SPINE-RISK VE: Multimodal Predictive Model for Failed Back Surgery Syndrome in Venezuelan Surgical Patients

NCT07693517 | Not Yet Recruiting

• 1 other identifier: SPINE-RISK-VE-2026-001
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

SPINE-RISK VE is a prospective multicenter cohort study designed to develop and internally validate a multimodal preoperative predictive model for Failed Back Surgery Syndrome (FBSS), now classified as Persistent Spinal Pain Syndrome Type 2 (PSPS-T2) per ICD-11 (code MG30.51), in Venezuelan adults patients undergoing elective lumbar spine surgery. The model integrates three variable domains obtainable from routine preoperative evaluation at zero additional cost to the patient: (1) inflammatory laboratory biomarkers (C-reactive protein \[CRP\], neutrophil-to-lymphocyte ratio \[NLR\], albumin, glycated hemoglobin \[HbA1c\], erythrocyte sedimentation rate \[ESR\]); (2) preoperative lumbar magnetic resonance imaging (MRI) findings (Modic changes, Pfirrmann disc degeneration grade, foraminal stenosis, number of surgical levels, spondylolisthesis); and (3) validated psychosocial instruments (Patient Health Questionnaire-9 \[PHQ-9\], Pain Catastrophizing Scale \[PCS\], smoking status, benzodiazepine use, prior lumbar surgery). Analysis proceeds in two phases: Phase 1 applies multivariable logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) variable selection to generate a printable clinical nomogram; Phase 2 applies a random forest machine learning algorithm with 10-fold cross-validation. Model reporting follows Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) guidelines. SPINE-RISK VE aims to produce the first validated multimodal predictive model for PSPS-T2/FBSS was developed in a Latin American surgical cohort, providing neurosurgeons with an evidence-based preoperative risk stratification tool applicable without Additional technological infrastructure.

Conditions

Persistent Spinal Pain Syndrome Type 2 (PSPS-T) Lower Spine; Postoperative Pain; Lumbar Spine Surgery; Chronic Low Back Pain

Keywords

predictive model; machine learning; random forest; LASSO regression; Modic changes; inflammatory biomarkers; pain catastrophizing; TRIPOD+AI; Venezuela; Latin America

Outcome Measures

Primary Outcomes (1)

• Predictive accuracy of SPINE-RISK VE model for PSPS-T2/FBSS at 12 months

  Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the multimodal predictive model (Phase 1: LASSO logistic regression nomogram; Phase 2: random forest algorithm) for identifying patients who develop Persistent Spinal Pain Syndrome Type 2 (PSPS-T2/FBSS) at 12 months post-lumbar surgery, defined as NRS \>=4 AND ODI \>=40% at postoperative follow-up assessment. Target AUC \>=0.80 per Riley et al. (Stat Med 2020) Minimum criteria for clinical prediction models.

  12 months post-lumbar surgery

Study Arms (1)

Lumbar surgery candidates

Adult patients (18 years or older) with an indication for elective lumbar spine surgery (discectomy, spinal fusion, or decompression) for degenerative lumbar disease at three Venezuelan referrals centers. All participants undergo standardized preoperative assessment, including inflammatory laboratory biomarkers, lumbar MRI morphological evaluation, and validated psychosocial instruments (PHQ-9, PCS). The primary outcome assessed at 12-month postoperative follow-up.

Other: SPINE-RISK VE multimodal preoperative assessment

Interventions

SPINE-RISK VE multimodal preoperative assessment OTHER

Adult patients (18 years or older) with an indication for elective lumbar spine surgery (discectomy, spinal fusion, or decompression) for degenerative lumbar disease at three Venezuelan referral centers. All participants undergo standardized preoperative assessment, including inflammatory laboratory biomarkers, lumbar MRI morphological evaluation, and validated psychosocial instruments (PHQ-9, PCS). Primary outcome assessed at 12-month postoperative follow-up

Lumbar surgery candidates

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients with degenerative lumbar spine disease undergoing elective lumbar surgery (discectomy, spinal fusion, or decompression) at three Venezuelan referral centers: Hospital Universitario de Caracas and two regional Neurosurgical referral centers in Venezuela. Consecutive recruitment during the study period. This population represents a low-middle income country (LMIC) Latin American surgical cohort not previously represented in published predictive models for PSPS-T2/FBSS

You may qualify if:

• Confirmed indication for elective lumbar spine surgery (discectomy, spinal fusion, or decompression) for degenerative lumbar disease
• Availability of preoperative lumbar MRI (with and without gadolinium contrast) within 6 months before surgery
• Availability of standard preoperative laboratory panel (CRP, CBC with differential, albumin, HbA1c, ESR) within 30 days before surgery
• Ability to complete validated psychosocial instruments (PHQ-9, PCS) in Spanish
• Provision of written informed consent prior to any study procedure
• Attending one of the three participating Venezuelan referral centers during the recruitment period

You may not qualify if:

• Active spinal infection or spinal tumor requiring oncological surgery
• Traumatic spinal fracture as primary indication
• Cognitive impairment preventing completion of self-report psychosocial instruments
• Active psychiatric emergency at time of preoperative assessment
• Prior participation in another clinical trial that could influence surgical or pain outcomes
• Inability to complete 12-month postoperative follow-up (geographic inaccessibility, planned relocation, or terminal illness)
• Age under 18 years

Sponsors & Collaborators

• juan jose valero quintero,MD: lead

Study Sites (1)

Hopsital Universitario de Caracas

Caracas, Distrito Capitañ, 1050, Venezuela

Location

Related Publications (7)

• von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Prev Med. 2007 Oct;45(4):247-51. doi: 10.1016/j.ypmed.2007.08.012. Epub 2007 Sep 4.

  PMID: 17950122 RESULT

• Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606-13. doi: 10.1046/j.1525-1497.2001.016009606.x.

  PMID: 11556941 RESULT

• Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.

  PMID: 30357870 RESULT

• Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.

  PMID: 38626948 RESULT

• Xu W, Ran B, Zhao J, Luo W, Gu R. Risk factors for failed back surgery syndrome following open posterior lumbar surgery for degenerative lumbar disease. BMC Musculoskelet Disord. 2022 Dec 31;23(1):1141. doi: 10.1186/s12891-022-06066-2.

  PMID: 36585650 RESULT

• Hajilo P, Imani B, Zandi S, Mehrafshan A, Khazaei S. Risk factors analysis and risk prediction model for failed back surgery syndrome: A prospective cohort study. Heliyon. 2024 Nov 22;11(1):e40607. doi: 10.1016/j.heliyon.2024.e40607. eCollection 2025 Jan 15.

  PMID: 39866404 RESULT

• Khazanchi R, Kumar D, Oris RJ, Bajaj A, Herrera DE, Chen AR, Shah RM, Asthana S, Reyes SG, Bajaj P, Hsu WK, Patel AA, Divi SN. Identifying Predictors of Failed Back Surgery Syndrome Following Lumbar Spine Surgery: A Machine Learning Approach. Spine (Phila Pa 1976). 2026 May 15;51(10):736-742. doi: 10.1097/BRS.0000000000005411. Epub 2025 May 29.

  PMID: 40443211 RESULT

MeSH Terms

Conditions

Pain, Postoperative

Condition Hierarchy (Ancestors)

Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Pain; Neurologic Manifestations; Signs and Symptoms

Study Officials

• juan j valero, Medical Doctor

  Universidad Central de Venezuela

  PRINCIPAL INVESTIGATOR

Central Study Contacts

juan j Valero, Medical Doctor

CONTACT

7868055589; juanjoseneuro@gmail.com

Fredy Contreras, PHD

CONTACT

00584149109021; sicontreras2009@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Principal Investigator, Neurosurgeon and Pain Medicine Specialist

Study Record Dates

• First Submitted: July 3, 2026
• First Posted: July 9, 2026
• Study Start (Estimated): September 4, 2026
• Primary Completion (Estimated): November 4, 2027
• Study Completion (Estimated): February 10, 2028
• Last Updated: July 10, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

VE (1)