ICD-10 diagnosis codes in electronic health records do not adequately capture fracture complexity for proximal humerus fractures.

BACKGROUND: The ability to do comparative effectiveness research (CER) for proximal humerus fractures (PHF) using data in electronic health record (EHR) systems and administrative claims databases was enhanced by the 10th revision of the International Classification of Diseases (ICD-10), which expanded the diagnosis codes for PHF to describe fracture complexity including displacement and the number of fracture parts. However, these expanded codes only enhance secondary use of data for research if the codes selected and recorded correctly reflect the fracture complexity. The objective of this project was to assess the accuracy of ICD-10 diagnosis codes documented during routine clinical practice for secondary use of EHR data. METHODS: A sample of patients with PHFs treated by orthopedic providers across a large, regional health care system between January 1, 2016, and December 31, 2018, were retrospectively identified from the EHR. Four fellowship-trained orthopedic surgeons reviewed patient radiographs and recorded the Neer Classification characteristics of displacement, number of parts, and fracture location(s). The fracture characteristics were then reviewed by a trained coder, and the most clinically appropriate ICD-10 diagnosis code based on the number of fracture parts was assigned. We assessed congruence between ICD-10 codes documented in the EHR and radiograph-validated codes, and assessed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for EHR-documented ICD-10 codes. RESULTS: There were 761 patients with unilateral, closed PHF who met study inclusion criteria. On average, patients were 67 years of age and 77% were female. Based on radiograph review, 37% were 1-part fractures, 42% were 2-part, 11% were 3-part, and 10% were 4-part fractures. Of the EHR diagnosis codes recorded during clinical practice, 59% were "unspecified" fracture diagnosis codes that did not identify the number of fracture parts. Examination of fracture codes revealed PPV was highest for 1-part (PPV = 0.66, 95% confidence interval [CI] 0.60-0.72) and 4-part fractures (PPV = 0.67, 95% CI 0.13-1.00). CONCLUSIONS: Current diagnosis coding practices do not adequately capture the fracture complexity needed to conduct subgroup analysis for PHF. Conclusions drawn from population studies or large databases using ICD-10 codes for PHF classification should be interpreted within this limitation. Future studies are warranted to improve diagnostic coding to support large observational studies using EHR and administrative claims data.

Strategies to Identify Mesenchymal Stromal Cells in Minimally Manipulated Human Bone Marrow Aspirate Concentrate Lack Consensus.

BACKGROUND: There is a need to identify and quantify mesenchymal stromal cells (MSCs) in human bone marrow aspirate concentrate (BMAC) source tissues, but current methods to do so were established in cultured cell populations. Given that surface marker and gene expression change in cultured cells, it is doubtful that these strategies are valid to quantify MSCs in fresh BMAC. PURPOSE: To establish the presence, quantity, and heterogeneity of BMAC-derived MSCs in minimally manipulated BMAC using currently available strategies. STUDY DESIGN: Descriptive laboratory study. METHODS: Five published strategies to identify MSCs were compared for suitability and efficiency to quantify clinical-grade BMAC-MSCs and cultured MSCs at the single cell transcriptome level on BMAC samples being used clinically from 15 orthopaedic patients and on 1 cultured MSC sample. Strategies included (1) the guidelines by the International Society for Cellular Therapy (ISCT), (2) CD271 expression, (3) the Ghazanfari et al transcriptional profile, (4) the Jia et al transcriptional profile, and (5) the Silva et al transcriptional profile. RESULTS: ISCT guidelines did not identify any MSCs in BMAC at the transcriptional level and only 1 in 9 million cells at the protein level. Of 12,850 BMAC cells, 9 expressed the CD271 gene. Only 116 of 396 Ghazanfari genes were detected in BMAC, whereas no cells expressed all of them. No cells expressed all Jia genes, but 25 cells expressed at least 13 of 22. No cells expressed all Silva genes, but 19 cells expressed at least 8 of 23. Most importantly, the liberalized strategies tended to identify different cells and most of them clustered with immune cells. CONCLUSION: Currently available methods need to be liberalized to identify any MSCs in fresh human BMAC and lack consensus at the single cell transcriptome and protein expression levels. These different cells should be isolated and challenged to establish phenotypic differences. CLINICAL RELEVANCE: This study demonstrated that improved strategies to quantify MSC concentrations in BMAC for clinical applications are urgently needed. Until then, injected minimally manipulated MSC doses should be reported as rough estimates or as unknown.

What Was the Change in Telehealth Usage and Proportion of No-show Visits for an Orthopaedic Trauma Clinic During the COVID-19 Pandemic?

BACKGROUND: In response to the coronavirus disease 2019 (COVID-19) pandemic, the Centers for Medicare and Medicaid Services pledged payment for telehealth visits for the duration of this public health emergency in an effort to decrease COVID-19 transmission and allow for deployment of residents and attending physicians to support critical-care services. Although the COVID-19 pandemic has vastly expanded telehealth use, no studies to our knowledge have analyzed the implementation and success of telehealth for orthopaedic trauma. This population is unique in that patients who have experienced orthopaedic trauma range in age from early childhood to late adulthood, they vary across the socioeconomic spectrum, may need to undergo emergent or urgent surgery, often have impaired mobility, and, historically, do not always follow-up consistently with healthcare providers. QUESTIONS/PURPOSES: (1) To what extent did telehealth usage increase for an outpatient orthopaedic trauma clinic at a Level 1 trauma center from the month before the COVID-19 stay-at-home order compared with the month immediately following the order? (2) What is the proportion of no-show visits before and after the implementation of telehealth? METHODS: After nonurgent clinic visits were postponed, telehealth visits were offered to all patients due to the COVID-19 stay-at-home order. Patients with internet access who had the ability to download the MyChart application on their mobile device and agreed to a telehealth visit were seen virtually between March 16, 2020 and April 10, 2020 (COVID-19) by three attending orthopaedic trauma surgeons at a large, urban, Level 1 trauma center. Clinic schedules and patient charts were reviewed to determine clinical volumes and no-show proportions. The COVID-19 period was compared with the 4 weeks before March 16, 2020 (pre-COVID-19), when all visits were conducted in-person. The overall clinic volume decreased from 340 to 233 (31%) between the two periods. The median (range) age of telehealth patients was 46 years (20 to 89). Eighty-four percent (72 of 86) of telehealth visits were postoperative and established nonoperative patient visits, and 16% (14 of 86) were new-patient visits. To avoid in-person suture or staple removal, patients seen for their 2-week postoperative visit had either absorbable closures, staples, or nonabsorbable sutures removed by a home health registered nurse or skilled nursing facility registered nurse. If radiographs were indicated, they were obtained at outside facilities or our institution before patients returned home for their telehealth visit. RESULTS: There was an increase in the percentage of office visits conducted via telehealth between the pre-COVID-19 and COVID-19 periods (0% [0 of 340] versus 37% [86 of 233]; p < 0.001), and by the third week of implementation, telehealth comprised approximately half of all clinic visits (57%; [30 of 53]). There was no difference in the no-show proportion between the two periods (13% [53 of 393] for the pre-COVID-19 period and 14% [37 of 270] for the COVID-19 period; p = 0.91). CONCLUSIONS: Clinicians should consider implementing telehealth strategies to provide high-quality care for patients and protect the workforce during a pandemic. In a previously telehealth-naïve clinic, we show successful implementation of telehealth for a diverse orthopaedic trauma population that historically has issues with mobility and follow-up. Our strategies include postponing long-term follow-up visits, having sutures or staples removed by a home health or skilled nursing facility registered nurse, having patients obtain pertinent imaging before the visit, and ensuring that patients have access to mobile devices and internet connectivity. Future studies should evaluate the incidence of missed infections or stiffness as a result of telehealth, analyze the subset of patients who may be more vulnerable to no-shows or technological failures, and conduct patient surveys to determine the factors that contribute to patient preferences for or against the use of telehealth. LEVEL OF EVIDENCE: Level III, therapeutic study.