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Validating a Patient Specific Normal Sagittal Spinal Contour Prediction Model – How to Know What “Normal”
Sagittal Spinal Alignment is for Each Patient
Peter O. Newton1,2, Fredrick G. Reighard1, Tracey Bastrom1, Joshua D. Doan1, Christine Farnsworth1
1
Rady Children’s Hospital San Diego, San Diego, CA, 2University of California, San Diego
Disclosures: Peter O. Newton (1; DePuy Synthes Spine. 2; DePuy Synthes Spine. 3B; DePuy Synthes Spine, Ethicon Endosurgery, K2M, Cubist. 4;
ElectroCore. 5; DePuy Synthes Spine,via Setting Scoliosis Straight Foundation, EOS Imaging, K2M, via Setting Scoliosis Straight Foundation,
Orthopediatrics Institutional support. 7; Theime Publishing),
Fredrick G. Reighard (None), Tracey Bastrom (None), Josh Doan (None), Christine Farnsworth (None)
INTRODUCTION:
Understanding what “normal” sagittal spinal alignment is for an individual is an important preoperative determinant for surgical interventions designed to
restore normal spinal alignment. The aim of this study was to optimize our current patient specific model of the normal sagittal alignment of the spine as a
function of the patient’s pelvic incidence (PI). Seventy-five “normal” patients’ spine radiographs were used to generate the model.1 An additional 75
patients were added to validate the model and determine its accuracy. Then, the additional 75 patients were incorporated into the prediction model to
improve its precision. The model addresses the issues of the wide variation in sagittal alignment and balance to permit comparison of the predicted normal
spine to deformed and surgically corrected spines. PI is a measure of pelvic shape that may be measured from lateral imaging of all spine patients, yet
remains a fixed anatomical measurement as it does not change with patient positioning.2 The purpose of this study was to develop and validate a predictive
algorithm to generate normal patient specific sagittal spinal alignment based primarily on a given patient’s pelvic incidence.
METHODS:
This IRB approved project enlisted 150 patients that were referred to a pediatric scoliosis clinic and were found to have no spinal disease. The holdout
method for cross validation was utilized to establish the training set (n=75 patients), from which a regression equation was developed. The testing set (n=75
patients) was used to validate the generalizability and accuracy of the equation. All 150 patients had radiographic imaging obtained using the EOS system
(EOS Imaging, Paris, France). EOS utilizes two perpendicular positioned collimated fan-beam sources and linear detectors to acquire simultaneous
orthogonal coronal and sagittal digital radiographs of a standing patient. Each coronal image was evaluated and measured with less than ten degrees coronal
Cobb angle or spinal curvature; therefore the diagnosis of scoliosis was eliminated.
3D reconstructions from each patient’s orthogonal images were performed by an experienced technician using sterEOS software (EOS Imaging, Paris,
France). First, pelvic landmarks (center of the femoral heads and sacral slope) were identified on both the coronal and sagittal radiographs. Next, a digital
spline was constructed to encase all twelve thoracic and five lumbar vertebra on the coronal and sagittal images. SterEOS then generated a “best fit”
template of the spine overlaid onto the spline to represent the patient’s specific vertebral anatomy. Stepwise adjustments were then performed to match the
spine template to the spine anatomy on the radiograph as accurately as possible.
Model Algorithm Generation and Validation
The model algorithm used in this study was developed from 75 patients (training/development set) spinal measurements generated using SterEOS
including: PI, T1-Sacrum offset (T1S), inflection point height (IPY), and inflection point depth (IPX). These data points were then used in multiple linear
regressions to correlate with the orientation and posterior-anterior positioning of each vertebra. Thirty-four regression equations were generated from this to
determine the Normal Contour Function (NCF). Non-linear regression was used to match the NCF that best fit the patient’s physical spinal contours using
methods identical to those used in a previous study.2 To validate the algorithm developed from the first group of 75 EOS imaging exams, another 75 patients
(testing/validation set) with no known spinal abnormalities were introduced. The regression equation developed in the original training set was utilized to
calculate predicted values of vertebra position. The mean absolute error (MAE) in predicted values minus actual position values for each vertebra was
calculated. The MAE for both the training and testing sets were calculated and compared utilizing multivariate ANOVA. Linear regressions were
performed to determine how much of the variance in the actual values is explained by the predicted values. SPSS v. 12 was utilized for analyses and alpha
was set at p<0.05 to declare significance.
RESULTS:
The development and validation cohorts were similar: age 14.5±2.6 years vs 14.4±1.9 years, PI 46.7±10.2° vs 45.7±10.4°, thoracic kyphosis 39.8±8.8° vs
42.2±11.3°, lumbar lordosis 37.1±11.2° vs 42.6±11.3°. The predictive algorithm generated vertebral body positions with MAEs that ranged from 1.3mm at
L5 to 4.1mm at T8 in the cohort in which the equation was validated on. There was no significant difference in MAE per level between the two cohorts
(Table I, p>0.10). The R squared values demonstrating the variance in actual values that is explained by the predicted values are shown in Table 2. The
range of R squared values for vertebral levels T1-L4 in the development cohort was 0.84-0.99. The range of R squared values for vertebral levels T1-L4 in
the validation cohort was 0.75-0.99. In both cohorts, the R squared values for L5 were much lower than the other vertebral levels tested (0.57 development,
0.59 validation).
DISCUSSION:
The algorithm performed well, with less than 5mm of error in vertebral body position at the apex of kyphosis. This model will help surgeons evaluate the
sagittal deformity of a given patient (related to the PI) relative to predicted normal for that individual (quantifying the relative magnitude of sagittal
deformity), as well as assess the sagittal plane correction following surgical treatment. Defining the goal (patient specific normal) and how well the goal was
met are critical in improving sagittal plane spinal correction strategies.
SIGNIFICANCE:
Restoring normal sagittal alignment is a goal of spinal deformity corrective surgery so knowing what is “normal” sagittal alignment for each patient is
meaningful. It is plausible that this model could be used to assess the amount of deformity pre-operatively in scoliotic patients as well as to quantify sagittal
correction post-operatively.
REFERENCES:
1. Legaye J, et al. Eur Spine J. 1998,7(2):99-103. 2. Cidambi KC, et al. Spine Deformity. 2015.3(3):228–232.
ACKNOWLEDGEMENTS:
Research support is gratefully acknowledged from the Rady Children’s Foundation Assaraf Family Research Fund and from a grant from Rady Children’s
Specialists of San Diego - Surgical Department.
Table I. MAE values per vertebral level (Predicted vs Actual) in millimeters.
Cohort
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
L1
L2
L3
L4
L5
Development 2.9
1.6
1.3
2.0
2.7
3.2
3.4
3.4
3.7
2.8
2.4
2.1
2.6
3.3
3.5
3.0
1.5
Validation
3.7
2.1
1.4
2.0
2.9
3.4
3.9
4.1
4.0
3.8
3.4
2.7
2.7
3.3
3.8
3.1
1.3
p
0.10
0.18
0.90
0.97
0.57
0.59
0.40
0.24
0.20
0.12
0.10
0.29
0.83
0.76 0.71
0.75 0.55
Table II. R square of model fit per vertebral level values.
Cohort
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
L1
L2
L3
L4
L5
Development 0.97
0.99
0.99
0.99
0.98
0.96
0.95
0.95
0.95
0.96
0.97
0.97
0.96
0.92 0.90
0.84 0.57
Validation
0.95
0.98
0.99
0.99
0.99
0.97
0.96
0.95
0.95
0.95
0.95
0.94
0.92
0.87 0.81
0.75 0.59
ORS 2016 Annual Meeting Paper No. 0083