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Objective: To evaluate inpatient outcomes among patients with hip fracture treated during the COVID-19 pandemic in New York City.

Background: The long incubation period and asymptomatic spread of COVID-19 present considerable challenges for health-care institutions. The identification of infected individuals is vital to prevent the spread of illness to staff and other patients as well as to identify those who may be at risk for disease-related complications. This is particularly relevant with the resumption of elective orthopaedic surgery around the world. We report the results of a universal testing protocol for COVID-19 in patients undergoing orthopaedic surgery during the coronavirus pandemic and to describe the postoperative course of asymptomatic patients who were positive for COVID-19. Methods: A retrospective review of adult operative cases between March 25, 2020, and April 24, 2020, at an orthopaedic specialty hospital in New York City was performed. Initially, a screening questionnaire consisting of relevant signs and symptoms (e.g., fever, cough, shortness of breath) or exposure dictated the need for nasopharyngeal swab real-time quantitative polymerase chain reaction (RT-PCR) testing for all admitted patients. An institutional policy change occurred on April 5, 2020, that indicated nasopharyngeal swab RT-PCR testing for all orthopaedic admissions. Screening and testing data for COVID-19 as well as relevant imaging, laboratory values, and postoperative complications were reviewed for all patients. Results: From April 5, 2020, to April 24, 2020, 99 patients underwent routine nasopharyngeal swab testing for COVID-19 prior to their planned orthopaedic surgical procedure. Of the 12.1% of patients who tested positive for COVID-19, 58.3% were asymptomatic. Three asymptomatic patients developed postoperative hypoxia, with 2 requiring intubation. The negative predictive value of using the signs and symptoms of disease to predict a negative test result was 91.4% (95% confidence interval [CI], 81.0% to 97.1%). Including a positive chest radiographic finding as a screening criterion did not improve the negative predictive value of screening (92.5% [95% CI, 81.8% to 97.9%]). Conclusions: A protocol for universal testing of all orthopaedic surgery admissions at 1 hospital in New York City during a 3-week period revealed a high rate of COVID-19 infections. Importantly, the majority of these patients were asymptomatic. Using chest radiography did not significantly improve the negative predictive value of screening. These results have important implications as hospitals anticipate the resumption of elective surgical procedures. Level of Evidence: Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.