Proximal Junction Failure in Spine Surgery: Integrating Geometrical and Biomechanical Global Descriptors Improves GAP Score-Based Assessment

• Published in: Spine (Philadelphia, Pa. 1976) Vol. 48; no. 15; p. 1072
• Main Authors: Rasouligandomani, Morteza, Del Arco, Alex, Pellisé, Ferran, González Ballester, Miguel A, Galbusera, Fabio, Noailly, Jérôme
• Format: Journal Article
• Language: English
• Published: United States 01.08.2023
• ISSN: 1528-1159
• DOI: 10.1097/BRS.0000000000004630

Retrospective observational study. Biomechanical and geometrical descriptors are used to improve global alignment and proportion (GAP) prediction accuracy to detect proximal junctional failure (PJF). PJF is probably the most important complication after sagittal imbalance surgery. The GAP score has been introduced as an effective predictor for PJF, but it fails in certain situations. In this study, 112 patient records were gathered (57 PJF; 55 controls) with biomechanical and geometrical descriptors measured to stratify control and failure cases. Biplanar EOS radiographs were used to build 3-dimensional full-spine models and determine spinopelvic sagittal parameters. The bending moment (BM) was calculated as the upper body mass times, the effective distance to the body center of mass at the adjacent upper instrumented vertebra +1. Other geometrical descriptors such as full balance index (FBI), spino-sacral angle (SSA), C7 plumb line/sacrofemoral distance ratio (C7/SFD ratio), T1-pelvic angle (TPA), and cervical inclination angle (CIA) were also evaluated. The respective abilities of the GAP, FBI, SSA, C7/SFD, TPA, CIA, body weight, body mass index, and BM to discriminate PJF cases were analyzed through receiver operating characteristic curves and corresponding areas under the curve (AUC). GAP (AUC = 0.8816) and FBI (AUC = 0.8933) were able to discriminate PJF cases but the highest discrimination power (AUC = 0.9371) was achieved with BM at upper instrumented vertebra + 1. Parameter cutoff analyses provided quantitative thresholds to characterize the control and failure groups and led to improved PJF discrimination, with GAP and BM being the most important contributors. SSA (AUC = 0.2857), C7/SFD (AUC = 0.3143), TPA (AUC = 0.5714), CIA (AUC = 0.4571), body weight (AUC = 0.6319), and body mass index (AUC = 0.7716) did not adequately predict PJF. BM reflects the quantitative biomechanical effect of external loads and can improve GAP accuracy. Sagittal alignments and mechanical integrated scores could be used to better prognosticate the risk of PJF.