Key result
A discrete event simulation model predicted that administering medications to reduce atheroma and clot growth rates starting at age 45 could reduce sudden cardiac deaths by 8- to 9-fold.
A novel discrete event simulation model suggests that early screening and aggressive treatment of atherosclerosis starting at age 45 could dramatically reduce the incidence of sudden cardiac death.
Answers are grounded in this paper and cite their sources.
Model-based prediction of early intervention benefit warrants prospective validation; leaves open real-world efficacy for sudden cardiac death prevention.
Sudden Cardiac Death (SCD) is responsible for at least 180,000 deaths a year and incurs an average cost of $286 billion annually in the United States alone. Herein, we present a novel discrete event simulation model of SCD, which quantifies the chains of events associated with the formation, growth, and rupture of atheroma plaques, and the subsequent formation of clots, thrombosis and on-set of arrhythmias within a population. The predictions generated by the model are in good agreement both with results obtained from pathological examinations on the frequencies of three major types of atheroma, and with epidemiological data on the prevalence and risk of SCD. These model predictions allow for identification of interventions and importantly for the optimal time of intervention leading to high potential impact on SCD risk reduction (up to 8-fold reduction in the number of SCDs in the population) as well as the increase in life expectancy.
No takes yet. Share an insight, caveat, or question.
Andreev et al. (2013) studied Sudden Cardiac Death. Medications reducing atheroma and clot growth rates vs. No treatment was evaluated on Number of sudden cardiac deaths (8- to 9-fold reduction). A discrete event simulation model predicted that administering medications to reduce atheroma and clot growth rates starting at age 45 could reduce sudden cardiac deaths by 8- to 9-fold.
Synapse has enriched 10 related papers. Consider them for comparative context: