Ramar, Kannan

The need for adequate good quality sleep to optimally function is well known. Over years, various physical, psychological, biological, and social factors have been investigated to understand their impact on sleep. However, understanding the etiological processes that are involved in causing sleep disturbances (SD) as impacted by stressful phases such as pandemics has not been well studied. Many such etiological and management strategies have surfaced during the latest “coronavirus disease of 2019 (COVID-19) pandemic. The occurrence of these SD in the infected and uninfected individuals poses a need to investigate factors linked to such occurrence during this phase. Some of such factors include stressful practices such as social distancing, masking, vaccines, and medications availability, changes in routines, and lifestyles. As the status of infection improved, a collective term for all the prolonged effects of COVID-19 after the resolution of the primary infection called the post-COVID-19 syndrome (PCS) surfaced. Apart from impacting sleep during the infectious phase, the aftereffects of this virus left an even greater impact during the PCS. Various mechanisms have been hypothesized to be linked to such SD during the PCS, but the available data are inconclusive. Further, the varied patterns of incidence of these SDs differed by many factors, such as age, gender, and geographical location, making clinical management even more challenging. This review elucidates the impact of coronavirus 2 (SARS-CoV-2) (COVID) disease on sleep health during the various phases of the COVID-19 pandemic. We also investigate different causal relationships, management strategies, and knowledge gaps related to SD during the COVID-19 pandemic.

Abstract Background Each training program has its own internal policies and restrictions, which must be considered while developing trainee schedules. Designing these schedules is complex and time consuming, and the final schedules often contain undesirable aspects for trainees. Objective We developed a decision-support system (DSS) to optimally schedule daily assignments and monthly rotations for trainees. The proposed DSS aims to 1) reduce the schedule development time, 2) maximize trainee preferences for desired rotations and vacation times, and 3) ensure adaptability of the DSS across multiple graduate medical programs through a flexible design and intuitive graphical user interface. Methods Using mixed-integer linear programming, we developed a scheduling model that 1) maximized trainees’ preferences on specific rotations and vacation times and 2) ensured fairness by assigning equal numbers of vacation days and a balanced schedule of difficult versus easy rotations among trainees. The model was successfully implemented in the Mayo Clinic Division of Pulmonary and Critical Care for the academic year 2018–2019. Results Using the DSS, it took only a few minutes to produce a schedule versus several days of preparation time required by the manual process. Compared with the manually developed schedule, the DSS schedule satisfied 11% more rotation preferences and improved fairness by 19%. All trainees met duty hours in the DSS schedule compared with 83% in the manually developed schedule. Conclusion The proposed DSS can dramatically reduce the schedule preparation time, accommodate more of trainees’ preferences, and improve fairness in assigning rotations.

Objective Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI. Method Prediction performances of multiple popular supervised learning were tested. Together with the typical steps of a machine learning project, steps to prevent bias were carefully conducted, in which analysis of correlation covariance, outlier removal, confounding analysis, and cross-validation were used. Result The most accurate model reached an area under receiver operating characteristic curve of 99.7%. Ten-fold cross-validation was used to ensure that the results were generalizable. Random forest and decision tree had the highest prediction accuracy rates of 98%. Similar accuracy rate was obtained on the validation cohort. Conclusions We developed a prediction model using advanced analytics to predict PI in at-risk hospitalized patients. This will help address appropriate interventions before the patients develop a PI.