GAP score potential in predicting post-operative spinal mechanical complications: a systematic review of the literature

Quarto, Emanuele; Zanirato, Andrea; Pellegrini, Marco; Vaggi, S.; Vitali, Francesca; Bourret, Stéphane; Huec, Jean-Charles Le; Formica, Matteo

doi:10.1007/s00586-022-07386-6

Cited by 19 publications

(12 citation statements)

References 55 publications

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“…2 Since then, some authors have supported the original article results, [39][40][41][42] whereas others concluded that GAP was not a good predictor for mechanical complications. 35,[43][44][45][46][47] Hiyama et al 48 showed that GAP did not differ significantly between PJF− (without PJF) and PJF+ (with PJF) groups. This contradiction seemed probable because PJF cases were compared with the cases without PJF, but where other types of mechanical complications still existed.…”

Section: Discussionmentioning

confidence: 99%

“…In 2017, the GAP was proposed to predict mechanical complications with an accuracy of around 90% 2 . Since then, some authors have supported the original article results, 39–42 whereas others concluded that GAP was not a good predictor for mechanical complications 35,43–47 . Hiyama et al 48 showed that GAP did not differ significantly between PJF− (without PJF) and PJF+ (with PJF) groups.…”

Section: Discussionmentioning

confidence: 99%

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

Rasouligandomani

Arco

et al. 2023

Spine

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Study Design. Retrospective observational study. Objective. Biomechanical and geometrical descriptors are used to improve global alignment and proportion (GAP) prediction accuracy to detect proximal junctional failure (PJF). Summary of Background Data. 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. Patients and Methods. 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). Results. 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. Conclusion. 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Rasouligandomani

Arco

et al. 2023

Spine

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“…The GAP scores were calculated from obtained data according to the original article, 19 ranging from 0 to 13 points. The GAP score addresses potential in predicting spinal mechanical complications and postoperative outcomes 20 . A proportioned spinopelvic state is assessed and defined by a GAP score of 0–2, a moderately disproportioned state by a score of 3–6 and a severely disproportioned state by a score >7 21 .…”

Section: Methodsmentioning

confidence: 99%

Diabetes‐related Screw Loosening: The Distinction of Surgical Sites and the Relationship among Diabetes, Implant Stabilization and Clinical Outcomes

Wang,

et al. 2023

Orthopaedic Surgery

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ObjectivesDiabetes mellitus (DM) is correlated with poor clinical outcomes in spinal surgery. However, the effect of it on screw stabilization has not been investigated. The aim of this study was to evaluate the screw loosening rate and postoperative outcomes in diabetic patients and to identify potential risk factors associated with loosening.MethodsThis was a retrospective study. Two hundred and forty‐three patients who received cervical or lumbar internal fixation between 2015 and 2019 were enrolled. Screw loosening was assessed on radiography, and clinical outcomes were evaluated by the improvement of visual analogue scale (VAS), Oswestry disability index (ODI) or Japanese Orthopaedic Association (JOA) scores. The relationship of DM, screw loosening and clinical outcomes were analyzed with chi‐square tests and regression analyses.ResultsOne hundred and twenty‐two patients (50.2%) with diabetes were included in this study. Diabetes led to the increase of the rate of screw loosening in the lumbar spine, while the loosening rate did not vary significantly in the cervical spine. The occurrence of screw loosening in the lumbar spine was more likely to be associated with clinical outcomes for motor performance including walking and sitting. However, no significant effect on JOA and VAS scores in the cervical spine of screw loosening was found. Moreover, the history of DM affected the outcomes of the patients who underwent spinal surgery.ConclusionDM had an adverse effect on screw stabilization. The impaired improvement of clinical outcomes in diabetics after spinal surgery was related to screw loosening. In addition to the direct effects on operative wounds and neural function, the impact on the screws due to DM was also worth noting.

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“…Since it is crucial to predict possible mechanical complications because there is less possibility of suffering from a mechanical complication in the proportionally aligned subgroup of ASD patients, we set the scale positive weight parameter to the ratio of negative instances over positives to give more weight to positive cases [20]. We use the default parameters (i.e., 1) for column and row sample parameters.…”

Section: Target Model Cross-validation and Performance Metricsmentioning

confidence: 99%

Privacy-Preserving Machine Learning (PPML)Inference for Clinically Actionable Models: How to Monetize ESSG Modelling Efforts?

Balaban,

Magara,

Yilgor

et al. 2024

Preprint

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Purpose: Machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. As algorithms have constantly been evolving with the exponential increase of computing power and vastly generated data, privacy of algorithms as well as of data becomes extremely important due to regulations and IP rights. Therefore, it is vital to address privacy and security concerns of both data and model together with other performance metrics when commercializing machine learning models. Aim: Our motivation is to show that it is possible to deploy privacy-preserving machine learning inference methods to monetize the investment in ML models without disclosing them and patients’ data by providing a security analysis to define an appropriate query limit per user with ESSG’s adult spinal deformity dataset. Method: We implement a privacy-preserving tree-based machine learning inference and run two security scenarios (scenario A and scenario B) containing four parts with progressively increasing the number of synthetic data points, which are used to enhance the accuracy of the attacker’s substitute model. A target model is generated with particular operation site(s) in each scenario, and substitute models are built with nine-time threefold cross-validation using the XGBoost algorithm with the remaining sites’ data to assess the security of the target model. First, we create box plots of the test sets’ accuracy, sensitivity, precision, and F-score metrics to compare the substitute models’ performance with the target model. Second, we compare the gain values of the target and substitute models’ features. Third, we provide an in-depth analysis to check the inclusion of target model split points in substitute models with a heatmap. Finally, we compare the outputs of public and privacy-preserving models and report intermediate timing results. Results: The privacy-preserving XGBoost model results are identical to the original plaintext model in the aforementioned two scenarios in terms of prediction accuracy. The differences between performance metrics of best-performing substitute models and target models are 0.27, 0.18, 0.25, 0.26 for scenario A, and 0.04, 0, 0.04, and 0.03 for scenario B for accuracy, sensitivity, precision, and F-score, respectively. The differences between target model accuracy and the mean accuracy values of models in each scenario on the substitute models’ test dataset are 0.38 for scenario A and 0.14 for scenario B. Conclusion: Based on our findings, we conclude that machine learning models (i.e., our target models) may contribute to the advancement in the field of application where they are deployed. When the security of both the model and the user data is guaranteed, it is possible to monetize the modeling efforts of companies or organizations such as ESSG.

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GAP score potential in predicting post-operative spinal mechanical complications: a systematic review of the literature

Cited by 19 publications

References 55 publications

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

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

Diabetes‐related Screw Loosening: The Distinction of Surgical Sites and the Relationship among Diabetes, Implant Stabilization and Clinical Outcomes

Privacy-Preserving Machine Learning (PPML)Inference for Clinically Actionable Models: How to Monetize ESSG Modelling Efforts?

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