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
<p>Abstract</p> <p>Background</p> <p>Treatments 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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2008-06-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Online Access: | http://www.biomedcentral.com/1472-6947/8/27 |
| _version_ | 1811329167994650624 |
|---|---|
| author | Knuiman Matthew Mannan Haider R Hobbs Michael |
| author_facet | Knuiman Matthew Mannan Haider R Hobbs Michael |
| author_sort | Knuiman Matthew |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Treatments 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.</p> <p>Methods</p> <p>Selected 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.</p> <p>Results</p> <p>The 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.</p> <p>Conclusion</p> <p>There 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.</p> |
| first_indexed | 2024-04-13T15:39:02Z |
| format | Article |
| id | doaj.art-8f848fb4e14c4e869273d8f7e7a8788f |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-13T15:39:02Z |
| publishDate | 2008-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-8f848fb4e14c4e869273d8f7e7a8788f2022-12-22T02:41:12ZengBMCBMC Medical Informatics and Decision Making1472-69472008-06-01812710.1186/1472-6947-8-27Adapting a Markov Monte Carlo simulation model for forecasting the number of Coronary Artery Revascularisation Procedures in an era of rapidly changing technology and policyKnuiman MatthewMannan Haider RHobbs Michael<p>Abstract</p> <p>Background</p> <p>Treatments 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.</p> <p>Methods</p> <p>Selected 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.</p> <p>Results</p> <p>The 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.</p> <p>Conclusion</p> <p>There 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.</p>http://www.biomedcentral.com/1472-6947/8/27 |
| spellingShingle | Knuiman Matthew Mannan Haider R Hobbs Michael 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 BMC Medical Informatics and Decision Making |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | http://www.biomedcentral.com/1472-6947/8/27 |
| work_keys_str_mv | AT knuimanmatthew adaptingamarkovmontecarlosimulationmodelforforecastingthenumberofcoronaryarteryrevascularisationproceduresinaneraofrapidlychangingtechnologyandpolicy AT mannanhaiderr adaptingamarkovmontecarlosimulationmodelforforecastingthenumberofcoronaryarteryrevascularisationproceduresinaneraofrapidlychangingtechnologyandpolicy AT hobbsmichael adaptingamarkovmontecarlosimulationmodelforforecastingthenumberofcoronaryarteryrevascularisationproceduresinaneraofrapidlychangingtechnologyandpolicy |