The release of the cardiac troponin T (cTnT) in patients with acute myocardial infarc tion (AMI) has been analyzed through a methodology based on nonlinear mixed-effects (NME) models. The aim of this work concerns the investigation of any possible relationship between clin ical covariates and the dynamics of the release of cTnT to derive more detailed and useful clinical information for the correct treatment of these patients. An ad-hoc mechanistic model describing the biomarker release process after AMI has been devised, assessed, and exploited to evaluate the im pact of the available clinical covariates on the cTnT release dynamic. The following approach was tested on a preliminary dataset composed of a small number of potential clinical covariates: em ploying an unsupervised approach, and despite the limited sample size, dyslipidemia, a known risk factor for cardiovascular disease, was found to be a statistically significant covariate. By increasing the number of covariates considered in the model, and patient cohort, we envisage that this approach may provide an effective means to automatically classify AMI patients and to investigate the role of interactions between clinical covariates and cTnT releas
Analysis of a Cardiac-Necrosis-Biomarker Release in Patients with Acute Myocardial Infarction via Nonlinear Mixed-Effects Models / Procopio, Anna; De Rosa, Salvatore; Montefusco, Francesco; Canino, Giovanni; Merola, Alessio; Sabatino, Jolanda; Critelli, Claudia; Indolfi, Ciro; Amato, Francesco; Cosentino, Carlo. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 12:24(2022), pp. 1-17. [10.3390/app122413038]
Analysis of a Cardiac-Necrosis-Biomarker Release in Patients with Acute Myocardial Infarction via Nonlinear Mixed-Effects Models
Amato, Francesco;
2022
Abstract
The release of the cardiac troponin T (cTnT) in patients with acute myocardial infarc tion (AMI) has been analyzed through a methodology based on nonlinear mixed-effects (NME) models. The aim of this work concerns the investigation of any possible relationship between clin ical covariates and the dynamics of the release of cTnT to derive more detailed and useful clinical information for the correct treatment of these patients. An ad-hoc mechanistic model describing the biomarker release process after AMI has been devised, assessed, and exploited to evaluate the im pact of the available clinical covariates on the cTnT release dynamic. The following approach was tested on a preliminary dataset composed of a small number of potential clinical covariates: em ploying an unsupervised approach, and despite the limited sample size, dyslipidemia, a known risk factor for cardiovascular disease, was found to be a statistically significant covariate. By increasing the number of covariates considered in the model, and patient cohort, we envisage that this approach may provide an effective means to automatically classify AMI patients and to investigate the role of interactions between clinical covariates and cTnT releasFile | Dimensione | Formato | |
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