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Objective Fully or partially automated spinal cord gray matter segmentation techniques for spinal cord gray matter segmentation will allow for pivotal spinal cord gray matter measurements in the study of various neurological disorders. The objective of this work was multi-fold: (1) to develop a gray matter segmentation technique that uses registration methods with an existing delineation of the cord edge along with Morphological Geodesic Active Contour (MGAC) models; (2) to assess the accuracy and reproducibility of the newly developed technique on 2D PSIR T1 weighted images; (3) to test how the algorithm performs on different resolutions and other contrasts; (4) to demonstrate how the algorithm can be extended to 3D scans; and (5) to show the clinical potential for multiple sclerosis patients. Methods The MGAC algorithm was developed using a publicly available implementation of a morphological geodesic active contour model and the spinal cord segmentation tool of the software Jim (Xinapse Systems) for initial estimate of the cord boundary. The MGAC algorithm was demonstrated on 2D PSIR images of the C2/C3 level with two different resolutions, 2D T2* weighted images of the C2/C3 level, and a 3D PSIR image. These images were acquired from 45 healthy controls and 58 multiple sclerosis patients selected for the absence of evident lesions at the C2/C3 level. Accuracy was assessed though visual assessment, Hausdorff distances, and Dice similarity coefficients. Reproducibility was assessed through interclass correlation coefficients. Validity was assessed through comparison of segmented gray matter areas in images with different resolution for both manual and MGAC segmentations. Results Between MGAC and manual segmentations in healthy controls, the mean Dice similarity coefficient was 0.88 (0.82–0.93) and the mean Hausdorff distance was 0.61 (0.46–0.76) mm. The interclass correlation coefficient from test and retest scans of healthy controls was 0.88. The percent change between the manual segmentations from high and low-resolution images was 25%, while the percent change between the MGAC segmentations from high and low resolution images was 13%. Between MGAC and manual segmentations in MS patients, the average Dice similarity coefficient was 0.86 (0.8–0.92) and the average Hausdorff distance was 0.83 (0.29–1.37) mm. Conclusion We demonstrate that an automatic segmentation technique, based on a morphometric geodesic active contours algorithm, can provide accurate and precise spinal cord gray matter segmentations on 2D PSIR images. We have also shown how this automated technique can potentially be extended to other imaging protocols.

IMPORTANCE: Primary care is increasingly delivered at or near workplaces, yet utilization and cost of employer-sponsored primary care services remain unknown. OBJECTIVE: To compare the health care utilization and cost of an employer-sponsored on-site, near-site, and virtual comprehensive primary care service delivery model with those of traditional community-based primary care. DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study of 23 518 commercially insured employees and dependents of an engineering and manufacturing firm headquartered in southern California was performed from January 1, 2016, to July 1, 2019. A subset of the population with most (≥50%) primary care visits through employer-sponsored on-site, near-site, or virtual care clinics was matched to a subset not having most such visits through the employer-sponsored clinics using propensity score matching (n = 1983 each). In sensitivity analyses, employees were matched to dependents at neighboring firms that lacked access to the employer-sponsored primary care delivery model (additional n = 1680). EXPOSURES: Integrated primary care, mental health, and physical therapy delivered through on-site, near-site, and virtual clinics. MAIN OUTCOMES AND MEASURES: Utilization (inpatient, outpatient, emergency department, pharmaceutical, radiology, and laboratory visits per 1000 member-months) and spending (2019 costs per member per month in US dollars) by service type. RESULTS: A total of 23 518 individuals (mean [SD] age, 27 [15] years; 14 604 [62.1%] male) were included in the full population sample and had been enrolled in the employer-sponsored health plan for a mean of 29 months (interquartile range, 14-48 months). Of eligible members, 5292 (22.5%) used the employer-sponsored services, with 2305 (9.8%) using them for most of their primary care. The mean (SD) cost of employer-sponsored service delivery was $87 ($32) per member month. Among the matched populations (mean [SD] age, 31 [11] years; 3349 [84.5%] male) of primary users vs control individuals, total spending was 45% lower per member per month (95% CI, 35%-55%; cost difference, −$167 per member per month; 95% CI, −$204 to −$130; P

PURPOSE: To explore: (1) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1-weighted scans related to gradient nonlinearity (GNL) and subject positioning; (2) the effect of vendor-implemented GNL-corrections; and (3) easily applicable methods that can be used to retrospectively correct data. METHODS: A multiple sclerosis patient was scanned at 7 sites using 3T MRI scanners with the same 3D T1-weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on: (1) phantom data fit; (2) normalization with C2 vertebral body diameters; and (3) the Jacobian determinant of non-linear registrations to a template, were tested. RESULTS: Depending on positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1-weighted scans when no distortion corrections were utilized. 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%. CONCLUSION: Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies and point the way to prospectively and retrospectively managing GNL-distortions in a variety of settings, including clinical environments.

Importance Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive. Objectives To investigate whether MS risk-associated HLA alleles also affect disease phenotypes. Design, Setting, and Participants A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015. Main Outcomes and Measures Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set. Results Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [−0.3 to 0.5], respectively; P = 1.8 × 10 −27 ). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard β = −1.20 × 10 −1 ; P = 1.7 × 10 −2 and standard β = −1.67 × 10 −1 ; P = 2.3 × 10 −4 , respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02 - B*07:02 - DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard β = −1.28 × 10 −1 ; P = 5.1 × 10 −3 and standard β = 9.52 × 10 −2 ; P = 3.6 × 10 −2 , respectively). Conclusions and Relevance We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.