Objectives: To assess the individual impact of social determinants of health disparities (SDHD) on surgical outcomes following orthopaedic trauma surgery. Design: Retrospective Cohort Study. Setting: Mariner Claims Database. Patients: Inclusion criteria were patients 18–85 years of age, undergoing surgery for hip fractures or ankle fractures from 2010 to 2018. Intervention: Patients were divided based on SDHD using International Classification of Diseases 9 and International Classification of Diseases 10 codes. Those with SDHD were propensity-score matched with those who did not have any disparities with respect to age, gender, Charlson comorbidity index, tobacco use, and obesity (body mass index >30 kg/m²). Outcomes: Ninety-day major medical complications, infection, readmission, and revisions within 1 year. Results: Patients with educational deficiencies had increased rates of readmission and major complications compared with those without disparities following hip and ankle fracture management. Moreover, economic disparities were associated with an increased risk of readmission and revision following hip fracture surgery and infection and readmission following ankle fracture surgery. Discussion/Conclusions: This study emphasizes the large impact of SDHD on patients' outcomes following surgery and the importance of proper follow-up interventions to optimize patient care. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

The use of wearable sensors to track activity is increasing. Therefore, a survey among AO Trauma members was conducted to provide an overview of their current utilization and determine future needs and directions. A cross sectional expert opinion survey was administered to members of AO Trauma. Respondents were surveyed concerning their experience, subspeciality, current use characteristics, as well as future needs concerning wearable technology. Three hundred and thirty-three survey sets were available for analysis (Response Rate 16.2%). 20.7% of respondents already use wearable technology as part of their clinical treatment. The most prevalent technology was accelerometry combined with smartphones (75.4%) to measure general patient activity. To facilitate the use of wearable technology in the future, the most pressing issues were cost, patient compliance and validity of results. Wearable activity monitors are currently being used in trauma surgery. Surgeons employing these technologies mostly measure simple activity or activity associated parameters. Cost was the greatest perceived barrier to implementation. Further research, especially concerning the interpretation of the outcome values obtained, is required to facilitate wearable activity monitoring as an objective patient outcome measurement tool.

Introduction: This multicenter cohort study investigated the association of serology and comorbid conditions with septic and aseptic nonunion. Methods: From January 1, 2011, to December 31, 2017, consecutive individuals surgically treated for nonunion were identified from seven centers. Nonunion-type, comorbid conditions and serology were assessed. Results: A total of 640 individuals were included. 57% were male with a mean age of 49 years. Nonunion sites included tibia (35.2%), femur (25.6%), humerus (20.3%), and other less frequent bones (18.9%). The type of nonunion included septic (17.7%) and aseptic (82.3%). Within aseptic, nonvascular (86.5%) and vascular (13.5%) nonunion were seen. Rates of smoking, alcohol abuse, and diabetes mellitus were higher in our nonunion cohort compared with population norms. Coronary artery disease and tobacco use were associated with septic nonunion (P < 0.05). Diphosphonates were associated with vascular nonunion (P < 0.05). Serologically, increased erythrocyte sedimentation rate, C-reactive protein, parathyroid hormone, red cell distribution width, mean platelet volume (MPV), and platelets and decreased absolute lymphocyte count, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and albumin were associated with septic nonunion while lower calcium was associated with nonvascular nonunion (P < 0.05). The presence of four or more of increased erythrocyte sedimentation rate, C-reactive protein, or red cell distribution width; decreased albumin; and age younger than 65 years carried an 89% positive predictive value for infection. Hypovitaminosis D was seen less frequently than reported in the general population, whereas anemia was more common. However, aside from hematologic and inflammatory indices, no other serology was abnormal more than 25% of the time. Discussion: Abnormal serology and comorbid conditions, including smoking, alcohol abuse, and diabetes mellitus, are seen in nonunion; however, serologic abnormalities may be less common than previously thought. Septic nonunion is associated with inflammation, younger age, and malnourishment. Based on the observed frequency of abnormality, routine laboratory work is not recommended for nonunion assessment; however, specific focused serology may help determine the presence of septic nonunion.

Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients’ own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study’s screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.