The Characterization of the Toll of Caring for Coronavirus Disease 2019 on ICU Nursing Staff.

OBJECTIVES: Coronavirus disease 2019 pandemic exercised a significant demand on healthcare workers. We aimed to characterize the toll of caring for coronavirus disease 2019 patients by registered nurses. DESIGN: An observational study of two registered nurses cohorts. SETTING: ICUs in a large academic center. SUBJECTS: Thirty-nine ICU registered nurses assigned to coronavirus disease 2019 versus noncoronavirus disease 2019 patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Skin temperature (t [°C]), galvanic skin stress response (GalvStress), blood pulse wave, energy expenditure (Energy [cal]), number of steps (hr-1), heart rate (min-1), and respiratory rate (min-1) were collected using biosensors during the shift. National Aeronautics and Space Administration Task Loading Index measured the subjective perception of an assignment load. Elevated skin temperatures during coronavirus disease 2019 shifts were recorded (ΔtCOVID vs tnon-COVID = +1.3 [°C]; 95% CI, 0.1-2.5). Registered nurses staffing coronavirus disease patients self-reported elevated effort (ΔEffortCOVID vs Effortnon-COVID = +28.6; 95% CI, 13.3-43.9) concomitant with higher energy expenditure (ΔEnergyCOVID vs Energynon-COVID = +21.5 [cal/s]; 95% CI, 4.2-38.7). Galvanic skin stress responses were more frequent among coronavirus disease registered nurse (ΔGalStressCOVID vs GalvStressnon-COVID = +10.7 [burst/hr]; 95% CI, 2.6-18.7) and correlated with self-reported increased mental burden (ΔTLXMentalCOVID vs ΔTLXMentalnon-COVID = +15.3; 95% CI, 1.0-29.6). CONCLUSIONS: There are indications that registered nurses providing care for coronavirus disease 2019 in the ICU reported increased thermal discomfort coinciding with elevated energy expenditure and a more pronounced self-perception of effort, stress, and mental demand.

Quantitative standardization of resident mouse behavior for studies of aggression and social defeat.

Territorial reactive aggression in mice is used to study the biology of aggression-related behavior and is also a critical component of procedures used to study mood disorders, such as chronic social defeat stress. However, quantifying mouse aggression in a systematic, representative, and easily adoptable way that allows direct comparison between cohorts within or between studies remains a challenge. Here, we propose a structural equation modeling approach to quantify aggression observed during the resident-intruder procedure. Using data for 658 sexually experienced CD-1 male mice generated by three research groups across three institutions over a 10-year period, we developed a higher-order confirmatory factor model wherein the combined contributions of latency to the first attack, number of attack bouts, and average attack duration on each trial day (easily observable metrics that require no specialized equipment) are used to quantify individual differences in aggression. We call our final model the Mouse Aggression Detector (MAD) model. Correlation analyses between MAD model factors estimated from multiple large datasets demonstrate generalizability of this measurement approach, and we further establish the stability of aggression scores across time within cohorts and demonstrate the utility of MAD for selecting aggressors which will generate a susceptible phenotype in social defeat experiments. Thus, this novel aggression scoring technique offers a systematic, high-throughput approach for aggressor selection in chronic social defeat stress studies and a more consistent and accurate study of mouse aggression itself.