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Scholarly Works (2 results)

  • Article
  • Peer Reviewed
Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health, Volume 24, Issue 2 (2023)

Introduction: Emergency physicians (EP) are suspected to have a high prevalence of insomnia and sleep-aid use. Most prior studies about sleep-aid use in EPs have been limited by low response rates. In this study our aim was to investigate the prevalence of insomnia and sleep-aid use among early-career Japanese EPs and assess the factors associated with insomnia and sleep-aid use.

Methods: We collected anonymous, voluntary, survey-based data regarding chronic insomnia and sleep-aid use from board-eligible EPs taking the initial Japanese Association of Acute Medicine board certification exam in 2019 and 2020. We describe the prevalence of insomnia and sleep-aid use and analyzed demographic and job-related factors using multivariable logistic regression analysis.

Results: The response rate was 89.71% (732 of 816). The prevalence of chronic insomnia and sleep-aid use was 24.89% (95% CI 21.78-28.29%) and 23.77% (95% CI 20.69-27.15%), respectively. Factors associated with chronic insomnia were long working hours (odds ratio [OR] 1.02, 1.01-1.03, per one-hour/week), and “stress factor” (OR 1.46, 1.13-1.90). Factors associated with sleep-aid use were male gender (OR 1.71, 1.03-2.86), unmarried status (OR 2.38, 1.39-4.10), and “stress factor” (OR 1.48, 1.13-1.94). The “stress factor” was mostly influenced by stressors in dealing with patients/families and co-workers, concern about medical malpractice, and fatigue.

Conclusions: Early-career EPs in Japan have a high prevalence of chronic insomnia and sleep-aid use. Long working hours and stress were associated with chronic insomnia, while male gender, unmarried status, and stress were associated with the use of sleep aids.

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Cover page: Prevalence and Risk Factors of Insomnia and Sleep-aid Use in Emergency Physicians in Japan: Secondary Analysis of a Nationwide SurveyCreative Commons 'BY' version 4.0 license
  • Article
  • Peer Reviewed
UC San Francisco Previously Published Works (2022)
Juvenile myelomonocytic leukemia (JMML) is a rare heterogeneous hematological malignancy of early childhood characterized by causative RAS pathway mutations. Classifying patients with JMML using global DNA methylation profiles is useful for risk stratification. We implemented machine learning algorithms (decision tree, support vector machine, and naïve Bayes) to produce a DNA methylation-based classification according to recent international consensus definitions using a well-characterized pooled cohort of patients with JMML (n = 128). DNA methylation was originally categorized into three subgroups: high methylation (HM), intermediate methylation (IM), and low methylation (LM), which is a trichotomized classification. We also dichotomized the subgroups as HM/IM and LM. The decision tree model showed high concordances with 450k-based methylation [82.3% (106/128) for the dichotomized and 83.6% (107/128) for the trichotomized subgroups, respectively]. With an independent cohort (n = 72), we confirmed that these models using both the dichotomized and trichotomized classifications were highly predictive of survival. Our study demonstrates that machine learning algorithms can generate clinical parameter-based models that predict the survival outcomes of patients with JMML and high accuracy. These models enabled us to rapidly and effectively identify candidates for augmented treatment following diagnosis.
    Cover page: Clinical parameter-based prediction of DNA methylation classification generates a prediction model of prognosis in patients with juvenile myelomonocytic leukemia
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