MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1249 products
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75/2022 - 10/19/2022
Rea, F.; Savaré, L; Franchi, M.; Corrao, G; Mancia, G
Adherence to Treatment by Initial Antihypertensive Mono and Combination Therapies | Abstract | | BACKGROUND
Aim of our study was to compare adherence to antihypertensive drug
therapy between newly treated patients in whom monotherapy or a
2-drug single-pill combination (SPC) was initially dispensed.
METHODS
The 63,448 residents of Lombardy Region (Italy), aged 40–80 years,
who were newly treated with antihypertensive drugs during 2016,
were identified and followed for 1 year after the first prescription. The
outcome of interest was adherence to drug therapy that was measured
according to the “proportion of days covered” (PDC) criterion, i.e.,
the ratio between the number of days in which the drug was available
and the days of follow-up. Patients who had a PDC >75% and
<25% were defined as highly and poorly adherent to drug therapy,
respectively. Log-binomial regression models were fitted to compare
the propensity to treatment adherence between the initial therapeutic
strategies, after adjusting for baseline demographic and clinical
covariates.
RESULTS
About 46% and 17% of patients showed high and poor adherence, respectively.
Compared with patients under initial monotherapy (85%),
those who were initially treated with a SPC (15%) had higher propensity
to be highly adherent and a lower propensity to be poorly adherent
to antihypertensive treatment (risk ratio: 1.18, 95% confidence interval
1.16–1.21; 0.42, 0.39–0.45, respectively). This was the case regardless the
sex, the age, the patient clinical status, and with almost any type of SPC.
CONCLUSIONS
In a real-life setting, patients who were initially prescribed a 2-drug
SPC exhibited more frequently a good adherence to antihypertensive
treatment than those starting with a single drug.
NOTE: This work has been partially carried out while one of the authors (L. Savarè) was affiliated with MOX Laboratory |
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74/2022 - 10/19/2022
Salvador, M.; Regazzoni, F.; Dede', L.; Quarteroni, A.
Fast and robust parameter estimation with uncertainty quantification for the cardiac function | Abstract | | Parameter estimation and uncertainty quantification are crucial in computational cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Robust and efficient mathematical methods must be designed to fit many model parameters starting from a few, possibly non-invasive, noisy observations. Moreover, the effective clinical translation requires short execution times and a small amount of computational resources. In the framework of Bayesian statistics, we combine Maximum a Posteriori estimation and Hamiltonian Monte Carlo to find an approximation of model parameters and their posterior distributions. To reduce the computational effort, we employ an accurate Artificial Neural Network surrogate of 3D cardiac electromechanics model coupled with a 0D cardiocirculatory model. Fast simulations and minimal memory requirements are achieved by using matrix-free methods, automatic differentiation and automatic vectorization. Furthermore, we account for the surrogate modeling error and measurement error. We perform three different in silico test cases, ranging from the ventricular function to the entire cardiovascular system, involving whole-heart mechanics, arterial and venous circulation. The proposed method is robust when high levels of signal-to-noise ratio are present in the quantities of interest in combination with a random initialization of the model parameters in suitable intervals. As a matter of fact, by employing a single central processing unit on a standard laptop and a few hours of computations, we attain small relative errors for all model parameters and we estimate posterior distributions that contain the true values inside the 90% credibility regions. With these benefits, our approach meets the requirements for clinical exploitation, while being compliant with Green Computing practices. |
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73/2022 - 10/19/2022
Spreafico, M.; Gasperoni, F.; Barbati, G.; Ieva, F.; Scagnetto, A.; Zanier, L.; Iorio, A.; Sinagra, G.; Di Lenarda, A.
Adherence to disease-modifying therapy in patients hospitalized for HF: findings from a community-based study | Abstract | | Background. Previous studies of polypharmacy (PP) for Heart Failure (HF) patients were based on surveys of highly selected populations and methods for estimating adherence in PP have widely varied. Moreover, adherence has been evaluated only in terms of physician’s prescriptions.
Aim. Describe pharmacological guidelines compliance in a real-world HF cohort based on effective patient’s drugs purchases and estimate the impact of PP adherence on survival.
Methods and Results. Between 2009 and 2015, patients hospitalized with a HF diagnosis and with at least one purchase post-discharge of angiotensin-converting enzyme inhibitors, ACE, or angiotensin receptor blockers, ARB, or beta-blocking, BB, or anti-aldosterone agents, AA, were recruited from an administrative database. Adherence was evaluated using two measures (Proportion of Days Covered, PDC, Medical Possession Ratio, MPR). A new measure of PP adherence was introduced, the Patient Adherence Indicator (PAI). A Cox model was estimated to quantify impact of PAI on survival. The most common drug combination was ACE/ARB and BB (58.1%) and the less frequent was ARB and AA (11.5%). Triplet ACE/ARB, BB and AA was purchased at least once by 27.3% of patients. Mean daily dosages were inferior to the target dosages for all drugs. From 41% to 58% of patients showed a poor poly-adherence measured by the PAI index.
Conclusions. Patients assume daily doses lower than the target dosages. PDC and MPR showed differences related to the specific drugs classes but were not prognostically different when combined into the PAI index. Adjusting for patient’s characteristics and intermediate events, PP non-adherence was significantly associated with lower survival. |
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72/2022 - 10/19/2022
Spreafico, M.; Ieva, F.; Arlati, F.; Capello, F.; Fatone, F.; Fedeli, F.; Genalti, G.; Anninga, J.; Gelderblom, H.; Fiocco, M.
Novel longitudinal multiple overall toxicity score to quantify adverse events experienced by patients during chemotherapy treatment: a retrospective analysis of the MRC BO06 trial in osteosarcoma | Abstract | | Aim: This work is intended to study the evolution of chemotherapy-induced toxicity over treatment, introducing a new method for summarize multiple toxic adverse events (AEs), i.e., the longitudinal Multiple Overall Toxicity (MOTox) score. A retrospective analysis of patients from MRC BO06/EORTC 80931 Randomized Controlled Trial for osteosarcoma was conducted.
Methods: Patients were randomised to six cycles of conventional versus dose-intense regimens of doxorubicin and cisplatin. Non-haematological toxicity data were collected prospectively and graded according to the Common Terminology Criteria for Adverse Events (CTCAE). The MOTox score was defined by condensing the worst AE and the overall toxic condition, including a time dimension. Multivariate models were constructed to assess the evolution of high overall toxicity, examining cycle-by-cycle the impact of personalized characteristics, such as achieved chemotherapy dose, previous toxic events, or biochemical factors.
Results: The flexible longitudinal depiction of MOTox score represents the strength of our method. A cycle-by-cycle dimension allowed to reconstruct different evolution patterns over treatment, leading to informative ramifications on patients’ health statuses. Patient’s toxic history played an important role in the quality of life over therapy, showing an autoregressive impact of previous toxicity. Conventional regimen had to be preferred to dose-intense one in terms of toxic AEs in the first half of the treatment.
Conclusion: This study shows that working in this direction is a difficult but profitable approach. The flexibility of our method, added to a cooperation with medical staff, could lead to improvements in the definition of useful tools for health care assessment and treatment planning. |
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71/2022 - 10/19/2022
Calabrò, D.; Lupo Pasini, M.; Ferro, N.; Perotto, S.
A deep learning approach for detection and localization of leaf anomalies | Abstract | | The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets. |
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70/2022 - 10/19/2022
Andrini, D.; Balbi, V.; Bevilacqua, G.; Lucci, G.; Pozzi, G.; Riccobelli, D.
Mathematical modelling of axonal cortex contractility | Abstract | | The axonal cortex is composed of a regular structure of F-actin and spectrin able to contract thanks to myosin II motors. Such an active tension is of fundamental importance in controlling the physiological shape of axons. Recent experiments show that axons modulate the contraction of the cortex when subject to mechanical deformations, exhibiting a non-trivial coupling between the hoop and the axial active tension. However, the underlying mechanisms are still poorly understood. In this paper, we propose a continuum model of the axon based on the active strain theory. By using the Coleman-Noll procedure, we shed light on the coupling between the hoop and the axial active strain through the Mandel stress tensor. We propose a qualitative analysis of the system under the simplifying assumption of incompressibility, showing the existence of a stable equilibrium solution. In particular, our results show that the axon regulates the active contraction to maintain a homeostatic stress state. Finally, we propose numerical simulations of the model, using a more suitable compressible constitutive law. The results are compared with experimental data, showing an excellent quantitative agreement. |
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69/2022 - 10/19/2022
Franco, N.R; Manzoni, A.; Zunino, P.
Learning Operators with Mesh-Informed Neural Networks | Abstract | | Thanks to their universal approximation properties and new efficient training strategies, Deep Neural Networks are becoming a valuable tool for the approximation of mathematical operators. In the present work, we introduce Mesh-Informed Neural Networks (MINNs), a class of architectures specifically tailored to handle mesh based functional data, and thus of particular interest for reduced order modeling of parametrized Partial Differential Equations (PDEs). The driving idea behind MINNs is to embed hidden layers into discrete functional spaces of increasing complexity, obtained through a sequence of meshes defined over the underlying spatial domain. The approach leads to a natural pruning strategy which enables the design of sparse architectures that are able to learn general nonlinear operators. We assess this strategy through an extensive set of numerical experiments, ranging from nonlocal operators to nonlinear diffusion PDEs, where MINNs are compared to classical fully connected Deep Neural Networks. Our results show that MINNs can handle functional data defined on general domains of any shape, while ensuring reduced training times, lower computational costs, and better generalization capabilities, thus making MINNs very well-suited for demanding applications such as Reduced Order Modeling and Uncertainty Quantification for PDEs. |
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68/2022 - 10/19/2022
Orlando, G.; Benacchio, T.; Bonaventura, L.
An IMEX-DG solver for atmospheric dynamics simulations with adaptive mesh refinement | Abstract | | We present an accurate and efficient solver for atmospheric dynamics simulations that allows for non-conforming mesh refinement. The model equations are the conservative Euler equations for compressible flows. The numerical method is based on an h-adaptive Discontinuous Galerkin spatial discretization and on a second order Additive Runge Kutta IMEX method for time discretization, especially designed for low Mach regimes. The solver is implemented in the framework of the deal.II library, whose mesh refinement capabilities are employed to enhance efficiency. A number of numerical experiments based on classical benchmarks for atmosphere dynamics demonstrate the properties and advantages of the proposed method. |
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