Impact of the COVID-19 Pandemic Hiatus From Sports Activities on Injuries Observed Among Division I NCAA Athletes.

Background: The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted athletic activities, including those in the Pacific 12 (Pac-12) Conference of the National Collegiate Athletic Association. It is currently unknown how the disruption in training and competition impacted athletes' risk of injury upon resumption of activities. Purpose: To describe and compare the rate, timing, mechanism, and severity of injuries among collegiate athletes across multiple sports in the Pac-12 Conference before and after the COVID-19 pandemic-associated hiatus of intercollegiate athletic activities. Study Design: Descriptive epidemiology study. Methods: Descriptive and injury data among intercollegiate athletes from both the season before the hiatus and the season after the hiatus were acquired from the Pac-12 Health Analytics Program database. Injury elements (timing of injury onset, injury severity, mechanism, recurrence, outcome, need for procedural intervention, and event segment during which the injury took place) were compared by time using the chi-square test and a multivariate logistic regression model. Subgroup analyses were performed on knee and shoulder injuries among athletes participating in sports with traditionally high rates of knee and shoulder injuries. Results: A total of 12,319 sports-related injuries across 23 sports were identified, with 7869 pre-hiatus injuries and 4450 post-hiatus injuries. There was no difference in the overall incidence of injury between the pre-hiatus and post-hiatus seasons. However, the proportion of noncontact injuries was higher in the post-hiatus season for football, baseball, and softball players, and the proportion of nonacute injuries in the post-hiatus season was higher among football, basketball, and rowing athletes. Finally, the overall proportion of injuries sustained by football players in the final 25% of competition or practice was higher in the post-hiatus season. Conclusion: Athletes competing in the post-hiatus season were observed to have higher rates of noncontact injuries and injuries sustained in the final 25% of competition. This study demonstrates that the COVID-19 pandemic has had varied effects on athletes from different sports, suggesting that many factors must be considered when designing return-to-sports programs for athletes after an extended absence from organized training.

Homebrew reagents for low-cost RT-LAMP

Reverse transcription–loop-mediated isothermal amplification (RT-LAMP) has gained popularity for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The high specificity, sensitivity, simple protocols, and potential to deliver results without the use of expensive equipment has made it an attractive alternative to RT-PCR. However, the high cost per reaction, the centralized manufacturing of required reagents, and their distribution under cold chain shipping limit RT-LAMP’s applicability in low-income settings. The preparation of assays using homebrew enzymes and buffers has emerged worldwide as a response to these limitations and potential shortages. Here, we describe the production of Moloney murine leukemia virus reverse transcriptase and BstLF DNA polymerase for the local implementation of RT-LAMP reactions at low cost. These reagents compared favorably to commercial kits, and optimum concentrations were defined in order to reduce time to threshold, increase ON/OFF range, and minimize enzyme quantities per reaction. As a validation, we tested the performance of these reagents in the detection of SARS-CoV-2 from RNA extracted from clinical nasopharyngeal samples, obtaining high agreement between RT-LAMP and RT-PCR clinical results. The in-house preparation of these reactions results in an order of magnitude reduction in costs;thus, we provide protocols and DNA to enable the replication of these tests at other locations. These results contribute to the global effort of developing open and low-cost diagnostics that enable technological autonomy and distributed capacities in viral surveillance.

Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.

Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting.

Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust's COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged.