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2026, Number 4

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Cir Columna 2026; 4 (4)

Postoperative radiculitis following anterior lumbar interbody fusion (ALIF): a systematic review

Valdez-Aguilar JE, García-González U, Taylor-Martínez MA

Language: Spanish
References: 22
Page: 320-326
PDF size: 685.51 Kb.


ABSTRACT

Introduction: postoperative radiculitis is a relevant complication following anterior lumbar interbody fusion (ALIF), involving inflammatory irritation of the nerve root without mechanical compression. Reported incidence ranges from 5-30%, influenced by surgical parameters and patient-specific characteristics. Objective: to analyze the dependent and independent factors associated with postoperative radiculitis after ALIF, describing biomechanical mechanisms, clinical relevance, and prevention strategies. Material and methods: a systematic review was conducted using PubMed, Scopus, and Google Scholar databases (2000-2024) with MeSH terms: radiculopathy, lumbar fusion, anterior lumbar interbody fusion, postoperative complications. Clinical studies, comparative trials and reviews reporting postoperative radiculitis were included. Odds Ratio (OR), p-values, and associated predictors were extracted and categorized. Results: surgery-dependent risk factors included excessive disc height restoration ≥ 12 mm (OR 1.9; p = 0.09), segmental lordosis correction > 20-25° (OR 2.4; p < 0.05), absence of posterior instrumentation (OR 2.97; p = 0.0088), and perioperative systemic corticosteroid use (OR 6.03; p = 0.0094). Independent factors such as obesity, prior abdominal surgery, osteoporosis, older age, and female gender did not demonstrate a statistically significant association with radiculitis, although linked to overall postoperative complication risk and delayed neurological recovery. Conclusions: post-ALIF radiculitis is multifactorial, requiring individualized surgical planning, biomechanically safe correction goals, and perioperative risk optimization. Prospective multicenter trials are needed to establish standardized thresholds for correction and validated predictive models.


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