Introduction Disruption of sleep and circadian rhythms is highly prevalent in intensive care unit (ICU) patients and may be exacerbated by environmental noise. The specific impact of alarm sounds on patients' sleep and circadian rhythms in the ICU remains unclear. The aim of this scoping review was to systematically map the existing evidence on the impact of alarm sounds on sleep and circadian rhythms in ICU patients. Methods A systematic literature search across four databases (PubMed, Embase, Web of Science, CINAHL and Ovid Emcare) was conducted to identify studies examining the impact of alarm sounds on sleep and circadian rhythms in ICU patients. Data on study population, methodology, and main findings on alarm sound impact were extracted, summarized in tables, and narratively synthesized. Results The search yielded 429 unique publications, of which 24 met the inclusion criteria. Across studies, alarm sounds were consistently identified as a major source of sleep disruption, with 52% to 91% of ICU patients rating alarms as highly disturbing. Objective measures, though limited by small sample sizes, confirmed the significant role of alarms in disturbing sleep, often linking alarm sounds to arousals and awakenings, particularly at higher sound levels. No studies were identified that assessed effects on circadian rhythms or the impact of alarm management strategies. Conclusion These findings underscore the substantial burden of alarm noise on ICU sleep, and highlight the lack of evidence regarding effects on circadian rhythms, as well as the need for effective alarm management strategies.

Background: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients. Methods: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 ‘wave’ in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values. Results: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80–0.84]) compared to the National early warning score (0.72 [0.69–0.74]) and the Modified early warning score (0.67 [0.65–0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [− 0.09 to 0.14], slope = 0.79 [0.73–0.86]). Conclusions: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.