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

SPINE-RISK VE: Development and Internal Validation of a Multimodal Preoperative Predictive Model for Failed Back Surgery Syndrome Using Inflammatory Biomarkers, Lumbar MRI Findings, and Psychosocial Factors in Venezuelan Surgical Patients

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Postoperative Pain; Chronic Low Back Pain; Lumbar Spine Surgery; Persistent Spinal Pain Syndrome Type 2 (PSPS-T) Lower Spine
• Intervention / Treatment: Other: SPINE-RISK VE multimodal preoperative assessment

Detailed Description

Failed Back Surgery Syndrome (FBSS), formally reclassified as Persistent Spinal Pain Syndrome Type 2 (PSPS-T2) in ICD-11 (code MG30.51), affects 10-40% of patients undergoing lumbar spine surgery and constitutes one of the most complex therapeutic challenges in contemporary neurosurgery. Despite the identification of individual risk factors in the literature, no validated multimodal predictive model integrating laboratory biomarkers, lumbar magnetic resonance imaging (MRI) morphology, and psychosocial variables exist for Latin American surgical populations.

The best available predictive model to date achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.715 for decompression and 0.701 for fusion using only electronic health record variables, without laboratory biomarkers or MRI-derived predictors, and without validation in any Latin American cohort. SPINE-RISK VE addresses this gap through a prospective multicenter cohort design enrolling 100-150 adults with Elective lumbar surgery indication at three Venezuelan referral centers.

PREDICTOR DOMAINS:

Domain 1 - Inflammatory biomarkers:

C-reactive protein (CRP greater than 3 mg/L), neutrophil-to-lymphocyte ratio (NLR greater than 3.0), serum albumin (less than 3.5 g/dL), glycated hemoglobin (HbA1c greater than 7%), and erythrocyte sedimentation rate (ESR). All obtainable from standard preoperative Laboratory panels.

Domain 2 - Lumbar MRI findings: Modic changes (Types I-III), disc degeneration grade (Pfirrmann scale I-V), foraminal stenosis, number of surgical levels, and Spondylolisthesis grade (Meyerding I-IV). All from already-requested preoperative imaging.

Domain 3 - Psychosocial factors: depression (Patient Health Questionnaire-9 [PHQ-9] cutoff of 10 or greater), pain catastrophizing (Pain Catastrophizing Scale [PCS] cutoff of 30 or greater, active smoking, preoperative benzodiazepine use, and prior lumbar surgery history.

PRIMARY OUTCOME: PSPS-T2/FBSS incidence at 12 months, defined as the Numeric Rating Scale (NRS) of 4 or greater AND Oswestry Disability Index (ODI) of 40% or greater at postoperative follow-up, consistent with International Association for the Study of Pain (IASP) criteria.

ANALYTICAL PLAN:

Phase 1: Multivariable logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) regularization to identify independent predictors and generate a Printable clinical nomogram. Software: R Version 4.x (glmnet, rms packages).

Phase 2: Random forest (500 trees, 10-fold cross-validation) compared against Extreme Gradient Boosting (XGBoost) and logistic regression. Performance metrics: AUC-ROC (target 0.80 or greater), sensitivity, specificity, calibration (Hosmer-Lemeshow test, Brier score). Model interpretability via SHapley Additive exPlanations (SHAP) values.

REPORTING: Transparent Reporting of a multivariable prediction model for an individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) 2024 and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

EXPECTED OUTPUTS: (1) Printable preoperative nomogram applicable without additional technological infrastructure; (2) exportable machine learning (ML) model with AUC target of 0.80 or greater; (3) first structured lumbar surgery database with 12-month Follow-up generated in Venezuela.

• Study Type: Observational
• Enrollment (Estimated): 150

Contacts and Locations

• Study Contact: Name: juan j Valero, Medical Doctor; Phone Number: 00584224263876 7868055589; Email: juanjoseneuro@gmail.com
• Study Contact Backup: Name: Fredy Contreras, PHD; Phone Number: 00584149109021; Email: sicontreras2009@gmail.com

Study Locations

• Venezuela
  • Distrito Capitañ
    • Caracas, Distrito Capitañ, Venezuela, 1050
      • Hopsital Universitario de Caracas
      • Contact: juan j valero, Medical Doctor; Phone Number: 7868055589; Email: juanjoseneuro@gmail.com
      • Contact: Edicson j Cardenas, Nursing; Phone Number: 00584241540328; Email: edicsonjcardenas@gmail.com
      • Principal Investigator: juan j valero, Medical Doctor
      • Sub-Investigator: Fredy Contreras, PHD
      • Sub-Investigator: Hector r Aceituno, Medical doctor
      • Sub-Investigator: Edicson j cardenas, Nursing

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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

Description

Inclusion Criteria:- Age 18 years or older

• 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

Exclusion Criteria:- Emergency lumbar spine surgery

• 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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: juan jose valero quintero,MD
• Investigators: Principal Investigator: juan j valero, Medical Doctor, Universidad Central de Venezuela

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): September 4, 2026
• Primary Completion (Estimated): November 4, 2027
• Study Completion (Estimated): February 10, 2028

Study Registration Dates

• First Submitted: July 3, 2026
• First Submitted That Met QC Criteria: July 3, 2026
• First Posted (Actual): July 9, 2026

Study Record Updates

• Last Update Posted (Actual): July 10, 2026
• Last Update Submitted That Met QC Criteria: July 8, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; predictive model; inflammatory biomarkers; Latin America; Venezuela; Modic changes; pain catastrophizing; random forest; LASSO regression; TRIPOD+AI
• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Pain, Postoperative
• Other Study ID Numbers: SPINE-RISK-VE-2026-001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data (IPD) will not be shared. This study collects sensitive clinical and psychosocial data from Venezuelan patients In a low-resource setting. The research team does not have access to formal data repository infrastructure for anonymized IPD sharing that meets international data protection standards. Aggregate study results and the derived predictive model (nomogram and machine learning algorithm) will be made publicly available through peer-reviewed publication In an indexed journal.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No