Tendon injury frequently results in the formation of adhesions that reduce joint range of motion. To study the cellular, molecular, and biomechanical events involved in intrasynovial tendon healing and adhesion formation, we developed a murine flexor tendon healing model in which the flexor digitorum longus (FDL) tendon of C57BL/6 mice was transected and repaired using suture. This model was used to test the hypothesis that murine flexor tendons heal with differential expression of matrix metalloproteases (MMPs), resulting in the formation of scar tissue as well as the subsequent remodeling of scar and adhesions. Healing tendons were evaluated by histology, gene expression via real-time RT-PCR, and in situ hybridization, as well as biomechanical testing to assess the metatarsophalangeal (MTP) joint flexion range of motion (ROM) and the tensile failure properties. Tendons healed with a highly disorganized fibroblastic tissue response that was progressively remodeled through day 35 resulting in a more organized pattern of collagen fibers. Initial repair involved elevated levels of Mmp-9 at day 7, which is associated with catabolism of damaged collagen fibers. High levels of Col3 are consistent with scar tissue, and gradually transition to the expression of Col1. Scleraxis expression peaked at day 7, but the expression was limited to the original tendon adjacent to the injury site, and no expression was present in granulation tissue involved in the repair response. The MTP joint ROM with standardized force on the tendon was decreased on days 14 and 21 compared to day 0, indicating the presence of adhesions. Peak expressions of Mmp-2 and Mmp-14 were observed at day 21, associated with tendon remodeling. At day 28, two genes associated with neotendon formation, Smad8 and Gdf-5, were elevated and an improvement in MTP ROM occurred. Tensile strength of the tendon progressively increased, but by 63 days the repaired tendons had not reached the tensile strength of normal tendon. The murine model of primary tendon repair, described here, provides a novel mechanism to study the tendon healing process, and further enhances the understanding of this process at the molecular, cellular, and biomechanical level.

Reconstruction of flexor tendons often results in adhesions that compromise joint flexion. Little is known about the factors involved in the formation of flexor tendon graft adhesions. In this study, we developed and characterized a novel mouse model of flexor digitorum longus (FDL) tendon reconstruction with live autografts or reconstituted freeze-dried allografts. Grafted tendons were evaluated at multiple time points up to 84 days post-reconstruction. To assess the flexion range of the metatarsophalangeal joint, we developed a quantitative outcome measure proportional to the resistance to tendon gliding due to adhesions, which we termed the Gliding Coefficient. At 14 days post-grafting, the Gliding Coefficient was 29-and 26-fold greater than normal FDL tendon for both autografts and allografts, respectively (p < 0.001), and subsequently doubled for 28-day autografts. Interestingly, there were no significant differences in maximum tensile force or stiffness between live autograft and freeze-dried allograft repairs over time. Histologically, autograft healing was characterized by extensive remodeling and exuberant scarring around both the ends and the body of the graft, whereas allograft scarring was abundant only near the graft-host junctions. Gene expression of GDF-5 and VEGF were significantly increased in 28-day autografts compared to allografts and to normal tendons. These results suggest that the biomechanical advantages for tendon reconstruction using live autografts over devitalized allografts are minimal. This mouse model can be useful in elucidating the molecular mechanisms in tendon repair and can aid in preliminary screening of molecular treatments of flexor tendon adhesions. ß

The objective of this research was to assess the implementation of collecting patient-reported outcomes data in the outpatient clinics of a large academic hospital and identify potential barriers and solutions to such an implementation. Three PROMIS computer adaptive test instruments, (1) physical function, (2) pain interference, and (3) depression, were administered at 23,813 patient encounters using a novel software platform on tablet computers. The average time to complete was 3.50 ± 3.12 min, with a median time of 2.60 min. Registration times for new patients did not change significantly, 6.87 ± 3.34 to 7.19 ± 2.69 min. Registration times increased for follow-up (p = .007) from 2.94 ± 1.57 (p < .01) min to 3.32 ± 1.78 min. This is an effective implementation strategy to collect patient-reported outcomes and directly import the results into the electronic medical record in real time for use during the clinical visit.

Tendon reconstruction using grafts often results in adhesions that limit joint flexion. These adhesions are precipitated by inflammation, fibrosis, and the paucity of tendon differentiation signals during healing. In order to study this problem, we developed a mouse model in which the flexor digitorum longus (FDL) tendon is reconstructed using a live autograft or a freeze-dried allograft, and identified growth and differentiation factor 5 (Gdf5) as a therapeutic target. In this study we have investigated the potential of rAAV-Gdf5 -loaded freeze-dried tendon allografts as "therapeutically endowed" tissue-engineering scaffolds to reduce adhesions. In reporter gene studies we have demonstrated that recombinant adeno-associated virus (rAAV)-loaded tendon allografts mediate efficient transduction of adjacent soft tissues, with expression peaking at 7 days. We have also demonstrated that the rAAV-Gdf5 vector significantly accelerates wound healing in an in vitro fibroblast scratch model and, when loaded onto freeze-dried FDL tendon allografts, improves the metatarsophalangeal (MTP) joint flexion to a significantly greater extent than the rAAV-lacZ controls do. Collectively, our data demonstrate the feasibility and efficacy of therapeutic tendon allograft processing as a novel paradigm in tissue engineering in order to address difficult clinical problems such as tendon adhesions.

This pilot study investigated the feasibility of Google Glass to assist visualization of fluoroscopic images during percutaneous pinning of hand fractures. Cadavers were used to compare total time to pin each fracture and total number of radiographs per fracture from a mini C-arm. A FluoroScan monitor was used for radiographic visualization compared to projecting the images in the Google Glass display. All outcome measures significantly improved for proximal phalanx fractures (127 versus 86 seconds, p = 0.017; 5.3 versus 2.2 images, p = 0.003), and fewer images were obtained during fixation of metacarpal fractures using Google Glass compared with traditional techniques (6.4 versus 3.6, p < 0.001). Typical FluoroScan monitor placement may require the surgeon to alter focus away from the operative field, whereas Google Glass allows constant attention directed toward the operative field.

Objectives: The Patient-Reported Outcomes Measurement Information System (PROMIS) is growing in popularity as healthcare shifts towards a value-based system. However, it remains unclear if PROMIS use improves the patient experience. The aim of the present study was to determine if PROMIS use as part of routine orthopaedic clinical care is associated with improved patient experience, as measured by the Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CGCAHPS) survey.Methods: All patient visits to an orthopaedic surgery clinic at a single academic medical centre between February 2015 and September 2016 were reviewed.Accounting for known patient factors that have an impact on clinic visit satisfaction, CGCAHPS scores were compared between patients who had PROMIS used as part of their routine care and those who had not had PROMIS used as part of their routine care. A p-value of <0.05 was considered significant.Results: A total of 8,607 patient visits fitted our inclusion criteria. Of these, surgeons elected not to use PROMIS in 8,422 patient encounters, leaving 185 patient visits in which PROMIS was actively used. When PROMIS was used, patients were significantly more likely to feel that the provider had spent enough time with them, to recommend this provider office to another patient and to rate the provider significantly higher on a scale from 0 to 10. Although not significant, a trend was found between use of PROMIS and whether a patient felt that a provider explained health information in way that the patient understood.Conclusions: PROMIS use in an orthopaedic clinic visit can have a positive impact on the patient experience, which is currently part of a number of alternative payment models.

Purpose Osteoporosis and osteopenia are extremely common and can lead to fragility fractures. The purpose of this study was to determine whether a computer learning system could classify whether a hand radiograph demonstrated osteoporosis based on the second metacarpal cortical percentage. Methods We used the second metacarpal cortical percentage as the osteoporosis predictor. A total of 4,000 posteroanterior (PA) radiographs of the hand were standardized through laterality correction, vertical alignment correction, segmentation, proxy osteoporosis predictor, and full pipeline. Laterality was classified using a LeNet convolutional neural network (CNN). Vertical alignment classification used 2,000 PA x-rays to determine vertical alignment of the second metacarpal. We employed segmentation to determine which pixels belong to the second metacarpal from 1,000 PA x-rays using the FSN-8 CNN. The full pipeline was tested on 265 previously unseen PA x-rays. Results Laterality classification accuracy was 99.62%, with a specificity of 100% and sensitivity of 99.3%. Rotation of the hand within 10 of vertical was accurate in 93.2% of films. Segmentation was 94.8% accurate. Proxy osteoporosis predictor was 88.4% accurate. Full pipeline accuracy was 93.9%. In the testing data set, the CNN had a sensitivity of 82.4% and specificity of 95.7%. In the balanced data set, 6 of 39 osteoporotic films were classified as nonosteoporotic; sensitivity was 82.4% and specificity, 94.3%. Conclusions We have created a series of CNN that can accurately identify osteoporosis from non-osteoporosis. Furthermore, our CNN is able to make adjustments to images based on laterality and vertical alignment. Clinical relevance Convolutional neural network and computer learning can be used as an adjunct to dual-energy x-ray absorptiometry scans or to screen and make appropriate referrals for further workup in patients with suspected osteoporosis.

Preoperative PROMIS PF, PI, and Depression scores can predict postoperative PROMIS score improvement for a select group of patients, which may help in setting expectations. Future work can help determine the level of true clinical improvement these findings represent.