Evidence map›Paper›PMID 18578858›Full record

ArticleBMC medical informatics and decision making2008

Adapting a Markov Monte Carlo simulation model for forecasting the number of coronary artery revascularisation procedures in an era of rapidly changing technology and policy.

Haider R Mannan, Matthew Knuiman, Michael Hobbs

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Haider R MannanDepartment of Epidemiology & Preventive Medicine, School of Public Health & Preventive Medicine, Monash University, The Alfred, Melbourne, Victoria 3004, Australia. haider.mannan@med.monash.edu.au
Matthew Knuiman
Michael Hobbs

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTreatments for coronary heart disease (CHD) have evolved rapidly over the last 15 years with considerable change in the number and effectiveness of both medical and surgical treatments. This period has seen the rapid development and uptake of statin drugs and coronary artery revascularization procedures (CARPs) that include Coronary Artery Bypass Graft procedures (CABGs) and Percutaneous Coronary Interventions (PCIs). It is difficult in an era of such rapid change to accurately forecast requirements for treatment services such as CARPs. In a previous paper we have described and outlined the use of a Markov Monte Carlo simulation model for analyzing and predicting the requirements for CARPs for the population of Western Australia (Mannan et al, 2007). In this paper, we expand on the use of this model for forecasting CARPs in Western Australia with a focus on the lack of adequate performance of the (standard) model for forecasting CARPs in a period during the mid 1990s when there were considerable changes to CARP technology and implementation policy and an exploration and demonstration of how the standard model may be adapted to achieve better performance.

methodsSelected key CARP event model probabilities are modified based on information relating to changes in the effectiveness of CARPs from clinical trial evidence and an awareness of trends in policy and practice of CARPs. These modified model probabilities and the ones obtained by standard methods are used as inputs in our Markov simulation model.

resultsThe projected numbers of CARPs in the population of Western Australia over 1995-99 only improve marginally when modifications to model probabilities are made to incorporate an increase in effectiveness of PCI procedures. However, the projected numbers improve substantially when, in addition, further modifications are incorporated that relate to the increased probability of a PCI procedure and the reduced probability of a CABG procedure stemming from changed CARP preference following the introduction of PCI operations involving stents.

conclusionThere is often knowledge and sometimes quantitative evidence of the expected impacts of changes in surgical practice and procedure effectiveness and these may be used to improve forecasts of future requirements for CARPs in a population.

Indexed as

Markov ChainsModels, StatisticalMonte Carlo MethodAdultAgedAge FactorsAngioplasty, Balloon, CoronaryAustraliaCohort StudiesCoronary Artery BypassCoronary DiseaseFemaleForecastingHealth PolicyHumansMale

Identifiers

PMID18578858
PMCPMC2443119

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.