Spinal energy balance can predict post-operative spine alignment in Lenke 1 Adolescent Idiopathic Scoliosis

Spinal energy balance can predict post-operative spine alignment in Lenke 1 Adolescent Idiopathic Scoliosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spinal energy balance can predict post-operative spine alignment in Lenke 1 Adolescent Idiopathic Scoliosis Tristan Langlais, Jérôme Sales de Gauzy, Joe Rassi, Mathilde Bony, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7284962/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives The preoperative planning of adolescent idiopathic scoliosis (AIS) remains largely debated. We hypothesized that adopting a biomechanical energetic framework could provide valuable insights for exploring the impact of spinal arthrodesis. Using this approach, we conducted a comparative analysis to quantify discrepancies between in silico simulations derived from preoperative radiographs and the actual three-dimensional spinal alignment obtained from postoperative imaging. Methods Fifty-two consecutive patients with Lenke Type 1 AIS (mean age: 16 years; mean thoracic Cobb angle: 52°) who underwent posterior spinal fusion were included in the analysis. All patients had complete biplanar radiographs at three time points: preoperatively, postoperatively and at two-years follow-up. Discrepancies between in silico simulated surgery, calculated using preoperative radiographs and a biomechanical model, and actual clinical outcomes was quantified using two metrics: maximum coronal/sagittal deviations (MaxC/MaxS) from T1-L5, and a comprehensive predictability factor (a c and a s ) measuring cumulative 3D position discrepancies across 17 vertebral levels, normalized by total spinal length. Results Mean MaxC was 4.7 mm (SD=4.9) and MaxS was 5.7 mm (SD=3.8). Mean values of a c was 3.4% (SD=3.8) and a s was 4.1% (SD=2.6). Of the cohort, 44 patients (90%) showed very good or good agreement in the coronal in silico simulation and 43 patients (88%) in the sagittal in silico simulation. When the coronal and sagittal results were combined, 38 patients (78%) showed very good or good agreement. Conclusion Distribution of biomechanical energy obtained from pre-operative radiographs is reliable to simulate spine alignment after arthrodesis in a Lenke 1 AIS cohort. Idiopathic scoliosis Surgical planning Biplanar radiograph Energy approach Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Adolescent Idiopathic Scoliosis (AIS) leads to surgical treatment in 25% of cases when conservative orthopedic treatment proves ineffective [ 1 ]. Surgical treatment by arthrodesis aims to halt the progression of pathological curvature, to correct the three-dimensional spinal deformity and to achieve reliable long-term spinal fusion. Three-dimensional correction strives to address coronal alignment described by shoulders, waist and pelvis location and orientation. Restoration of sagittal balance between thoracic and lumbar curvatures, and reduction of axial vertebral rotation in the horizontal plane to minimize rib prominence, are also targeted. Patient comfort and preservation of spinal mobility are the main parameters for preoperative planning of location and extent of arthrodesis. Inadequate correction may lead to malalignment [ 2 ] provoking significant post-operative complications. Major reported complications [ 3 ] are adding-on of non-instrumented curve and proximal or distal junctional kyphosis which often lead to implant breakage or pull-out. Beyond the choice of instrumented levels, complications are strongly impacted by the amount of corrections applied [ 4 , 5 ]. It appears that preoperative planning still remains a matter of debate, and strategy for combining patient comfort with complications minimization remains an open question. To explore complex behavior of AIS, reductionist methodologies have been implemented and generally they were engineering tools - inspired for in silico modelling. Literature review reports some semi-analytical or multi-body discrete models [ 6 – 10 ] but most of them are using the finite element method to study etiopathogenesis [ 11 , 12 ] or prediction of clinical outcomes [ 13 ]. The ability of the finite element method to capture clinical reality shows limitations because of no-standardization of clinical setting with mandatory patient-dependance of surgical strategy. Anatomy may be partially accessible through clinical imaging on a macroscopic scale, but pathological tissue properties, boundary conditions - i.e. kinematic and loading conditions - and their temporal evolutions are generally inaccessible in clinical setting. In addition, pathological events and treatment impacts are involved in a reactive loop that is difficult to grasp and predict. To our knowledge, no strategy has yet been proposed for predicting AIS surgical outcomes, taking into account the unavoidable limitations of clinical reality. To overcome these limitations, we proposed a holistic approach which central assumption was that spine could be considered as a quasi-conservative thermodynamic system at the macroscopic scale with stationary equilibria associated with total energy minima [ 14 ]. Our top-down analysis considers succession of vertebral segments, as schematically depicted in Fig. 1 . In thermodynamic terms, internal energy accounts for strain energy of deformable structures, i.e. discs, ligaments, facet joints, and vertebral endplates, while external work accounts for the work of intersegmental ligaments, muscle actions, and gravity. Both internal and external forces derive from local energy potentials in the vicinity of successive quasi-static equilibria, and this results in global non-linear response segmented into piecewise linear model at each step where vertebral bodies kinematic and effective biophysical tensors are describing the macroscopic mechanical response of the segment. Kinematics and biophysics were identified using an inverse algorithm nourished by imaging, i.e. biplanar X-rays obtained in clinical routine. The methodology gives access to complete energy balance into the spine. Results showed that biophysical energy provided a relevant framework for characterizing spinal alignment in AIS patients [ 14 ]. However, the community is still debating a robust methodology for surgical planning. We hypothesized that the overall postoperative alignment of the spine with arthrodesis could be explored using the redistribution patterns of biophysical energies and predicted using patient's radiographic preoperative record. We conducted a comparative analysis, measuring discrepancies between in silico simulations and the actual three-dimensional spinal alignment reconstructed from postoperative imaging. Materials and methods Study design and patients’ selection This study was registered in the National Commission on Informatics and Freedoms (CNIL) database register (No 2239822). All participants provided informed consent prior to data collection and analysis, in accordance with institutional ethical standards. We conducted a retrospective analysis of a consecutive patient cohort treated between September 2017 and August 2020. Inclusion criteria were AIS classified as Lenke 1 [ 15 ], with a right thoracic curvature, undergoing posterior vertebral arthrodesis, no prior surgical treatment or halo gravity preparation, and a complete follow-up. Complete follow-up was defined as comprehensive clinical assessment and radiographic evaluation at three time points: preoperatively, three months postoperatively, and at final follow-up (with a minimum of two years). All imaging studies were digitally archived within the hospital Picture Archiving and Communication System (PACS). All included patients underwent biplanar low-dose coronal and sagittal radiographs (EOS®, EOS imaging, Paris, France) in the standard position [ 16 ] one month before surgery, at three months after surgery and at final follow-up. One hundred and sixty-two AIS underwent posterior fusion correction consecutively during the inclusion period, and 52 patients met the inclusion criteria (flow chart in Fig. 2 ). All posterior correction fusion was performed by a single operator using a standardized technique [ 17 ]. A posterior translation correction with concave thoracic sublaminar bands was performed. The upper end of the construct was fixed with a clamp [ 18 ] (transverse hook and multiaxial pedicle screw), and the lower end with multiaxial pedicle screws. Instrumentation was chrome-cobalt alloy rod of 5.5 mm diameter (Solera, Medtronic®, Dublin, Ireland). Demographic, clinical and radiographic characteristics are summarized in Table 1. Among the 52 patients, four patients (7.7%) required secondary surgical intervention during the follow-up period. The indications for reoperation were diverse: two patients underwent transverse connection removal due to persistent thoracic discomfort; one patient developed a surgical site infection at three weeks post-operatively, necessitating surgical drainage and a patient experienced bilateral rod fracture in the lumbar region at four years after the arthrodesis, requiring revision surgery with domino connection. The mean follow-up duration for the entire cohort was 3.0 years (range: 2.0–6.0 years). Energy-based method of surgical modelling and outcome assessment The first step was to segment spinal components to implement the pre-operative 3D wire frame model, that is center of vertebral endplate, i.e. points i and j in Fig. 1 b and centers of spinous process from T1 to L5 in the coronal and sagittal X-ray images (Image J®) (Fig. 3 ). Second, the energy method supported by the inverse algorithm was used to identify effective tensors describing stiffnesses, and muscles, ligaments, and gravity works at equilibrium [ 19 ] (SPINERGY software®) and to derive energy balance in frontal, sagittal and horizontal planes using pre-operative X-rays [ 14 ], see Fig. 4 . The third step concerned the in silico surgery simulation and was based on the knowledge of the instrumentation levels and their locations, the instrumentation sagittal shape, as well as the reduction on the upper and lower instrumented levels. Instrumentation modified effective tensors in arthrodesis zone with significant additional stiffness depending upon rod geometrical properties, material, and span and which were straightforwardly obtained from exact solution of slender beam quasi-static responses [ 20 ]. As shown in Fig. 1 c, rods, i.e. points 1 and 2 , were rigid-body attached to vertebral bodies i and j . Clinical outcome was established using 3-months post-operatively X-rays biplanar records. Objective quantification of discrepancies between in silico simulation and clinical outcome was based upon two criteria in coronal and sagittal planes. The first criterion was local and defined as greater distances MaxC and MaxS, in coronal and sagittal planes, between clinical image and in silico simulation. The second criterion called predictability factor a (%), was global while considering the seventeen vertebral levels, and defined as distances sum over k vertebral bodies scaled by the curvilinear length l as follows: a (%) = (1/ l ) ∑ k ( x k 2 model - x k 2 clinic ) 0.5 where x k gives the three-dimensional coordinate of vertebral bodies from T1 to L5. The percentage \(\:{\mathbf{a}}_{\text{c}}\) and \(\:{\mathbf{a}}_{\text{s}}\) were values of this factor in coronal and sagittal planes, respectively. Finally, agreement for both coronal and sagittal planes, was stratified as follows: very good agreement with \(\:{\mathbf{a}}_{\text{c}}\) or \(\:{\mathbf{a}}_{\text{s}}\) lower than 5% and MaxC or MaxS lower than 5 mm good agreement with \(\:{\mathbf{a}}_{\text{c}}\) or \(\:{\mathbf{a}}_{\text{s}}\) lower than 10% and MaxC or MaxS comprises between 5 mm and 10 mm or \(\:{\mathbf{a}}_{\text{c}}\) or \(\:{\mathbf{a}}_{\text{s}}\) comprises between 5% and 10% and MaxC,MaxS lower than 10 mm moderate agreement with \(\:{\mathbf{a}}_{\text{c}}\) or \(\:{\mathbf{a}}_{\text{s}}\) lower than 15% and MaxC or MaxS comprises between 10 mm and 15 mm or \(\:{\mathbf{a}}_{\text{c}}\) or \(\:{\mathbf{a}}_{\text{s}}\) comprises between 10% and 15% and MaxC or MaxS lower than 15 mm poor agreement with \(\:{\mathbf{a}}_{\text{c}}\) or \(\:{\mathbf{a}}_{\text{s}}\) greater than 15% and MaxC or MaxS greater than 15 mm Statistics Data normality and heteroskedasticity were assessed with the Shapiro-Wilk test and the Levene’s test. Continuous outcomes were compared with Kruskall-Wallis test, Mann-Whitney test or ANOVA test according data distribution. Non-continuous outcomes were compared with Fisher’s exact test according data distribution. If the null hypothesis of the test was rejected, post-hoc pairwise analyses were performed with Tukey’s HSD test. Alpha risk was set to 5% (α = 0.05). The results of the statistical tests are presented in Tables 2 to 5 . Statistical analysis was performed using EasyMedStat 3.42 (EasyMedStat®). Results Amongst 52 patient records examined, only three did not show numerical convergence. Mean values of MaxC were 4.7 mm (SD = 4.9 mm; CI 95% = 3.3–6.1 mm) and MaxS 5.7 mm (SD = 3.8 mm; CI 95% = 4.6–6.8 mm) in coronal plane and sagittal plane, respectively. Mean values of \(\:{\mathbf{a}}_{\text{c}}\) were 3.4% (SD = 3.8%; CI 95% = 2.3–4.5%) and \(\:{\mathbf{a}}_{\text{s}}\:\) were 4.1% (SD = 2.6; CI 95% = 3.3–4.8%) in coronal plane and in sagittal plane, respectively. There was no difference in assessed outcomes according to the lumbar and sagittal modifiers of the Lenke classification as shown in Table 2 . Agreement values are summarized in Table 3 . Of the cohort, 44 patients (90%) showed very good or good agreement in the coronal in silico simulation. Similarly, 43 patients (88%) showed very good or good agreement in the sagittal in silico simulation. When the coronal and sagittal results were combined, 24 patients (49%) showed very good agreement, 14 patients (29%) showed good agreement, 6 patients (12%) showed moderate agreement, and 5 patients (10%) showed poor agreement. For coronal in silico simulation (Table 4 ), there was no difference in agreement according to demographic, surgical, or radiographic parameters. Whereas for sagittal in silico simulation, mean preoperative T5-T12 kyphosis was 13.3 (SD 8.7) in the very good agreement group (n = 30) and 22.1 (SD 10.2) in the good agreement group (n = 13). Pairwise analyses revealed differences between these groups (p = 0.015). Figure 5 depicts the patterns associated with the four agreement groups. Discussion We first hypothesized that focusing on the energetic approach might be relevant for exploring and interpreting the impact of arthrodesis in operated patients and, beyond that, might provide indications for surgical planning. We carried out a clinical study on a cohort of 52 patients, and the results showed that in 80% of cases, our hypothesis was validated, since we obtained very good to good agreement between the predictive in silico simulation and the clinical results. To our knowledge, the proposed methodology is the first capable of predicting the clinical response of the operated scoliotic spine, based on routine preoperative clinical imaging and without the intrinsic ergonomic limitations of specialized engineering tools, often difficult to use in the clinical setting. Numerous finite element models of AIS and of increasing complexity have been proposed to corrected pathological deformations [ 2 , 6 , 7 , 9 , 13 , 21 – 23 ] while generally focusing on the post-operative coronal Cobb angle. This finite element approach is the main method found in the literature to investigate the complex mechanical processes underlying surgical scoliosis correction and post-operative spinal adaptation. Limitations of such methods may include high computational cost due to the size of the problems for whole musculoskeletal systems, which can involve non-linear responses, inter-patient variations in material properties, particularly for pathological tissues, and difficulties in identifying boundary conditions and kinematic loads in vivo . More recent approaches explored artificial intelligence capabilities software used for surgery alignment predictions limited to 75% accuracy [ 24 ] with significant limitation of biophysical and clinical non-explicability. Regression techniques are also explored in the literature with limited patient-specific prediction possibilities [ 25 ]. Depending on computing capabilities, the time required on a personal computer for the 3D preoperative digital wireframe model to converge was around one hour, while surgical simulation took only ten to thirty seconds. These times were compatible with the clinical context and constraints. In addition, the methodology assesses in near-real time the impact of instrumentation modification in terms of location, extent and shape and on patient alignment. The surgery predictive in silico simulation showed discrepancies in combined coronal and sagittal distances of up to 10% between predicted spine position and shape and actual post-operative outcomes, with maximum distances less than 10 mm in 80% of patients. We find these results promising, given all the uncertainties involved in patient management, including imaging, intra-operative modifications, instrumentation, and associated clinical procedures. Another interesting finding was that agreement outcomes were not impacted by magnitude of the pre-operative Cobb angle and the curvature types, i.e. lumbar and thoracic sagittal modifiers according to Lenke classification. Regarding the difference between coronal and sagittal alignment, particularly as a result of the T5-T12 thoracic kyphosis, the study found that modeling was sensitive to the orientation of the spinous process of the sacrum relative to the horizontal plane. Uncertainties intrinsic to anatomy increased in this region. Our methodology met a limitation revealed by the non- convergence of numerical process for three cases amongst fifty-two in the cohort. This reflects clinical reality constructed upon a consecutive series of patients involving discrepancies in imaging records and consecutive difficulties to segment wire frame anatomy and specifically spine-pelvis angulation. Furthermore, the model was evaluated in a Lenke 1 AIS cohort. While it met a frequent group encountered in clinics, it was also discriminant to evaluate the performance of our energy method in case of thoracic scoliosis where outcome of underlying levels is still controversial. Another limitation, transverse planes were indicated on the figures as illustrations of the of the spine pre- and post-operative 3D distribution but are not quantitatively detailed and will be further studied in future work. Future work will focus on reducing the sensitivity of the energy model to clinical data dispersions, such as local spine-pelvis orientations, as well as improving computation times and ergonomics adapted to clinical users and contexts. In addition, the clinical relevance will benefit from feedback from multicenter studies involving different AIS cohorts and clinical practices. The advantage of this type of simulation is that it allows to anticipate the overall post-operative alignment, as well as the behavior of the curvatures above and below the instrumentation. Ultimately, this approach should help clinicians in their choice of instrumented levels. Conclusion Distribution of biomechanical energy obtained from pre-operative radiographs is reliable to simulate patient-specific spine alignment post-operatively in a Lenke 1 AIS cohort. In addition, exploration of energies could enable modelling of the curve variation above and below the instrumented area for different surgery planning. Abbreviations AIS: Adolescent idiopathic scoliosis 3D : Three-dimensional SD: Standard deviation CI: Confident interval IQR: Interquartile Range Declarations Author Contribution T.L, P.S and P.A wrote the main manuscript. M.B, P.S and P.A prepares all figures. J.R, A.E collected the data. T.L and P.A analyzed the data. All authors reviewed the manuscript. Acknowledgement The authors are grateful to Yves Cotrel Foundation, Toulouse Tech Transfer and French government through doctoral scholarship. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Sanders JO, Newton PO, Browne RH et al (2014) Bracing for idiopathic scoliosis: how many patients require treatment to prevent one surgery? J Bone Joint Surg Am 96:649–653. https://doi.org/10.2106/JBJS.M.00290 Zhang H, Li T, Zhang G et al (2024) Postoperative adding-on phenomenon in Lenke 1A/B and 2A/B adolescent idiopathic scoliosis: risk factors and predictive index. 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Eur Spine J 28:1987–1997. https://doi.org/10.1007/s00586-019-06000-6 Tables Table 1: Cohort characteristics n represents the number Results are presented as mean (Standard deviation); range Cohort: n = 52 AIS included Age of surgery (years) 16 (3); 12 - 26 Body Mass Index (kg/cm 2 ) 21 (3); 14 - 28 Lenke classification (n) Lumbar modifier Thoracic sagittal profil modifier A: n=34; B: n=11, C: n=7 N: n=41; - : n=11 Upper instrumented vertebrae (n) T2: n=2; T3: n=30; T4: n=17; T5: n=3 Lower instrumented vertebrae (n) L1: n=16; L2: n=15; L3: n=16; L4: n=5 Number of instrumented levels (n) 11 (1); 9 - 14 Number of concave thoracic sublaminar bands (n) 3 (0.5); 3-5 Preoperative thoracic cobb angle (°) 52 (9); 33-78 3-months postoperative thoracic Cobb angle (°) 12 (5); 1-26 Last follow-up postoperative thoracic Cobb angle (°) 12 (5); 1-26 Preoperative T5-T12 sagittal angle (°) 16 (10); 2-43 3-months postoperative sagittal angle (°) 16 (9); 2-51 Last follow-up postoperative sagittal angle (°) 17 (7); 4-40 Re-operations Deep infection Rod breakage Transverse connection removal n=4 n=1 n=1 n=2 Follow-up (years) 3 (1); 2-6 Table 2: Outcome assessment according Lenke’s classification modifiers (%) MaxC (mm) (%) MaxS (mm) Lumbar Spine Modifier A 2.1 (IQR 1.9) 3.1 (IQR 2.7) 2.9 (IQR 6.4) 4.5 (IQR 4.5) B 2.6 (IQR 1.8) 4.1 (IQR 3.7) 3.3 (IQR 3.1) 3.8 (IQR 0.9) C 2.8 (IQR 11.4) 3.9 (IQR 1.2) 3 (IQR 1.1) 4.5 (IQR1.9) p-values 0.4 0.4 0.6 0.7 Thoracic Sagittal Profile Modifier Hypo 1.7 (IQR 2.5) 2.3 (IQR 2.6) 2.6 (IQR 1.4) 3.8 (IQR 1.7) Normal 2.5 (IQR 1.8) 3.4 (IQR 3.2) 3.5 (IQR 3.5) 4.7 (IQR 5) p-values 0.6 0.5 0.07 0.1 Results are presented as mean (IQR = Interquartile Range) Table 3: Degree of agreement between simulations and X-ray data 3 months post-operative. Values represent the number of patients in each group. Coronal (n=49) Sagittal (n=49) Combination of coronal and sagittal (n=49) Very Good agreement 36 30 24 Good agreement 8 13 14 Moderate agreement 2 4 6 Poor agreement 3 2 5 Table 4: Demographic, surgery and radiographic parameters according to the coronal agreement n represented the numbers of patient Very Good n=36 Good n=8 Moderate and poor n=5 p-value Body Mass Index (kg/cm 2 ) 20.6 (SD 3) 20.3 (SD 2.8) 21.9 (SD 3.4) 0.6 Lumbar Spine Modifier : n (%) A 25 (69.4%) 6 (75%) 1 (20%) 0.1 B 7 (19.4%) 2 (25%) 2 (40%) C 4 (11.1%) 0 2 (40%) Thoracic Sagittal Profile Modifier : n (%) Hypo 7 (19.4%) 1 (12.5%) 1 (20%) 0.9 Normal 29 (80.5%) 7 (87.5%) 4 (80%) Preoperative thoracic Cobb angle (°) 50.5 (SD 8.3) 54.5 (SD 13) 49.8 (SD 10.4) 0.6 Preoperative T5-T12 sagittal angle (°) 15.4 (SD 10.3) 23.4 (SD 7) 16.6 (SD 7) 0.08 Number of instrumented levels 11.3 (SD 1.4) 10 (SD 0.9) 10.4 (SD 0.9) 0.07 Correction of Coronal Thoracic Cobb angle (%) 76% 76% 72% 0.6 Table 5: Demographic, surgery and radiographic parameters according to the sagittal agreement n represented the numbers of patient Very Good n=30 Good n=13 Moderate and poor n=6 p-value Body Mass Index (kg/cm 2 ) 20.9 (SD 3.3) 20.4 (SD 2.6) 20.5 (SD 1.4) 0.9 Lumbar Spine Modifier : n (%) A 20 (66.7%) 10 (76.9%) 2 (33.3%) 0.1 B 6 (20%) 1 (7.7%) 4 (66.7%) C 4 (13.3%) 2 (15.4%) 0 Thoracic Sagittal Profile Modifier : n (%) Hypo 21 (77.8%) 12 (80%) 4 (80%) 0.9 Normal 6 (22%) 3 (20%) 1 (20%) Preoperative thoracic Cobb angle (°) 50.5 (SD 9.3) 53.6 (SD 10) 48.5 (7.4) 0.4 Preoperative T5-T12 sagittal angle (°) 13.3 (SD 8.7) 22.1 (SD 10.2) 22.8 (SD 7.5) 0.006 Number of instrumented levels 11.3 (SD 8.7) 10.7 (SD 1) 10 (SD 0.6) 0.06 Correction of T5-T12 Sagittal angle (%) 50% 26% 25% 0.07 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7284962","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513051248,"identity":"4affb0dc-1cd5-4833-aefd-b3de34d95055","order_by":0,"name":"Tristan Langlais","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2NsUrDUBSGTxDscqhroCXxEU4ICE6+SotDllQ7dpBwoRCX4lzxJfIIJ1zQJaWOd+iQIjhlCAjiEMTcWsTlth0F7zecczj8Hz+AxfIncYSefX0xEHgAqB+eUUCt8DamlXB7hzuUTfRnw1DsUy56cvpW36zw5OH2mT/GSZQ9LfKyBro2tnSHqcuPr+iuinE+IznKiqvLYA50LkwKOinwsURQ8YCReJRxfNZDaMjYgs605k+JfqvkDSURLSut0C5FuHkqkVTE7TwakIr3KIWTuos7iYGKQfZJBveqCoM5mZXOrPNST96l56lova6axO8u46CsJ2bld+F36JT1PERoC8vN8sVBaYvFYvlHfAE57Fy7l2EgTgAAAABJRU5ErkJggg==","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":true,"prefix":"","firstName":"Tristan","middleName":"","lastName":"Langlais","suffix":""},{"id":513051249,"identity":"6c330f7b-b387-48a6-815b-f27afd1c2fd5","order_by":1,"name":"Jérôme Sales de Gauzy","email":"","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Jérôme","middleName":"Sales","lastName":"de Gauzy","suffix":""},{"id":513051250,"identity":"a00df1c8-bc57-4653-afda-ebd4dbb57854","order_by":2,"name":"Joe Rassi","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Toulouse, Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Joe","middleName":"","lastName":"Rassi","suffix":""},{"id":513051251,"identity":"9229e98a-f7c6-42ef-acbc-fc05294d479b","order_by":3,"name":"Mathilde Bony","email":"","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Bony","suffix":""},{"id":513051252,"identity":"94dbbc63-2815-405d-9c26-a8e9755da82a","order_by":4,"name":"Baptiste Brun-Cottan","email":"","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Baptiste","middleName":"","lastName":"Brun-Cottan","suffix":""},{"id":513051253,"identity":"20dec1fd-2cb8-4046-9396-e8946d98cf5c","order_by":5,"name":"Amandine Eon","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Toulouse, Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Amandine","middleName":"","lastName":"Eon","suffix":""},{"id":513051254,"identity":"7358cabb-5e5d-483f-8c79-2224044acee6","order_by":6,"name":"Franck Accadbled","email":"","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Franck","middleName":"","lastName":"Accadbled","suffix":""},{"id":513051255,"identity":"75542467-5c30-4dfa-a0c5-c9f97a638f52","order_by":7,"name":"Pascal Swider","email":"","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Pascal","middleName":"","lastName":"Swider","suffix":""},{"id":513051257,"identity":"5f9294c3-21ff-4c4b-92d3-5f3a7f7f3127","order_by":8,"name":"Pauline Assemat","email":"","orcid":"","institution":"Institut de Mécanique des Fluides (IMFT), INPT, CNRS \u0026 Université de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Pauline","middleName":"","lastName":"Assemat","suffix":""}],"badges":[],"createdAt":"2025-08-03 18:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7284962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7284962/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91524992,"identity":"bac5d21c-9b25-4fab-98ec-5cf6fe367ddb","added_by":"auto","created_at":"2025-09-17 11:04:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131898,"visible":true,"origin":"","legend":"\u003cp\u003eFrom clinical imaging to energy-based 3D wire-frame model of scoliotic spine.\u0026nbsp; \u003cstrong\u003e(a)\u003c/strong\u003e X-ray imaging (EOS®) showing spine deformation in the frontal plane; \u003cstrong\u003e(b)\u003c/strong\u003e vertebral body kinematics \u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003e\u003cstrong\u003eu\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e \u003c/sub\u003edescribing spine correction in the presence of arthrodesis rods. \u003cstrong\u003e(c)\u003c/strong\u003e Updated 3D wire frame model based upon energy minimization and involving surgical instrumentation.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7284962/v1/72e612adb31e80bb2c7fce29.jpg"},{"id":91524997,"identity":"9189d24b-51ff-461d-ada4-ae34165ead3d","added_by":"auto","created_at":"2025-09-17 11:04:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63321,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7284962/v1/9b2c7add9165995e74a133cf.jpg"},{"id":91527646,"identity":"69bfea33-4b51-4c49-b88a-e5c63b6e967e","added_by":"auto","created_at":"2025-09-17 11:20:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87100,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the 3D wireframe geometry obtained by segmentation in image J. Centers of end plate are segmented in the coronal and sagittal planes in red, spinous processes are segmented in blue to reconstruct wire structure in the transverse plane.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7284962/v1/4d803d5221b0c060bab64a75.jpg"},{"id":91524993,"identity":"e48e7186-2f46-40c6-907a-58663645bf8b","added_by":"auto","created_at":"2025-09-17 11:04:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69746,"visible":true,"origin":"","legend":"\u003cp\u003eA comparison between numerical simulation of the preoperative case and the clinical RX using a wireframe representation in the 3 planes of space for one patient. Green curves represent the real geometry and blue curves represent the simulation. Axes are expressed in mm. MaxC corresponds to the maximum discrepancies in the coronal plane, MaxS in the sagittal plane, \u003cstrong\u003ea\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ec\u0026nbsp; \u003c/strong\u003e\u003c/sub\u003eis the predictability factor in the coronal plane, \u003cstrong\u003ea\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/sub\u003e in the sagittal plane.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7284962/v1/090ed63cfeb1e865c49c07f5.jpg"},{"id":91524999,"identity":"f67e4527-7c05-40e3-badd-09785e12bfc2","added_by":"auto","created_at":"2025-09-17 11:04:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181325,"visible":true,"origin":"","legend":"\u003cp\u003eA comparison between numerical simulation of surgery and post-operative surgery using a wireframe representation in the 3 planes of space. Black and grey curves represent the real geometry whereas blue and red represent the simulation. Grey and red correspond to the instrumented levels whereas black and blue the free vertebrae segments. Axes are expressed in mm. MaxC corresponds to the maximum discrepancies in the coronal plane, MaxS in the sagittal plane, \u003cstrong\u003ea\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ec\u0026nbsp; \u003c/strong\u003e\u003c/sub\u003eis the predictability factor in the coronal plane, \u003cstrong\u003ea\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/sub\u003e in the sagittal plane. (a) Example of a patient with very good agreement (b) Example of a patient with good agreement. (c) Example of a patient with moderate agreement (d) Example of a patient with poor agreement.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7284962/v1/bf952c5ae677d8faf4e61d46.jpg"},{"id":92641793,"identity":"d0051762-a304-472a-97bf-f410d7b49244","added_by":"auto","created_at":"2025-10-02 08:32:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1328548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7284962/v1/14e3cbbd-9a2d-419a-8830-1ead1ff90fa1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spinal energy balance can predict post-operative spine alignment in Lenke 1 Adolescent Idiopathic Scoliosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescent Idiopathic Scoliosis (AIS) leads to surgical treatment in 25% of cases when conservative orthopedic treatment proves ineffective [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Surgical treatment by arthrodesis aims to halt the progression of pathological curvature, to correct the three-dimensional spinal deformity and to achieve reliable long-term spinal fusion. Three-dimensional correction strives to address coronal alignment described by shoulders, waist and pelvis location and orientation. Restoration of sagittal balance between thoracic and lumbar curvatures, and reduction of axial vertebral rotation in the horizontal plane to minimize rib prominence, are also targeted. Patient comfort and preservation of spinal mobility are the main parameters for preoperative planning of location and extent of arthrodesis. Inadequate correction may lead to malalignment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] provoking significant post-operative complications. Major reported complications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] are adding-on of non-instrumented curve and proximal or distal junctional kyphosis which often lead to implant breakage or pull-out. Beyond the choice of instrumented levels, complications are strongly impacted by the amount of corrections applied [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It appears that preoperative planning still remains a matter of debate, and strategy for combining patient comfort with complications minimization remains an open question.\u003c/p\u003e\u003cp\u003eTo explore complex behavior of AIS, reductionist methodologies have been implemented and generally they were engineering tools - inspired for \u003cem\u003ein silico\u003c/em\u003e modelling. Literature review reports some semi-analytical or multi-body discrete models [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] but most of them are using the finite element method to study etiopathogenesis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] or prediction of clinical outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The ability of the finite element method to capture clinical reality shows limitations because of no-standardization of clinical setting with mandatory patient-dependance of surgical strategy. Anatomy may be partially accessible through clinical imaging on a macroscopic scale, but pathological tissue properties, boundary conditions - i.e. kinematic and loading conditions - and their temporal evolutions are generally inaccessible in clinical setting. In addition, pathological events and treatment impacts are involved in a reactive loop that is difficult to grasp and predict. To our knowledge, no strategy has yet been proposed for predicting AIS surgical outcomes, taking into account the unavoidable limitations of clinical reality. To overcome these limitations, we proposed a holistic approach which central assumption was that spine could be considered as a quasi-conservative thermodynamic system at the macroscopic scale with stationary equilibria associated with total energy minima [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our top-down analysis considers succession of vertebral segments, as schematically depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In thermodynamic terms, internal energy accounts for strain energy of deformable structures, i.e. discs, ligaments, facet joints, and vertebral endplates, while external work accounts for the work of intersegmental ligaments, muscle actions, and gravity. Both internal and external forces derive from local energy potentials in the vicinity of successive quasi-static equilibria, and this results in global non-linear response segmented into piecewise linear model at each step where vertebral bodies kinematic and effective biophysical tensors are describing the macroscopic mechanical response of the segment. Kinematics and biophysics were identified using an inverse algorithm nourished by imaging, i.e. biplanar X-rays obtained in clinical routine. The methodology gives access to complete energy balance into the spine. Results showed that biophysical energy provided a relevant framework for characterizing spinal alignment in AIS patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the community is still debating a robust methodology for surgical planning. We hypothesized that the overall postoperative alignment of the spine with arthrodesis could be explored using the redistribution patterns of biophysical energies and predicted using patient's radiographic preoperative record. We conducted a comparative analysis, measuring discrepancies between \u003cem\u003ein silico\u003c/em\u003e simulations and the actual three-dimensional spinal alignment reconstructed from postoperative imaging.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003eStudy design and patients\u0026rsquo; selection\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study was registered in the National Commission on Informatics and Freedoms (CNIL) database register (No 2239822). All participants provided informed consent prior to data collection and analysis, in accordance with institutional ethical standards. We conducted a retrospective analysis of a consecutive patient cohort treated between September 2017 and August 2020. Inclusion criteria were AIS classified as Lenke 1 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with a right thoracic curvature, undergoing posterior vertebral arthrodesis, no prior surgical treatment or halo gravity preparation, and a complete follow-up. Complete follow-up was defined as comprehensive clinical assessment and radiographic evaluation at three time points: preoperatively, three months postoperatively, and at final follow-up (with a minimum of two years). All imaging studies were digitally archived within the hospital Picture Archiving and Communication System (PACS). All included patients underwent biplanar low-dose coronal and sagittal radiographs (EOS\u0026reg;, EOS imaging, Paris, France) in the standard position [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] one month before surgery, at three months after surgery and at final follow-up.\u003c/p\u003e\u003cp\u003eOne hundred and sixty-two AIS underwent posterior fusion correction consecutively during the inclusion period, and 52 patients met the inclusion criteria (flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All posterior correction fusion was performed by a single operator using a standardized technique [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A posterior translation correction with concave thoracic sublaminar bands was performed. The upper end of the construct was fixed with a clamp [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (transverse hook and multiaxial pedicle screw), and the lower end with multiaxial pedicle screws. Instrumentation was chrome-cobalt alloy rod of 5.5 mm diameter (Solera, Medtronic\u0026reg;, Dublin, Ireland).\u003c/p\u003e\u003cp\u003eDemographic, clinical and radiographic characteristics are summarized in Table\u0026nbsp;1. Among the 52 patients, four patients (7.7%) required secondary surgical intervention during the follow-up period. The indications for reoperation were diverse: two patients underwent transverse connection removal due to persistent thoracic discomfort; one patient developed a surgical site infection at three weeks post-operatively, necessitating surgical drainage and a patient experienced bilateral rod fracture in the lumbar region at four years after the arthrodesis, requiring revision surgery with domino connection. The mean follow-up duration for the entire cohort was 3.0 years (range: 2.0\u0026ndash;6.0 years).\u003c/p\u003e\u003cp\u003e\u003cem\u003eEnergy-based method of surgical modelling and outcome assessment\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe first step was to segment spinal components to implement the pre-operative 3D wire frame model, that is center of vertebral endplate, i.e. points \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and centers of spinous process from T1 to L5 in the coronal and sagittal X-ray images (Image J\u0026reg;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Second, the energy method supported by the inverse algorithm was used to identify effective tensors describing stiffnesses, and muscles, ligaments, and gravity works at equilibrium [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (SPINERGY software\u0026reg;) and to derive energy balance in frontal, sagittal and horizontal planes using pre-operative X-rays [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The third step concerned the \u003cem\u003ein silico\u003c/em\u003e surgery simulation and was based on the knowledge of the instrumentation levels and their locations, the instrumentation sagittal shape, as well as the reduction on the upper and lower instrumented levels. Instrumentation modified effective tensors in arthrodesis zone with significant additional stiffness depending upon rod geometrical properties, material, and span and which were straightforwardly obtained from exact solution of slender beam quasi-static responses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, rods, i.e. points \u003cem\u003e1\u003c/em\u003e and \u003cem\u003e2\u003c/em\u003e, were rigid-body attached to vertebral bodies \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eClinical outcome was established using 3-months post-operatively X-rays biplanar records. Objective quantification of discrepancies between \u003cem\u003ein silico\u003c/em\u003e simulation and clinical outcome was based upon two criteria in coronal and sagittal planes. The first criterion was local and defined as greater distances MaxC and MaxS, in coronal and sagittal planes, between clinical image and \u003cem\u003ein silico\u003c/em\u003e simulation. The second criterion called predictability factor \u003cb\u003ea\u003c/b\u003e (%), was global while considering the seventeen vertebral levels, and defined as distances sum over \u003cem\u003ek\u003c/em\u003e vertebral bodies scaled by the curvilinear length \u003cem\u003el\u003c/em\u003e as follows:\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e (%) = (1/\u003cem\u003el\u003c/em\u003e) \u0026sum;\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003emodel\u003c/em\u003e\u003c/sub\u003e- \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003eclinic\u003c/em\u003e\u003c/sub\u003e)\u003csup\u003e0.5\u003c/sup\u003e where \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e gives the three-dimensional coordinate of vertebral bodies from T1 to L5. The percentage \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e were values of this factor in coronal and sagittal planes, respectively.\u003c/p\u003e\u003cp\u003eFinally, agreement for both coronal and sagittal planes, was stratified as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003every good agreement with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e lower than 5% and MaxC or MaxS lower than 5 mm\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003egood agreement with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e lower than 10% and MaxC or MaxS comprises between 5 mm and 10 mm or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e comprises between 5% and 10% and MaxC,MaxS lower than 10 mm\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003emoderate agreement with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e lower than 15% and MaxC or MaxS comprises between 10 mm and 15 mm or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e comprises between 10% and 15% and MaxC or MaxS lower than 15 mm\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003epoor agreement with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003egreater than 15% and MaxC or MaxS greater than 15 mm\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eData normality and heteroskedasticity were assessed with the Shapiro-Wilk test and the Levene\u0026rsquo;s test. Continuous outcomes were compared with Kruskall-Wallis test, Mann-Whitney test or ANOVA test according data distribution. Non-continuous outcomes were compared with Fisher\u0026rsquo;s exact test according data distribution. If the null hypothesis of the test was rejected, post-hoc pairwise analyses were performed with Tukey\u0026rsquo;s HSD test. Alpha risk was set to 5% (α\u0026thinsp;=\u0026thinsp;0.05). The results of the statistical tests are presented in Tables \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Statistical analysis was performed using EasyMedStat 3.42 (EasyMedStat\u0026reg;).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmongst 52 patient records examined, only three did not show numerical convergence. Mean values of MaxC were 4.7 mm (SD\u0026thinsp;=\u0026thinsp;4.9 mm; CI 95% = 3.3\u0026ndash;6.1 mm) and MaxS 5.7 mm (SD\u0026thinsp;=\u0026thinsp;3.8 mm; CI 95% = 4.6\u0026ndash;6.8 mm) in coronal plane and sagittal plane, respectively. Mean values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e were 3.4% (SD\u0026thinsp;=\u0026thinsp;3.8%; CI 95% = 2.3\u0026ndash;4.5%) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{a}}_{\\text{s}}\\:\\)\u003c/span\u003e\u003c/span\u003ewere 4.1% (SD\u0026thinsp;=\u0026thinsp;2.6; CI 95% = 3.3\u0026ndash;4.8%) in coronal plane and in sagittal plane, respectively. There was no difference in assessed outcomes according to the lumbar and sagittal modifiers of the Lenke classification as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAgreement values are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Of the cohort, 44 patients (90%) showed very good or good agreement in the coronal \u003cem\u003ein silico\u003c/em\u003e simulation. Similarly, 43 patients (88%) showed very good or good agreement in the sagittal \u003cem\u003ein silico\u003c/em\u003e simulation. When the coronal and sagittal results were combined, 24 patients (49%) showed very good agreement, 14 patients (29%) showed good agreement, 6 patients (12%) showed moderate agreement, and 5 patients (10%) showed poor agreement. For coronal \u003cem\u003ein silico\u003c/em\u003e simulation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), there was no difference in agreement according to demographic, surgical, or radiographic parameters. Whereas for sagittal \u003cem\u003ein silico\u003c/em\u003e simulation, mean preoperative T5-T12 kyphosis was 13.3 (SD 8.7) in the very good agreement group (n\u0026thinsp;=\u0026thinsp;30) and 22.1 (SD 10.2) in the good agreement group (n\u0026thinsp;=\u0026thinsp;13). Pairwise analyses revealed differences between these groups (p\u0026thinsp;=\u0026thinsp;0.015). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the patterns associated with the four agreement groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe first hypothesized that focusing on the energetic approach might be relevant for exploring and interpreting the impact of arthrodesis in operated patients and, beyond that, might provide indications for surgical planning. We carried out a clinical study on a cohort of 52 patients, and the results showed that in 80% of cases, our hypothesis was validated, since we obtained very good to good agreement between the predictive \u003cem\u003ein silico\u003c/em\u003e simulation and the clinical results. To our knowledge, the proposed methodology is the first capable of predicting the clinical response of the operated scoliotic spine, based on routine preoperative clinical imaging and without the intrinsic ergonomic limitations of specialized engineering tools, often difficult to use in the clinical setting.\u003c/p\u003e\u003cp\u003eNumerous finite element models of AIS and of increasing complexity have been proposed to corrected pathological deformations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] while generally focusing on the post-operative coronal Cobb angle. This finite element approach is the main method found in the literature to investigate the complex mechanical processes underlying surgical scoliosis correction and post-operative spinal adaptation. Limitations of such methods may include high computational cost due to the size of the problems for whole musculoskeletal systems, which can involve non-linear responses, inter-patient variations in material properties, particularly for pathological tissues, and difficulties in identifying boundary conditions and kinematic loads \u003cem\u003ein vivo\u003c/em\u003e. More recent approaches explored artificial intelligence capabilities software used for surgery alignment predictions limited to 75% accuracy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] with significant limitation of biophysical and clinical non-explicability. Regression techniques are also explored in the literature with limited patient-specific prediction possibilities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDepending on computing capabilities, the time required on a personal computer for the 3D preoperative digital wireframe model to converge was around one hour, while surgical simulation took only ten to thirty seconds. These times were compatible with the clinical context and constraints. In addition, the methodology assesses in near-real time the impact of instrumentation modification in terms of location, extent and shape and on patient alignment. The surgery predictive \u003cem\u003ein silico\u003c/em\u003e simulation showed discrepancies in combined coronal and sagittal distances of up to 10% between predicted spine position and shape and actual post-operative outcomes, with maximum distances less than 10 mm in 80% of patients. We find these results promising, given all the uncertainties involved in patient management, including imaging, intra-operative modifications, instrumentation, and associated clinical procedures. Another interesting finding was that agreement outcomes were not impacted by magnitude of the pre-operative Cobb angle and the curvature types, i.e. lumbar and thoracic sagittal modifiers according to Lenke classification. Regarding the difference between coronal and sagittal alignment, particularly as a result of the T5-T12 thoracic kyphosis, the study found that modeling was sensitive to the orientation of the spinous process of the sacrum relative to the horizontal plane. Uncertainties intrinsic to anatomy increased in this region.\u003c/p\u003e\u003cp\u003eOur methodology met a limitation revealed by the non- convergence of numerical process for three cases amongst fifty-two in the cohort. This reflects clinical reality constructed upon a consecutive series of patients involving discrepancies in imaging records and consecutive difficulties to segment wire frame anatomy and specifically spine-pelvis angulation. Furthermore, the model was evaluated in a Lenke 1 AIS cohort. While it met a frequent group encountered in clinics, it was also discriminant to evaluate the performance of our energy method in case of thoracic scoliosis where outcome of underlying levels is still controversial. Another limitation, transverse planes were indicated on the figures as illustrations of the of the spine pre- and post-operative 3D distribution but are not quantitatively detailed and will be further studied in future work.\u003c/p\u003e\u003cp\u003eFuture work will focus on reducing the sensitivity of the energy model to clinical data dispersions, such as local spine-pelvis orientations, as well as improving computation times and ergonomics adapted to clinical users and contexts. In addition, the clinical relevance will benefit from feedback from multicenter studies involving different AIS cohorts and clinical practices. The advantage of this type of simulation is that it allows to anticipate the overall post-operative alignment, as well as the behavior of the curvatures above and below the instrumentation. Ultimately, this approach should help clinicians in their choice of instrumented levels.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDistribution of biomechanical energy obtained from pre-operative radiographs is reliable to simulate patient-specific spine alignment post-operatively in a Lenke 1 AIS cohort. In addition, exploration of energies could enable modelling of the curve variation above and below the instrumented area for different surgery planning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAIS:\u0026nbsp;\u003c/strong\u003eAdolescent idiopathic scoliosis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3D\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Three-dimensional\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD:\u0026nbsp;\u003c/strong\u003eStandard deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u0026nbsp;\u003c/strong\u003e Confident interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR:\u003c/strong\u003e Interquartile Range\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.L, P.S and P.A wrote the main manuscript. M.B, P.S and P.A prepares all figures. J.R, A.E collected the data. T.L and P.A analyzed the data. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful to Yves Cotrel Foundation, Toulouse Tech Transfer and French government through doctoral scholarship.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSanders JO, Newton PO, Browne RH et al (2014) Bracing for idiopathic scoliosis: how many patients require treatment to prevent one surgery? 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Eur Spine J 28:1987\u0026ndash;1997. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00586-019-06000-6\u003c/span\u003e\u003cspan address=\"10.1007/s00586-019-06000-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Cohort characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003en represents the number\u003c/p\u003e\n\u003cp\u003eResults are presented as mean (Standard deviation); range\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eCohort: n = 52 AIS included\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eAge of surgery (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e16 (3); 12 - 26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eBody Mass Index (kg/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e21 (3); 14 - 28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eLenke classification (n)\u003c/p\u003e\n \u003cp\u003eLumbar modifier\u003c/p\u003e\n \u003cp\u003eThoracic sagittal profil modifier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA: n=34; B: n=11, C: n=7\u003c/p\u003e\n \u003cp\u003eN: n=41; - : n=11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eUpper instrumented vertebrae (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eT2: n=2; T3: n=30; T4: n=17; T5: n=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eLower instrumented vertebrae (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eL1: n=16; L2: n=15; L3: n=16; L4: n=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eNumber of instrumented levels (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e11 (1); 9 - 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eNumber of concave thoracic sublaminar bands (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e3 (0.5); 3-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003ePreoperative thoracic cobb angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e52 (9); 33-78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003e3-months postoperative thoracic Cobb angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e12 (5); 1-26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eLast follow-up postoperative thoracic Cobb angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e12 (5); 1-26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003ePreoperative T5-T12 sagittal angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e16 (10); 2-43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003e3-months postoperative sagittal angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e16 (9); 2-51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eLast follow-up postoperative sagittal angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e17 (7); 4-40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eRe-operations\u003c/p\u003e\n \u003cp\u003eDeep infection\u003c/p\u003e\n \u003cp\u003eRod breakage\u003c/p\u003e\n \u003cp\u003eTransverse connection removal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003en=4\u003c/p\u003e\n \u003cp\u003en=1\u003c/p\u003e\n \u003cp\u003en=1\u003c/p\u003e\n \u003cp\u003en=2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eFollow-up (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e3 (1); 2-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Outcome assessment according Lenke\u0026rsquo;s classification modifiers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cimg width=\"13\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaxC (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cimg width=\"13\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaxS (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003eLumbar Spine Modifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.1 (IQR 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.1 (IQR 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.9 (IQR 6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.5 (IQR 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eB\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.6 (IQR 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.1 (IQR 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.3 (IQR 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.8 (IQR 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eC\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.8 (IQR 11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.9 (IQR 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3 (IQR 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.5 (IQR1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-values\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003eThoracic Sagittal Profile Modifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHypo\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.7 (IQR 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.3 (IQR 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.6 (IQR 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.8 (IQR 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eNormal\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.5 (IQR 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.4 (IQR 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.5 (IQR 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.7 (IQR 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-values\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults are presented as mean (IQR = Interquartile Range)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDegree of agreement between simulations and X-ray data 3 months post-operative.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValues represent the number of patients in each group.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eCoronal (n=49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSagittal (n=49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCombination of coronal and sagittal (n=49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eVery Good agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eGood agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eModerate agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003ePoor agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDemographic, surgery and radiographic parameters according to the coronal agreement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003en represented the numbers of patient\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eVery Good\u003c/p\u003e\n \u003cp\u003en=36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003en=8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eModerate and poor\u003c/p\u003e\n \u003cp\u003en=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eBody Mass Index (kg/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20.6 (SD 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20.3 (SD 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e21.9 (SD 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 671px;\"\u003e\n \u003cp\u003eLumbar Spine Modifier : n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eA\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eB\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eC\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 671px;\"\u003e\n \u003cp\u003eThoracic Sagittal Profile Modifier : n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHypo\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eNormal\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e29 (80.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003ePreoperative thoracic Cobb angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e50.5 (SD 8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e54.5 (SD 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e49.8 (SD 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003ePreoperative T5-T12 sagittal angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15.4 (SD 10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23.4 (SD 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e16.6 (SD 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eNumber of instrumented levels\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11.3 (SD 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10 (SD 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e10.4 (SD 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eCorrection of Coronal Thoracic Cobb angle (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Demographic, surgery and radiographic parameters according to the sagittal agreement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003en represented the numbers of patient\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVery Good\u003c/p\u003e\n \u003cp\u003en=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003en=13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eModerate and poor\u003c/p\u003e\n \u003cp\u003en=6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eBody Mass Index (kg/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e20.9 (SD 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20.4 (SD 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e20.5 (SD 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003eLumbar Spine Modifier : n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eA\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e20 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eB\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e6 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eC\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e4 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003eThoracic Sagittal Profile Modifier : n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHypo\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e21 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eNormal\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e6 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003ePreoperative thoracic Cobb angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e50.5 (SD 9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e53.6 (SD 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e48.5 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003ePreoperative T5-T12 sagittal angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e13.3 (SD 8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22.1 (SD 10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e22.8 (SD 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eNumber of instrumented levels\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e11.3 (SD 8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10.7 (SD 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e10 (SD 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eCorrection of T5-T12 Sagittal angle (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic scoliosis, Surgical planning, Biplanar radiograph, Energy approach","lastPublishedDoi":"10.21203/rs.3.rs-7284962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7284962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preoperative planning of adolescent idiopathic scoliosis (AIS) remains largely debated. We hypothesized that adopting a biomechanical energetic framework could provide valuable insights for exploring the impact of spinal arthrodesis. Using this approach, we conducted a comparative analysis to quantify discrepancies between \u003cem\u003ein silico\u003c/em\u003e simulations derived from preoperative radiographs and the actual three-dimensional spinal alignment obtained from postoperative imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-two consecutive patients with Lenke Type 1 AIS (mean age: 16 years; mean thoracic Cobb angle: 52°) who underwent posterior spinal fusion were included in the analysis. All patients had complete biplanar radiographs at three time points: preoperatively, postoperatively and at two-years follow-up. Discrepancies between \u003cem\u003ein silico\u003c/em\u003e simulated surgery, calculated using preoperative radiographs and a biomechanical model, and actual clinical outcomes was quantified using two metrics: maximum coronal/sagittal deviations (MaxC/MaxS) from T1-L5, and a comprehensive predictability factor (a\u003csub\u003ec\u003c/sub\u003e and a\u003csub\u003es\u003c/sub\u003e) measuring cumulative 3D position discrepancies across 17 vertebral levels, normalized by total spinal length.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean MaxC was 4.7 mm (SD=4.9) and MaxS was 5.7 mm (SD=3.8). Mean values of a\u003csub\u003ec\u003c/sub\u003e was 3.4% (SD=3.8) and a\u003csub\u003es\u003c/sub\u003e was 4.1% (SD=2.6). Of the cohort, 44 patients (90%) showed very good or good agreement in the coronal \u003cem\u003ein silico\u003c/em\u003e simulation and 43 patients (88%) in the sagittal \u003cem\u003ein silico\u003c/em\u003esimulation. When the coronal and sagittal results were combined, 38 patients (78%) showed very good or good agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of biomechanical energy obtained from pre-operative radiographs is reliable to simulate spine alignment after arthrodesis in a Lenke 1 AIS cohort.\u003c/p\u003e","manuscriptTitle":"Spinal energy balance can predict post-operative spine alignment in Lenke 1 Adolescent Idiopathic Scoliosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 11:04:45","doi":"10.21203/rs.3.rs-7284962/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4386911b-0b14-49bc-bc1f-378f2914262f","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-02T08:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 11:04:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7284962","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7284962","identity":"rs-7284962","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}