Key result
Centralized stroke workflow factors boost simulated thrombolysis rates by ~8 percentage points.
Why the study?
It remains largely unclear how to improve thrombolysis rates in decentralized acute ischemic stroke care compared to centralized stroke care.
Does simulating the implementation of centralized care success factors improve thrombolysis rates in decentralized community hospitals for acute ischemic stroke?
Does simulating the implementation of centralized care success factors improve thrombolysis rates in decentralized community hospitals for acute ischemic stroke?
Simulation modeling demonstrates that optimizing pre-hospital delays offers the highest yield for increasing thrombolysis delivery rates in decentralized stroke networks.
Simulation suggests pre-hospital optimization may raise thrombolysis rates in decentralized networks; hypothesis-generating and requires prospective validation.
BACKGROUND: Various studies demonstrate better patient outcome and higher thrombolysis rates achieved by centralized stroke care compared to decentralized care, i.e. community hospitals. It remains largely unclear how to improve thrombolysis rate in decentralized care. The aim of this simulation study was to assess the impact of previously identified success factors in a central model on thrombolysis rates and patient outcome when implemented for a decentral model. METHODS: Based on a prospectively collected dataset of 1084 ischemic stroke patients, simulation was used to replicate current practice and estimate the effect of re-organizing decentralized stroke care to resemble a centralized model. Factors simulated included symptom onset call to help, emergency medical services transportation, and in-hospital diagnostic workup delays. Primary outcome was proportion of patients treated with thrombolysis; secondary endpoints were good functional outcome at 90 days, Onset-Treatment-Time (OTT), and OTT intervals, respectively. RESULTS: Combining all factors might increase thrombolysis rate by 7.9%, of which 6.6% ascribed to pre-hospital and 1.3% to in-hospital factors. Good functional outcome increased by 11.4%, 8.7% ascribed to pre-hospital and 2.7% to in-hospital factors. The OTT decreased 17 minutes, 7 minutes ascribed to pre-hospital and 10 minutes to in-hospital factors. An increase was observed in the proportion thrombolyzed within 1.5 hours; increasing by 14.1%, of which 5.6% ascribed to pre-hospital and 8.5% to in-hospital factors. CONCLUSIONS: Simulation technique may target opportunities for improving thrombolysis rates in acute stroke. Pre-hospital factors proved to be the most promising for improving thrombolysis rates in an implementation study.
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Lahr et al. (2013) studied Acute ischemic stroke (n=1,084). Simulated implementation of centralized stroke care success factors vs. Current practice (decentralized stroke care) was evaluated on Proportion of patients treated with thrombolysis (absolute increase of 7.9%). Combining pre-hospital and in-hospital success factors from a centralized model in a decentralized simulation increased the thrombolysis rate by 7.9% compared to current practice.
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