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Background: To meet the requirements imposed by the time-dependency of acute stroke therapies, it is necessary 1) to initiate structural and cultural changes in the breadth of stroke-ready hospitals and 2) to find new ways to train the personnel treating patients with acute stroke. We aimed to implement and validate a composite intervention of a stroke team algorithm and simulation-based stroke team training as an effective quality initiative in our regional interdisciplinary neurovascular network consisting of 7 stroke units.
Methods: We recorded door-to-needle times of all consecutive stroke patients receiving thrombolysis at seven stroke units for 3 months before and after a 2 month intervention which included setting up a team-based stroke workflow at each stroke unit, a train-the-trainer seminar for stroke team simulation training and a stroke team simulation training session at each hospital as well as a recommendation to take up regular stroke team trainings.
Results: The intervention reduced the network-wide median door-to-needle time by 12 minutes from 43,0 (IQR 29,8–60,0, n = 122) to 31,0 (IQR 24,0–42,0, n = 112) minutes (p < 0.001) and substantially increased the share of patients receiving thrombolysis within 30 minutes of hospital arrival from 41.5% to 59.6% (p < 0.001). Stroke team training participants stated a significant increase in knowledge on the topic of acute stroke care and in the perception of patient safety. The overall course concept was regarded as highly useful by most participants from different professional backgrounds.
Conclusions: The composite intervention of a binding team-based algorithm and stroke team simulation training showed to be well-transferable in our regional stroke network. We provide suggestions and materials for similar campaigns in other stroke networks.
Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities
(2021)
The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.