ReviewNorth American Spine Society journal2025
The predictive value of the global alignment and proportion (GAP) score for mechanical complications following adult spinal deformity surgery: A systematic review and meta-analysis.
Review in North American Spine Society journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Mechanical complications (MCs) remain a significant challenge in adult spinal deformity (ASD) surgery. The global alignment and proportion (GAP) score, a pelvic incidence-based metric, was introduced to predict the risk of postoperative MCs by assessing the proportionality of spinopelvic alignment. Methods: We systematically reviewed the evidence on the predictive validity of the GAP score and its modifications with regard to postoperative MCs (eg, proximal junctional kyphosis/failure, rod fracture, or implant failure) and patient-reported outcomes in adult spinal deformity (ASD) surgery. A systematic search of PubMed/MEDLINE, Embase, Scopus, and the Cochrane Library was conducted for studies published from January 2017 to July 2025. A proportion meta-analysis regarding the occurrence of MCs was performed using those articles that divided the patients in the three original GAP categories. Results: A total of 32 studies met the inclusion criteria, encompassing over 5,700 patients. The reported incidence of MCs varied from 17.6% to 61%. The predictive accuracy of the GAP score was inconsistent, with reported Area Under the Curve (AUC) values for predicting MCs ranging from a low of 0.53 to a moderately accurate 0.86. A meta-analysis found a significant association, with an odds ratio (OR) of 2.65 for MCs in severely disproportioned patients. However, the score's validity was diminished in specific cohorts, particularly for circumferential minimally invasive surgery (cMIS). Recent evidence demonstrates that the GAP score's predictive power is enhanced when combined with biological factors. The modified GAPB score, which incorporates Body Mass Index and Bone Mineral Density, demonstrated superior predictive accuracy (AUC=0.885 vs. 0.798). Machine learning models utilizing these multifactorial inputs achieved predictive accuracies over 73%. Conclusions: The predictive value of the GAP score with regard to MCs in ASD surgery is moderate but context-dependent. Its utility as a stand-alone predictor is limited, and the current evidence in the scientific literature supports the use of multifactorial predictive models, such as the GAPB score, and the application of machine learning to integrate patient-specific biological and biomechanical factors.
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