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Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach
BACKGROUND: Markov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history. METHODS: Data gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year perio...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627615/ https://www.ncbi.nlm.nih.gov/pubmed/34837958 http://dx.doi.org/10.1186/s12874-021-01456-x |
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author | Rezaianzadeh, Abbas Morasae, Esmaeil Khedmati Khalili, Davood Seif, Mozhgan Bahramali, Ehsan Azizi, Fereidoun Bagheri, Pezhman |
author_facet | Rezaianzadeh, Abbas Morasae, Esmaeil Khedmati Khalili, Davood Seif, Mozhgan Bahramali, Ehsan Azizi, Fereidoun Bagheri, Pezhman |
author_sort | Rezaianzadeh, Abbas |
collection | PubMed |
description | BACKGROUND: Markov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history. METHODS: Data gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year period from a cohort of 12,882 people was used to conduct the analyses. First, transition probabilities (TPs) between 12 components of MetS by Markov as well as control and failure rates of relevant interventions were calculated. Then, the risk of developing each component by 2036 was predicted once by a Markov model and then by a MSD model. Finally, the two models were validated and compared to assess their performance and advantages by using mean differences, mean SE of matrices, fit of the graphs, and Kolmogorov-Smirnov two-sample test as well as R(2) index as model fitting index. RESULTS: Both Markov and MSD models were shown to be adequate for prediction of MetS trends. But the MSD model predictions were closer to the real trends when comparing the output graphs. The MSD model was also, comparatively speaking, more successful in the assessment of mean differences (less overestimation) and SE of the general matrix. Moreover, the Kolmogorov-Smirnov two-sample showed that the MSD model produced equal distributions of real and predicted samples (p = 0.808 for MSD model and p = 0.023 for Markov model). Finally, R(2) for the MSD model was higher than Markov model (73% for the Markov model and 85% for the MSD model). CONCLUSION: The MSD model showed a more realistic natural history than the Markov model which highlights the importance of paying attention to this method in therapeutic and preventive procedures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01456-x. |
format | Online Article Text |
id | pubmed-8627615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86276152021-11-30 Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach Rezaianzadeh, Abbas Morasae, Esmaeil Khedmati Khalili, Davood Seif, Mozhgan Bahramali, Ehsan Azizi, Fereidoun Bagheri, Pezhman BMC Med Res Methodol Research BACKGROUND: Markov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history. METHODS: Data gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year period from a cohort of 12,882 people was used to conduct the analyses. First, transition probabilities (TPs) between 12 components of MetS by Markov as well as control and failure rates of relevant interventions were calculated. Then, the risk of developing each component by 2036 was predicted once by a Markov model and then by a MSD model. Finally, the two models were validated and compared to assess their performance and advantages by using mean differences, mean SE of matrices, fit of the graphs, and Kolmogorov-Smirnov two-sample test as well as R(2) index as model fitting index. RESULTS: Both Markov and MSD models were shown to be adequate for prediction of MetS trends. But the MSD model predictions were closer to the real trends when comparing the output graphs. The MSD model was also, comparatively speaking, more successful in the assessment of mean differences (less overestimation) and SE of the general matrix. Moreover, the Kolmogorov-Smirnov two-sample showed that the MSD model produced equal distributions of real and predicted samples (p = 0.808 for MSD model and p = 0.023 for Markov model). Finally, R(2) for the MSD model was higher than Markov model (73% for the Markov model and 85% for the MSD model). CONCLUSION: The MSD model showed a more realistic natural history than the Markov model which highlights the importance of paying attention to this method in therapeutic and preventive procedures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01456-x. BioMed Central 2021-11-27 /pmc/articles/PMC8627615/ /pubmed/34837958 http://dx.doi.org/10.1186/s12874-021-01456-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Rezaianzadeh, Abbas Morasae, Esmaeil Khedmati Khalili, Davood Seif, Mozhgan Bahramali, Ehsan Azizi, Fereidoun Bagheri, Pezhman Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach |
title | Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach |
title_full | Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach |
title_fullStr | Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach |
title_full_unstemmed | Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach |
title_short | Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach |
title_sort | predicting the natural history of metabolic syndrome with a markov-system dynamic model: a novel approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627615/ https://www.ncbi.nlm.nih.gov/pubmed/34837958 http://dx.doi.org/10.1186/s12874-021-01456-x |
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