEID) at the L5-S1 Level (time frame: The recurrence will be monitored and documented during follow-up visits at least 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06833099, which also lists 21 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Feb 2025.