Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach

Abstract 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-yea...

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Autores principales:	Abbas Rezaianzadeh, Esmaeil Khedmati Morasae, Davood Khalili, Mozhgan Seif, Ehsan Bahramali, Fereidoun Azizi, Pezhman Bagheri
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Metabolic syndrome Markov-system dynamics Natural history Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/62cb5eb0e57a45e39d4b0be07ee9ca29
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spelling	oai:doaj.org-article:62cb5eb0e57a45e39d4b0be07ee9ca292021-11-28T12:38:50ZPredicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach10.1186/s12874-021-01456-x1471-2288https://doaj.org/article/62cb5eb0e57a45e39d4b0be07ee9ca292021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01456-xhttps://doaj.org/toc/1471-2288Abstract 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 R2 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, R2 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.Abbas RezaianzadehEsmaeil Khedmati MorasaeDavood KhaliliMozhgan SeifEhsan BahramaliFereidoun AziziPezhman BagheriBMCarticleMetabolic syndromeMarkov-system dynamicsNatural historyMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Metabolic syndrome Markov-system dynamics Natural history Medicine (General) R5-920
spellingShingle	Metabolic syndrome Markov-system dynamics Natural history Medicine (General) R5-920 Abbas Rezaianzadeh Esmaeil Khedmati Morasae Davood Khalili Mozhgan Seif Ehsan Bahramali Fereidoun Azizi Pezhman Bagheri Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach
description	Abstract 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 R2 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, R2 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.
format	article
author	Abbas Rezaianzadeh Esmaeil Khedmati Morasae Davood Khalili Mozhgan Seif Ehsan Bahramali Fereidoun Azizi Pezhman Bagheri
author_facet	Abbas Rezaianzadeh Esmaeil Khedmati Morasae Davood Khalili Mozhgan Seif Ehsan Bahramali Fereidoun Azizi Pezhman Bagheri
author_sort	Abbas Rezaianzadeh
title	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_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_sort	predicting the natural history of metabolic syndrome with a markov-system dynamic model: a novel approach
publisher	BMC
publishDate	2021
url	https://doaj.org/article/62cb5eb0e57a45e39d4b0be07ee9ca29
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Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach

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