No abstract

Contributors NE, AG, RN and RMM made a substantial contribution to the work. AG, WJR, TF, EMC, KAT-D and DVF acquired, analysed and interpreted data. AC, Rd, RH, SH, OP, DR, ECT and MD revised it critically for important intellectual content.

OBJECTIVEScaphocephaly secondary to sagittal craniosynostosis has been treated in recent years with spring-assisted cranioplasty, an innovative approach that leverages the use of metallic spring distractors to reshape the patient skull. In this study, a population of patients who had undergone spring cranioplasty for the correction of scaphocephaly at the Great Ormond Street Hospital for Children was retrospectively analyzed to systematically assess spring biomechanical performance and kinematics in relation to spring model, patient age, and outcomes over time.METHODSData from 60 patients (49 males, mean age at surgery 5.2 ± 0.9 months) who had received 2 springs for the treatment of isolated sagittal craniosynostosis were analyzed. The opening distance of the springs at the time of insertion and removal was retrieved from the surgical notes and, during the implantation period, from planar radiographs obtained at 1 day postoperatively and at the 3-week follow-up. The force exerted by the spring to the patient skull at each time point was derived after mechanical testing of each spring model—3 devices with the same geometry but different wire thicknesses. Changes in the cephalic index between preoperatively and the 3-week follow-up were recorded.RESULTSStiffer springs were implanted in older patients (p < 0.05) to achieve the same opening on-table as in younger patients, but this entailed significantly different—higher—forces exerted on the skull when combinations of stiffer springs were used (p < 0.001). After initial force differences between spring models, however, the devices all plateaued. Indeed, regardless of patient age or spring model, after 10 days from insertion, all the devices were open.CONCLUSIONSResults in this study provide biomechanical insights into spring-assisted cranioplasty and could help to improve spring design and follow-up strategy in the future.

The UK Multiple Sclerosis Register (UKMSR) is a large cohort study designed to capture 'real world' information about living with multiple sclerosis (MS) in the UK from diverse sources. The primary source of data is directly from people with Multiple Sclerosis (pwMS) captured by longitudinal questionnaires via an internet portal. This population's diagnosis of MS is self-reported and therefore unverified. The second data source is clinical data which is captured from MS Specialist Treatment centres across the UK. This includes a clinically confirmed diagnosis of MS (by Macdonald criteria) for consented patients. A proportion of the internet population have also been consented at their hospital making comparisons possible. This dataset is called the 'linked dataset'. The purpose of this paper is to examine the characteristics of the three datasets: the self-reported portal data, clinical data and linked data, in order to assess the validity of the self-reported portal data. The internet (n = 11,021) and clinical (n = 3,003) populations were studied for key shared characteristics. We found them to be closely matched for mean age at diagnosis (clinical = 37.39, portal = 39.28) and gender ratio (female %, portal = 73.1, clinical = 75.2). The Two Sample Kolmogorov-Smirnov test was for the continuous variables to examine is they were drawn from the same distribution. The null hypothesis was rejected only for age at diagnosis (D = 0.078, p < 0.01). The populations therefore, were drawn from different distributions, as there are more patients with relapsing disease in the clinical cohort. In all other analyses performed, the populations were shown to be drawn from the same distribution. Our analysis has shown that the UKMSR portal population is highly analogous to the entirely clinical (validated) population. This supports the validity of the self-reported diagnosis and therefore that the portal population can be utilised as a viable and valid cohort of people with Multiple Sclerosis for study.

Implantation of spring-like distractors in the treatment of sagittal craniosynostosis is a novel technique that has proven functionally and aesthetically effective in correcting skull deformities; however, final shape outcomes remain moderately unpredictable due to an incomplete understanding of the skull-distractor interaction. The aim of this study was to create a patient specific computational model of spring assisted cranioplasty (SAC) that can help predict the individual overall final head shape. Pre-operative computed tomography images of a SAC patient were processed to extract a 3D model of the infant skull anatomy and simulate spring implantation. The distractors were modeled based on mechanical experimental data. Viscoelastic bone properties from the literature were tuned using the specific patient procedural information recorded during surgery and from x-ray measurements at follow-up. The model accurately captured spring expansion on-table (within 9% of the measured values), as well as at first and second follow-ups (within 8% of the measured values). Comparison between immediate post-operative 3D head scanning and numerical results for this patient proved that the model could successfully predict the final overall head shape. This preliminary work showed the potential application of computational modeling to study SAC, to support pre-operative planning and guide novel distractor design.