Changes of Fixed Anatomical Spinopelvic Parameter in Patients with Lumbosacral Transitional Vertebrae: A Matched Pair Analysis

Functional spinopelvic parameters are crucial for describing spinal alignment (SA), but this is susceptible to variation. Anatomically fixed pelvic shape is defined by the parameters pelvic radius (PR), pelvic incidence (PI), and sacral table angle (STA). In patients with lumbosacral transitional vertebrae (LSTV), the spinopelvic alignment may be altered by changes of these parameters and influences of SA. There have been no reports studying the relation between LSTV, four (4 LV) and six (6 LV) lumbar vertebrae, and fixed anatomical spinopelvic parameters. A retrospective analysis of 819 abdomen–pelvis CT scans was performed, identifying 53 patients with LSTV. In a matched-pair analysis, we analyzed the influence of LSTV and the subgroups 4 LV (n = 9) and 6 LV (n = 11) on PR, PI, and STA. LSTV were classified according to Castellvi classification. In patients with 6 LV, measurement points at the superior endplates of S1 and S2 were compared. The prevalence of LSTV was 6.5% (53/819), 6 LV was 1.3% (11/819), and 4 LV was 1.1% (9/819) in our study population. PI significantly increased (p < 0.001), STA significantly decreased (p < 0.001), and PR (p = 0.051) did not differ significantly in the LSTV group (n = 53). Similar findings were observed in the 4 LV subgroup, with an increase in PI (p < 0.021), decrease in STA (p < 0.011), and no significant difference in PR (p < 0.678). The same results were obtained in the 6 LV subgroup at measuring point S2 (true S1) PI (p = 0.010), STA (p = 0.004), and PR (p = 0.859), but not at measuring point S1 (true L6). Patients with LSTV, 4 LV, and 6 LV showed significant differences in PI and STA compared to the matched control group. PR showed no significant differences. The altered spinopelvic anatomy in LSTV patients need to be reflected in preoperative planning rebalancing the sagittal SA.

Change in pelvic incidence between the supine and standing positions in patients with bilateral sacroiliac joint vacuum signs

OBJECTIVEPelvic incidence (PI) is a commonly utilized spinopelvic parameter in the evaluation and treatment of patients with spinal deformity and is believed to be a fixed parameter. However, a fixed PI assumes that there is no motion across the sacroiliac (SI) joint, which has been disputed in recent literature. The objective of this study was to determine if patients with SI joint vacuum sign have a change in PI between the supine and standing positions.METHODSA retrospective chart review identified patients with a standing radiograph, supine radiograph, and CT scan encompassing the SI joints within a 6-month period. Patients were grouped according to their SI joints having either no vacuum sign, unilateral vacuum sign, or bilateral vacuum sign. PI was measured by two independent reviewers.RESULTSSeventy-three patients were identified with an average age of 66 years and a BMI of 30 kg/m2. Patients with bilateral SI joint vacuum sign (n = 27) had an average absolute change in PI of 7.2° (p < 0.0001) between the standing and supine positions compared to patients with unilateral SI joint vacuum sign (n = 20) who had a change of 5.2° (p = 0.0008), and patients without an SI joint vacuum sign (n = 26) who experienced a change of 4.1° (p = 0.74). ANOVA with post hoc Tukey test showed a statistically significant difference in the change in PI between patients with the bilateral SI joint vacuum sign and those without an SI joint vacuum sign (p = 0.023). The intraclass correlation coefficient between the two reviewers was 0.97 for standing PI and 0.96 for supine PI (p < 0.0001).CONCLUSIONSPatients with bilateral SI joint vacuum signs had a change in PI between the standing and supine positions, suggesting there may be increasing motion across the SI joint with significant joint degeneration.

Spinopelvic Parameters Do Not Predict the Sagittal Orientation of the Acetabulum

Background: The orientation of the acetabulum has a fundamental role in impingement and instability of the hip, and the spinopelvic parameters are thought to predict the sagittal orientation of the acetabulum (SOA). However, similar to the acetabular version (axial orientation) and inclination (coronal orientation), the cephalic or caudal orientation of the acetabulum in the sagittal plane, or SOA, may primarily be an intrinsic feature of the acetabulum itself. Purpose: To determine whether the spinopelvic parameters predict the sagittal orientation of the acetabulum in individuals without lumbar deformity. Study Design: Cross-sectional study; Level of evidence, 4. Methods: A retrospective analysis was performed in 89 patients (94 hips; 62 female, 27 male; mean ± SD age, 45.9 ± 15.4 years) without lumbosacral deformity who underwent magnetic resonance arthrogram (MRA) for assessment of hip pain. The SOA was determined in the sagittal cut MRA. A line was drawn at the distal limit of the anterior and posterior acetabular horns longitudinally to the transverse ligament, and the angle between this line and the axial plane represented the SOA. The sacral slope, pelvic incidence, and spinopelvic tilt were determined using a 3-dimensional cursor and the axial, sagittal, and coronal cuts. All MRA studies were performed with the patient in the supine position. Results: The SOA had a mean ± SD cephalic orientation of 18° ± 6.6°. No significant correlation was observed between the SOA and the sacral slope ( r = –0.03; P = .77). A weak correlation was observed between the SOA and the pelvic incidence ( r = 0.22; P = .03) and between the SOA and the spinopelvic tilt ( r = 0.41; P < .01). Conclusion: The SOA cannot be presumed based on the spinopelvic parameter. Similar to the well-known parameters to assess the axial and coronal orientation of the acetabulum, the assessment of the SOA demands acetabular-specific parameters. Additional studies are necessary to assess the SOA in asymptomatic hips, including disparities between genders. Clinically significant values for abnormal SOA of the acetabulum remain to be defined.

Is the Goutallier grade of multifidus fat infiltration associated with adjacent-segment degeneration after lumbar spinal fusion?

OBJECTIVEThe aim of this study was to investigate whether fat infiltration of the lumbar multifidus (LM) muscle affects revision surgery rates for adjacent-segment degeneration (ASD) after L4–5 transforaminal lumbar interbody fusion (TLIF) for degenerative spondylolisthesis.METHODSA total of 178 patients undergoing single-level L4–5 TLIF for spondylolisthesis (2006 to 2016) were retrospectively analyzed. Inclusion criteria were a minimum 2-year follow-up, preoperative MR images and radiographs, and single-level L4–5 TLIF for degenerative spondylolisthesis. Twenty-three patients underwent revision surgery for ASD during the follow-up. Another 23 patients without ASD were matched with the patients with ASD. Demographic data, Roussouly curvature type, and spinopelvic parameter data were collected. The fat infiltration of the LM muscle (L3, L4, and L5) was evaluated on preoperative MRI using the Goutallier classification system.RESULTSA total of 46 patients were evaluated. There were no differences in age, sex, BMI, or spinopelvic parameters with regard to patients with and those without ASD (p > 0.05). Fat infiltration of the LM was significantly greater in the patients with ASD than in those without ASD (p = 0.029). Fat infiltration was most significant at L3 in patients with ASD than in patients without ASD (p = 0.017). At L4 and L5, there was an increasing trend of fat infiltration in the patients with ASD than in those without ASD, but the difference was not statistically significant (p = 0.354 for L4 and p = 0.077 for L5).CONCLUSIONSFat infiltration of the LM may be associated with ASD after L4–5 TLIF for spondylolisthesis. Fat infiltration at L3 may also be associated with ASD at L3–4 after L4–5 TLIF.

Spinopelvic Anatomic Parameters Prediction Model of NSLBP based on open dataset

Objective: The purpose of this study is to perform analysis through the low back pain open data set to predict the incidence of non-specific chronic low back pain (NSLBP) to obtain a more accurate and convenient sagittal spinopelvic parameter model.Methods: The logistic regression analysis and multilayer perceptron (MLP) algorithm is used to construct a NSLBP prediction model based on the parameters of the spinopelvic parameters from open data source.Results: Degree of spondylolisthesis (DS), Pelvic radius (PR), Sacral slope (SS), Pelvic tilt (PT) are four predictors screened out by regression analysis that have significant predictive power for the risk of NSLBP. The overall accuracy of the equation prediction model is 85.8%.The MLP network algorithm determines that DS is the most powerful predictor of NSLBP through more precise modeling. The model has good predictive ability of 95.2% of accuracy.Conclusions: MLP models play a more accurate role in the construction of predictive models. Computer science is playing a greater role in helping precision medicine clinical research.

Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision

Study Design: Cross sectional database study. Objective: To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. Methods: Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). Results: The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). Conclusion: This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.