## 1 Introduction  
Current trends in the diagnostic field make use of increasingly sophisticated techniques based on the analysis of circulating biomarkers: these techniques, in addition to their specificity and sensitivity, are also minimally invasive since they are based on sample blood analysis. For example, several cardiac biomarkers are released into circulation following stress on cardiac function or damage to myocardial tissue. In particular, among these, the measurements of specific cardiac biomarkers, such as the cardiac troponins T (cTnT) and I (cTnI), are currently exploited to diagnose and confirm the presence of acute myocardial infarction (AMI): this condition represents one of the most frequent and severe acute coronary syndrome (ACS), associated with insufficient blood supply and, finally, death of the involved cardiac tissue, [1]. More recently, especially with the advent of the high-sensitivity tests for cardiac troponins, their use has extended beyond the diagnosis of AMI,to encompass clinical risk stratification and prognostic assessment, [2]. Several factors, such as gender, age, and circadian cycle, [3; 4; 5], influence the release of cTns. For this reason, in this work, we are interested in investigating and modeling the impact of such available clinical covariates on the release dynamic of cTns, in particular of cTnT, in order to derive more detailed and better performing simulation models. To this end, we exploit the flexible modeling approach of the nonlinear mixed-effects (NME) models to describe the response of a dynamic system by taking into account both the influence of fixed and random effects, with the possibility to include the effects of the covariates directly in the model. This approach is especially suitable to deal with repeated experimental measurements on different subjects, e.g., balanced and unbalanced longitudinal datasets, and with missing data, [6; 7]. Furthermore, NME models allow the exploitation of all the possible information available in the experimental set, in order to improve the quality of the model results. Similarly to the classic nonlinear regression approach, [8; 9; 10; 11], mixed-effects models find wide application in various biological fields and especially in pharmacokinetics. As an example, in [12], NME models are applied to collect clinical data on 59 newborns treated with phenobarbital, in the first 16 days after birth, in order to investigate how to improve the dosing phase of the drug, optimizing the therapy. Another popular trend concerns the application of NME to model the growth in cellular species, [13], and the dynamics of the concentration of molecular species in a biological compartment [14; 15]. In the clinical setting as well, the NME models show their great potential: e.g., [PERSON] et al., in [16], leveraged NME models to describe the reduction in viral load after the initiation of treatment and to evaluate the intra- and inter-patient variability in the HIV clinical trials. [PERSON] et al., in [17], applied ME models to identify key parameters in datasets of diabetic patients with foot ulcers: the identification of such parameters proved especially useful for diagnosis and treatment. In [18], [PERSON] et al. used the nonlinear semi-parametric and parametric ME model to detect different patterns of growth in the number of NSTEMI diagnoses, with the aim of monitoring the diffusion of new diagnostic techniques. As mentioned above, in this work, NME models are applied to analyze a dataset of patients subject to acute myocardial infarction with ST-segment elevation (STEMI). In particular, we are interested in evaluating the effect of the available clinical covariates on the evolution of cTnT plasmatic concentration, with the aim of identifying different sub-classes of STEMI patients. The main advantage of a computer-aided classification of patients may consist in the possibility to optimize both the diagnosis and the treatment of these patients and to maximize the added value of the use of cardiac-necrosis biomarkers to assess prognosis.