Development of a nomogram model for predicting the risk of insomnia in nurses who underwent the Long- COVID (preprint)

ABSTRACT

Purpose To investigate the prevalence of insomnia among nurses diagnosed with Long-COVID, analyze the potential risk factors, and establish a nomogram prediction model.Methods General demographic information was obtained, and assessments of sleep quality, burnout, and stress were performed in a single center in May 2023. Three hundred and ninety-eight nurses were recruited. The Lasso regression technique was employed to screen for potential factors contributing to insomnia. A prognostic nomogram was constructed and evaluated by receiver operating characteristic curves and calibration curves.Results Fifty-four percent of nurses complained of insomnia in this study. Eleven variables were independently associated with sleep patterns, including family, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout. The R-squared value was 0.4642 and the area under curve was 0.8661. The derived nomogram showed that neurological sequela, stress, job burnout, sleep time before infection, and previous sleep problems also made the most substantial contributions to predicting sleep patterns. The calibration curves for predicting insomnia showed significant agreement between the nomogram models and actual observations.Conclusion The present study established a nomogram prediction model of insomnia for nurses diagnosed with Long-COVID, which is helpful for the early clinical identification of high-risk individuals with insomnia.