Comparison of chest compression interruption times across 2 automated devices: a randomized, crossover simulation study.

OBJECTIVE: The goal of this study was to compare chest compression interruption times required to apply, adjust, and remove 2 different automated chest compression (ACC) devices using the same evaluation protocol. METHODS: Twenty-nine registered nurses and respiratory therapists used 2 ACC devices in separate resuscitation scenarios involving a patient manikin simulating a 45-year-old man in cardiac arrest in his intensive care unit room. Device presentation was randomized, with half of the participants using LUCAS 2 in the first scenario and the other half using AutoPulse in the first scenario. RESULTS: The mean chest compression interruption time to apply the ACC device to the patient was significantly shorter for AutoPulse (mean [M] = 31.6 ± 8.44) than for LUCAS 2 (M = 39.1 ± 11.20; t(28) = 3.65, P = .001). The mean chest compression interruption time to remove the ACC device from the patient and resume manual compressions was also significantly shorter for AutoPulse (M = 6.5 ± 3.65) than for LUCAS 2 (M = 10.1 ± 3.97; t(26) = 3.36, P = .002). There was no difference in the mean chest compression interruption time to adjust the position of the ACC device on the patient between AutoPulse (M = 14.3 ± 5.24) and LUCAS 2 (M = 12.5 ± 3.89; t(23) = -1.45, P = .162). CONCLUSIONS: The results of this study trended in favor of AutoPulse. However, the interruption in chest compression to apply either device to the patient was notably longer than the maximum interruption time recommended by the American Heart Association.

Medical practices display power law behaviors similar to spoken languages.

BACKGROUND: Medical care commonly involves the apprehension of complex patterns of patient derangements to which the practitioner responds with patterns of interventions, as opposed to single therapeutic maneuvers. This complexity renders the objective assessment of practice patterns using conventional statistical approaches difficult. METHODS: Combinatorial approaches drawn from symbolic dynamics are used to encode the observed patterns of patient derangement and associated practitioner response patterns as sequences of symbols. Concatenating each patient derangement symbol with the contemporaneous practitioner response symbol creates "words" encoding the simultaneous patient derangement and provider response patterns and yields an observed vocabulary with quantifiable statistical characteristics. RESULTS: A fundamental observation in many natural languages is the existence of a power law relationship between the rank order of word usage and the absolute frequency with which particular words are uttered. We show that population level patterns of patient derangement: practitioner intervention word usage in two entirely unrelated domains of medical care display power law relationships similar to those of natural languages, and that-in one of these domains-power law behavior at the population level reflects power law behavior at the level of individual practitioners. CONCLUSIONS: Our results suggest that patterns of medical care can be approached using quantitative linguistic techniques, a finding that has implications for the assessment of expertise, machine learning identification of optimal practices, and construction of bedside decision support tools.