Background: Fatigue training may be an effective way to mitigate fatigue-related risk. We aimed to critically review

Background: Scheduled napping during work shifts may be an effective way to mitigate fatigue-related risk. This study aimed to critically review and synthesize existing literature on the impact of scheduled naps on fatigue-related outcomes for EMS personnel and similar shift worker groups. Methods: A systematic literature review was performed of the impact of a scheduled nap during shift work on EMS personnel or similar shift workers. The primary (critical) outcome of interest was EMS personnel safety. Secondary (important) outcomes were patient safety; personnel performance; acute states of fatigue, alertness, and sleepiness; indicators of sleep duration and/or quality; employee retention/turnover; indicators of long-term health; and cost to the system. Meta-analyses were performed to evaluate the impact of napping on a measure of personnel performance (the psychomotor vigilance test [PVT]) and measures of acute fatigue. Results: Of 4,660 unique records identified, 13 experimental studies were determined relevant and summarized. The effect of napping on reaction time measured at the end of shift was small and non-significant (SMD 0.12, 95% CI −0.13 to 0.36; p = 0.34). Napping during work did not change reaction time from the beginning to the end of the shift (SMD −0.01, 95% CI −25.0 to 0.24; p = 0.96). Naps had a moderate, significant effect on sleepiness measured at the end of shift (SMD 0.40, 95% CI 0.09 to 0.72; p = 0.01). The difference in sleepiness from the start to the end of shift was moderate and statistically significant (SMD 0.41, 95% CI 0.09 to 0.72; p = 0.01). Conclusions: Reviewed literature indicated that scheduled naps at work improved performance and decreased fatigue in shift workers. Further research is required to identify the optimal timing and duration of scheduled naps to maximize the beneficial outcomes.

The quality of existing evidence on the impact of shift duration on fatigue and fatigue-related risks is low or very low. Despite these limitations, this systematic review suggests that for outcomes considered critical or important to EMS personnel, shifts <24 hours in duration are more favorable than shifts ≥24 hours.

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In this model of opioid overdose cardiac arrest, brain tissue hypoxia is common and treatable. Further work will elucidate best strategies and impact of titrated care on functional outcomes.

The effect of task load interventions on fatigue, fatigue-related risks, and/or sleep quality was not estimable and the overall quality of evidence was judged low or very low. There was considerable heterogeneity in how task load was defined and measured.

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OBJECTIVES: Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias. DESIGN: Retrospective observational cohort study. SETTING: Two academic medical centers (“UPMC” and “University of Alabama Birmingham” [UAB]). PATIENTS: Comatose adults resuscitated from cardiac arrest. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: As potential predictors, we considered clinical, laboratory, imaging, and quantitative electroencephalography data available early after hospital arrival. We followed patients until death, discharge, or awakening from coma. We used penalized Cox regression with a least absolute shrinkage and selection operator penalty and five-fold cross-validation to predict time to awakening in UPMC patients and then externally validated the model in UAB patients. This model censored patients after WLST-N, considering subsequent potential for awakening to be unknown. Next, we developed a penalized logistic model predicting awakening, which treated failure to awaken after WLST-N as a true observed outcome, and a separate logistic model predicting WLST-N. We scaled and centered individual patients’ Cox and logistic predictions for awakening to allow direct comparison and then explored the difference in predictions across probabilities of WLST-N. Overall, 1,254 patients were included, and 29% awakened. Cox models performed well (mean area under the curve was 0.93 in the UPMC test sets and 0.83 in external validation). Logistic predictions of awakening were systematically more pessimistic than Cox-based predictions for patients at higher risk of WLST-N, suggesting potential for self-fulfilling prophecies to arise when failure to awaken after WLST-N is considered as the ground truth outcome. CONCLUSIONS: Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.