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临床试验/NCT07693517
NCT07693517尚未招募不适用

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

juan jose valero quintero,MD1 个研究点 分布在 1 个国家目标入组 150 人开始时间: 2026年9月4日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
尚未招募
发起方
入组人数
150
试验地点
1
主要终点
Predictive accuracy of SPINE-RISK VE model for PSPS-T2/FBSS at 12 months

研究概览

简要总结

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.

详细描述

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.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

年龄范围
18 Years 至 100 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • 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

排除标准

  • 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

研究组 & 干预措施

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.

干预措施: SPINE-RISK VE multimodal preoperative assessment (Other)

结局指标

主要结局

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

时间窗: 12 months post-lumbar surgery

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.

次要结局

未报告次要终点

研究者

发起方
juan jose valero quintero,MD
申办方类型
Other
责任方
Sponsor Investigator
主要研究者

juan jose valero quintero,MD

Principal Investigator, Neurosurgeon and Pain Medicine Specialist

Universidad Central de Venezuela

研究点 (1)

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