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1.
Circulation ; 149(11): 807-821, 2024 03 12.
Artigo em Inglês | MEDLINE | ID: mdl-37929565

RESUMO

BACKGROUND: Randomized trials in obstructive coronary artery disease (CAD) have largely shown no prognostic benefit from coronary revascularization. Although there are several potential reasons for the lack of benefit, an underexplored possible reason is the presence of coincidental nonischemic cardiomyopathy (NICM). We investigated the prevalence and prognostic significance of NICM in patients with CAD (CAD-NICM). METHODS: We conducted a registry study of consecutive patients with obstructive CAD on coronary angiography who underwent contrast-enhanced cardiovascular magnetic resonance imaging for the assessment of ventricular function and scar at 4 hospitals from 2004 to 2020. We identified the presence and cause of cardiomyopathy using cardiovascular magnetic resonance imaging and coronary angiography data, blinded to clinical outcomes. The primary outcome was a composite of all-cause death or heart failure hospitalization, and secondary outcomes were all-cause death, heart failure hospitalization, and cardiovascular death. RESULTS: Among 3023 patients (median age, 66 years; 76% men), 18.2% had no cardiomyopathy, 64.8% had ischemic cardiomyopathy (CAD+ICM), 9.3% had CAD+NICM, and 7.7% had dual cardiomyopathy (CAD+dualCM), defined as both ICM and NICM. Thus, 16.9% had CAD+NICM or dualCM. During a median follow-up of 4.8 years (interquartile range, 2.9, 7.6), 1116 patients experienced the primary outcome. In Cox multivariable analysis, CAD+NICM or dualCM was independently associated with a higher risk of the primary outcome compared with CAD+ICM (adjusted hazard ratio, 1.23 [95% CI, 1.06-1.43]; P=0.007) after adjustment for potential confounders. The risks of the secondary outcomes of all-cause death and heart failure hospitalization were also higher with CAD+NICM or dualCM (hazard ratio, 1.21 [95% CI, 1.02-1.43]; P=0.032; and hazard ratio, 1.37 [95% CI, 1.11-1.69]; P=0.003, respectively), whereas the risk of cardiovascular death did not differ from that of CAD+ICM (hazard ratio, 1.15 [95% CI, 0.89-1.48]; P=0.28). CONCLUSIONS: In patients with CAD referred for clinical cardiovascular magnetic resonance imaging, NICM or dualCM was identified in 1 of every 6 patients and was associated with worse long-term outcomes compared with ICM. In patients with obstructive CAD, coincidental NICM or dualCM may contribute to the lack of prognostic benefit from coronary revascularization.


Assuntos
Cardiomiopatias , Doença da Artéria Coronariana , Insuficiência Cardíaca , Isquemia Miocárdica , Masculino , Humanos , Idoso , Feminino , Doença da Artéria Coronariana/complicações , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/epidemiologia , Cardiomiopatias/diagnóstico por imagem , Cardiomiopatias/epidemiologia , Cardiomiopatias/complicações , Insuficiência Cardíaca/epidemiologia , Insuficiência Cardíaca/complicações , Prognóstico
2.
J Chem Theory Comput ; 19(20): 7358-7370, 2023 Oct 24.
Artigo em Inglês | MEDLINE | ID: mdl-37791529

RESUMO

We propose a data-driven framework for identifying coarse-grained (CG) Lennard-Jones (LJ) potential parameters in confined systems for simple liquids. Our approach involves the use of a Deep Neural Network (DNN) that is trained to approximate the solution of the Inverse Liquid State (ILST) problem for confined systems. The DNN model inherently incorporates essential physical characteristics specific to confined fluids, enabling an accurate prediction of inhomogeneity effects. By utilizing transfer learning, we predict single-site LJ potentials of simple multiatomic liquids confined in a slit-like channel, which effectively replicate both the fluid structure and molecular force of the target All-Atom (AA) system when the electrostatic interactions are not dominant. In addition, we showcase the synergy between the data-driven approach and the well-known Bottom-Up coarse-graining method utilizing Relative-Entropy (RE) Minimization. Through the sequential utilization of these two methods, the robustness of the iterative RE method is significantly augmented, leading to a remarkable enhancement in convergence.

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