Influence of medical trainee sleep pattern (chronotype) on burn-out and satisfaction with work schedules: a multicentre observational study.

BACKGROUND: Medical trainees' work schedule is designed to cover duties without consideration of differences in circadian rhythms during a 24-hour period (chronotype). OBJECTIVE: To explore chronotype variation among medical trainees and understand its association with burn-out and schedule satisfaction. METHODS: In a multicentre observational study, we conducted two surveys between 1 October 2018 and 1 April 2019. Trainees from nine centres across the USA participated. We measured burn-out using Maslach Burnout Inventory (MBI), and trainee chronotype using the Morningness-Eveningness Questionnaire (MEQ). RESULTS: 324 (32%) out of 1012 responded to our survey. Participants were 51% female and had a mean age of 30.8 years. Most participants had an intermediate MEQ type (65%). A large proportion of participants had burn-out on at least one of three tested MBI scales (62%); 5% of participants had burn-out on all three MBI scales. More participants with evening MEQ type had burn-out (66%) compared with morning MEQ type (55%), however, the results were not statically significant (p=0.294). Overall satisfaction with work shifts was 6.5 (95% CI 6.3 to 6.7), with higher satisfaction with day shift 7.7 (95% CI 7.5 to 7.9) and lowest satisfaction with overnight 24-hour call 3.5 (95% CI 3.2 to 3.9). Satisfaction was lower in trainees with burn-out 6.0 (95% CI 5.7 to 6.4), (p<0.001). In the follow-up survey, burn-out was present in at least one scale in 64% compared with 60% of respondents in the initial survey. CONCLUSION: Burn-out is prevalent among medical trainees. Improving alignment between trainee preferences may improve performance, reduce human errors and burn-out.

To Treat or Not to Treat Poorly Differentiated Adenocarcinoma of Unknown Origin.

This case illustrates that although advances have been made with diagnosis and treatment of adenocarcinoma of unknown origin with targeted therapy, more research needs to be done on poorly differentiated adenocarcinoma that initially presents with extensive metastases. In this patient's case, it was beneficial and ethical to reduce the toxicity and emotional burden and thus limit further investigation into her adenocarcinoma. However, it is imperative to recognize that only a small subset of adenocarcinoma of unknown origin are responsive to current therapies and more research is required for the many cases that present with poorly differentiated adenocarcinoma and widespread metastasis.

Managed care COVID-19 outcomes in a population health program.

OBJECTIVES: To determine (1) factors linked to hospitalizations among managed care patients (MCPs), (2) outcome improvement with use of outpatient off-label treatment, and (3) outcome comparison between MCPs and a mirror group. STUDY DESIGN: Retrospective cohort study comparing MCPs with an age- and gender-matched mirror group in Florida from April 1, 2020, to May 31, 2020. METHODS: A total of 38,193 MCPs in a Florida primary care group were monitored for COVID-19 incidence, hospitalization, and mortality. The highest-risk patients were managed by the medical group's COVID-19 Task Force. As part of a population health program, the COVID-19 Task Force contacted patients, conducted medical encounters, and tracked data including comorbidities and medical outcomes. The MCPs enrolled in the medical group were compared with a mirror group from the state of Florida. RESULTS: The mean (SD) age among the MCPs was 67.9 (15.2) years, and 60% were female. Older age and hypertension were the most important factors in predicting COVID-19. Obesity, chronic kidney disease (CKD), and congestive heart failure (CHF) were linked to higher rates of hospitalizations. Patients prescribed off-label outpatient medications had 73% lower likelihood of hospitalization (P < .05). Compared with the mirror group, MCPs had 60% lower COVID-19 mortality (P < .05). CONCLUSIONS: MCPs have risk factors similar to the general population for COVID-19 incidence and progression, including older age, hypertension, obesity, CHF, and CKD. Outpatient treatment with off-label medicines decreased hospitalizations. A comprehensive population health program decreased COVID-19 mortality.

Current Understanding of COVID-19 Clinical Course and Investigational Treatments.

Importance: Currently, there is no unified framework linking disease progression to established viral levels, clinical tests, inflammatory markers, and investigational treatment options. Objective: It may take many weeks or months to establish a standard treatment approach. Given the growing morbidity and mortality with respect to COVID-19, this systemic review presents a treatment approach based on a thorough review of scholarly articles and clinical reports. Our focus is on staged progression, clinical algorithms, and individualized treatment. Evidence Review: We followed the protocol for a quality review article proposed by Heyn et al. (1). A literature search was conducted to find all relevant studies related to COVID-19. The search was conducted between April 1, 2020, and April 13, 2020, using the following electronic databases: PubMed (1809 to present); Google Scholar (1900 to present); MEDLINE (1946 to present), CINAHL (1937 to present); and Embase (1980 to present). The keywords used included COVID-19, 2019-nCov, SARS-CoV-2, SARS-CoV, and MERS-CoV, with terms such as efficacy, seroconversion, microbiology, pathophysiology, viral levels, inflammation, survivability, and treatment and pharmacology. No language restriction was placed on the search. Reference lists were manually scanned for additional studies. Findings: Of the articles found in the literature search, 70 were selected for inclusion in this study (67 cited in the body of the manuscript and 3 additional unique references in the Figures). The articles represent work from China, Japan, Taiwan, Vietnam, Rwanda, Israel, France, the United Kingdom, the Netherlands, Canada, and the United States. Most of the articles were cohort or case studies, but we also drew upon other information, including guidelines from hospitals and clinics instructing their staff on procedures to follow. In addition, we based some decisions on data collected by organizations such as the CDC, FDA, IHME, IDSA, and Worldometer. None of the case studies or cohort studies used a large number of participants. The largest group of participants numbered <500 and some case studies had fewer than 30 patients. However, the review of the literature revealed the need for individualized treatment protocols due to the variability of patient clinical presentation and survivability. A number of factors appear to influence mortality: the stage at which the patient first presented for care, pre-existing health conditions, age, and the viral load the patient carried. Conclusion and Relevance: COVID-19 can be divided into three distinct stages, beginning at the time of infection (Stage I), sometimes progressing to pulmonary involvement (Stage II, with or without hypoxemia), and less frequently to systemic inflammation (Stage III). In addition to modeling the stages of disease progression along with diagnostic testing, we have also created a treatment algorithm that considers age, comorbidities, clinical presentation, and disease progression to suggest drug classes or treatment modalities. This paper presents the first evidence-based recommendations for individualized treatment for COVID-19.

Life after breast, prostate, and colon cancer: Primary care's role.

When patients survive cancer, they eventually come back to their primary care physicians. This group has special needs, including surveillance for recurrent and new cancer, health promotion, and interventions to mitigate the lingering effects of the cancer and the adverse effects of its treatment.