SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction.

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Age and Racial Disparities in Telemedicine Utilization in an Academic Orthopedic Surgery Department.

Introduction: The COVID-19 pandemic has highlighted significant racial and age-related health disparities. In response to pandemic-related restrictions, orthopedic surgery departments have expanded telemedicine use. We analyzed data from a tertiary care institute during the pandemic to understand potential racial and age-based disparities in access to care and telemedicine utilization. Materials and Methods: Data on patient race and age, and numbers of telemedicine visits, in-person office visits, and types of telemedicine were extracted for time periods during and preceding the pandemic. We calculated odds ratios for visit occurrence and type across race and age groups. Results: Patients ages 27-54 were 1.3 (95% confidence interval [CI] 1.1-1.4, p < 0.01) and 1.2 (95% CI 1.0-1.3, p < 0.05) times more likely to be seen than patients <27 during the pandemic, versus the 2019 and 2020 controls. Patients 54-82 were 1.3 (95% CI 1.1-1.5, p < 0.001) times more likely to be seen than patients <27 during the pandemic versus the 2019 control. Patients 27-54, 54-82, and 82+, respectively, were 3.3 (95% CI 2.6-4.2, p < 1e-20), 3.5 (95% CI 2.8-4.4, p < 1e-24), and 1.9 (95% CI 1.1-3.4, p < 0.05) times more likely to be seen by telemedicine than patients <27. Among pandemic telemedicine appointments, Black patients were 1.5 (95% CI 1.2-1.9, p < 1e-3) times more likely to be seen by audio-only telemedicine than White patients, as compared with video telemedicine. Conclusions: Telemedicine access barriers must be reduced to ensure that disparities during the pandemic do not persist.

Factors Driving Rapid Adoption of Telemedicine in an Academic Orthopedic Surgery Department.

Introduction: With the COVID-19 epidemic ever-expanding, nonemergent access to health care resources has been reduced to decrease the exposure for patients and health care providers. Alternatives to in-office outpatient medical evaluations are necessary. We aimed to analyze how quickly orthopedic surgery providers at a large academic institution adopted telemedicine, and identify any factors that were associated with earlier or "faster" telemedicine adoption. Methods: We analyzed the telemedicine activity of 39 providers within the Department of Orthopedic Surgery between March 16, 2020, and May 30, 2020, and constructed logistic regression models to identify characteristics with significant association to earlier or faster telemedicine adoption. Results: No significant predictors of percentage of visits conducted via telemedicine were found. However, increased experience and practice at multiple locations was associated with slower telemedicine adoption time, while Professor level academic rank was associated with a faster time to achieving 10% of pre-COVID visit volumes via telemedicine. Higher pre-COVID visit volumes were also significantly associated with faster telemedicine adoption. Demographic factors, including, age, gender, practice locations, academic degrees, pediatric specialty, and use of physician assistants/nurse practitioners, were not found to have significant associations with telemedicine use. Conclusions: These results indicate that telemedicine has an important role to play within academic orthopedic surgery practices, with a wide and diverse range of orthopedic surgery providers choosing to utilize it during the COVID-19 pandemic. Given the rapid expansion and urgency driving the adoption of telemedicine, these results illustrate the importance of considering provider-side characteristics in ensuring that providers are well equipped to utilize telemedicine.