Dataset Information

A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.

ABSTRACT: Models that represent the mechanisms that initiate and sustain atrial fibrillation (AF) in the heart are computationally expensive to simulate and therefore only capture short time scales of a few heart beats. It is therefore difficult to embed biophysical mechanisms into both policy-level disease models, which consider populations of patients over multiple decades, and guidelines that recommend treatment strategies for patients. The aim of this study is to link these modelling paradigms using a stylised population-level model that both represents AF progression over a long time-scale and retains a description of biophysical mechanisms. We develop a non-Markovian binary switching model incorporating three different aspects of AF progression: genetic disposition, disease/age related remodelling, and AF-related remodelling. This approach allows us to simulate individual AF episodes as well as the natural progression of AF in patients over a period of decades. Model parameters are derived, where possible, from the literature, and the model development has highlighted a need for quantitative data that describe the progression of AF in population of patients. The model produces time series data of AF episodes over the lifetimes of simulated patients. These are analysed to quantitatively describe progression of AF in terms of several underlying parameters. Overall, the model has potential to link mechanisms of AF to progression, and to be used as a tool to study clinical markers of AF or as training data for AF classification algorithms.

SUBMITTER: Chang ET

PROVIDER: S-EPMC4829251 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.

Chang Eugene T Y ET Lin Yen Ting YT Galla Tobias T Clayton Richard H RH Eatock Julie J

PloS one 20160412 4

Models that represent the mechanisms that initiate and sustain atrial fibrillation (AF) in the heart are computationally expensive to simulate and therefore only capture short time scales of a few heart beats. It is therefore difficult to embed biophysical mechanisms into both policy-level disease models, which consider populations of patients over multiple decades, and guidelines that recommend treatment strategies for patients. The aim of this study is to link these modelling paradigms using a ...[more]

PMID: 27070920

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Project description:RationaleAs part of the End TB Strategy, the World Health Organization calls for low-tuberculosis (TB) incidence settings to achieve pre-elimination (<10 cases per million) and elimination (<1 case per million) by 2035 and 2050, respectively. These targets require testing and treatment for latent tuberculosis infection (LTBI).ObjectivesTo estimate the ability and costs of testing and treatment for LTBI to reach pre-elimination and elimination targets in California.MethodsWe created an individual-based epidemic model of TB, calibrated to historical cases. We evaluated the effects of increased testing (QuantiFERON-TB Gold) and treatment (three months of isoniazid and rifapentine). We analyzed four test and treat targeting strategies: (1) individuals with medical risk factors (MRF), (2) non-USB, (3) both non-USB and MRF, and (4) all Californians. For each strategy, we estimated the effects of increasing test and treat by a factor of 2, 4, or 10 from the base case. We estimated the number of TB cases occurring and prevented, and net and incremental costs from 2017 to 2065 in 2015 U.S. dollars. Efficacy, costs, adverse events, and treatment dropout were estimated from published data. We estimated the cost per case averted and per quality-adjusted life year (QALY) gained.Measurements and main resultsIn the base case, 106,000 TB cases are predicted to 2065. Pre-elimination was achieved by 2065 in three scenarios: a 10-fold increase in the non-USB and persons with MRF (by 2052), and 4- or 10-fold increase in all Californians (by 2058 and 2035, respectively). TB elimination was not achieved by any intervention scenario. The most aggressive strategy, 10-fold in all Californians, achieved a case rate of 8 (95% UI 4-16) per million by 2050. Of scenarios that reached pre-elimination, the incremental net cost was $20 billion (non-USB and MRF) to $48 billion. These had an incremental cost per QALY of $657,000 to $3.1 million. A more efficient but somewhat less effective single-lifetime test strategy reached as low as $80,000 per QALY.ConclusionsSubstantial gains can be made in TB control in coming years by scaling-up current testing and treatment in non-USB and those with medical risks.

Dataset Information

A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.

Publications

A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.

Similar Datasets

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