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Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics.

ABSTRACT: Early detection of left ventricular remodeling (LVR) is crucial. While cardiac magnetic resonance (CMR) provides valuable information, it has limitations. Coronary angiography-derived fractional flow reserve (caFFR) and index of microcirculatory resistance (caIMR) offer viable alternatives. 157 patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention were prospectively included. 23.6% of patients showed LVR. Machine learning algorithms constructed three LVR prediction models: Model 1 incorporated clinical and procedural parameters, Model 2 added CMR parameters, and Model 3 included echocardiographic and functional parameters (caFFR and caIMR) with Model 1. The random forest algorithm showed robust performance, achieving AUC of 0.77, 0.84, and 0.85 for Models 1, 2, and 3. SHAP analysis identified top features in Model 2 (infarct size, microvascular obstruction, admission hemoglobin) and Model 3 (current smoking, caFFR, admission hemoglobin). Findings indicate coronary physiology and echocardiographic parameters effectively predict LVR in patients with STEMI, suggesting their potential to replace CMR.

SUBMITTER: Zheng W

PROVIDER: S-EPMC11004870 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics.

Zheng Wen W Guo Qian Q Guo Ruifeng R Guo Yingying Y Wang Hui H Xu Lei L Huo Yunlong Y Ai Hui H Que Bin B Wang Xiao X Nie Shaoping S

iScience 20240315 4

Early detection of left ventricular remodeling (LVR) is crucial. While cardiac magnetic resonance (CMR) provides valuable information, it has limitations. Coronary angiography-derived fractional flow reserve (caFFR) and index of microcirculatory resistance (caIMR) offer viable alternatives. 157 patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention were prospectively included. 23.6% of patients showed LVR. Machine learning algorithm ...[more]

PMID: 38600975

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Project description:BackgroundCardiac computed tomography (CCT) based computational fluid dynamics (CFD) allows to assess intracardiac flow features, which are hypothesized as an early predictor for heart diseases and may support treatment decisions. However, the understanding of intracardiac flow is challenging due to high variability in heart shapes and contractility. Using statistical shape modeling (SSM) in combination with CFD facilitates an intracardiac flow analysis. The aim of this study is to prove the usability of a new approach to describe various cohorts.Materials and methodsCCT data of 125 patients (mean age: 60.6 ± 10.0 years, 16.8% woman) were used to generate SSMs representing aneurysmatic and non-aneurysmatic left ventricles (LVs). Using SSMs, seven group-averaged LV shapes and contraction fields were generated: four representing patients with and without aneurysms and with mild or severe mitral regurgitation (MR), and three distinguishing aneurysmatic patients with true, intermediate aneurysms, and globally hypokinetic LVs. End-diastolic LV volumes of the groups varied between 258 and 347 ml, whereas ejection fractions varied between 21 and 26%. MR degrees varied from 1.0 to 2.5. Prescribed motion CFD was used to simulate intracardiac flow, which was analyzed regarding large-scale flow features, kinetic energy, washout, and pressure gradients.ResultsSSMs of aneurysmatic and non-aneurysmatic LVs were generated. Differences in shapes and contractility were found in the first three shape modes. Ninety percent of the cumulative shape variance is described with approximately 30 modes. A comparison of hemodynamics between all groups found shape-, contractility- and MR-dependent differences. Disturbed blood washout in the apex region was found in the aneurysmatic cases. With increasing MR, the diastolic jet becomes less coherent, whereas energy dissipation increases by decreasing kinetic energy. The poorest blood washout was found for the globally hypokinetic group, whereas the weakest blood washout in the apex region was found for the true aneurysm group.ConclusionThe proposed CCT-based analysis of hemodynamics combining CFD with SSM seems promising to facilitate the analysis of intracardiac flow, thus increasing the value of CCT for diagnostic and treatment decisions. With further enhancement of the computational approach, the methodology has the potential to be embedded in clinical routine workflows and support clinicians.

Project description:BackgroundCardiac dysfunction and cardiovascular events are prevalent among patients with chronic kidney disease without overt obstructive coronary artery disease, but the mechanisms remain poorly understood. Coronary microvascular dysfunction has been proposed as a link between abnormal renal function and impairment of cardiac function and cardiovascular events. We aimed to investigate the relations between chronic kidney disease, coronary microvascular dysfunction, cardiac dysfunction, and adverse cardiovascular outcomes.MethodsPatients undergoing cardiac stress positron emission tomography, echocardiogram, and renal function ascertainment at Brigham and Women's Hospital were studied longitudinally. Patients free of overt coronary (summed stress score <3 and without a history of ischemic heart disease), valvular, and end-organ disease were followed up for the adverse composite outcome of death or hospitalization for myocardial infarction or heart failure. Coronary flow reserve (CFR) was determined from positron emission tomography. Echocardiograms were used to measure cardiac mechanics: diastolic (lateral and septal E/e') and systolic (global longitudinal, radial, and circumferential strain). Image analyses and event adjudication were blinded. The associations between estimated glomerular filtration rate (eGFR), CFR, diastolic and systolic indices, and adverse cardiovascular outcomes were assessed in adjusted models and mediation analyses.ResultsOf the 352 patients (median age, 65 years; 63% female; 22% black) studied, 35% had an eGFR <60 mL·min-1·1.73 m-2, a median left ventricular ejection fraction of 62%, and a median CFR of 1.8. eGFR and CFR were associated with diastolic and systolic indices, as well as future cardiovascular events (all P<0.05). In multivariable models, CFR, but not eGFR, was independently associated with cardiac mechanics and cardiovascular events. The associations between eGFR, cardiac mechanics, and cardiovascular events were partly mediated via CFR.ConclusionsCoronary microvascular dysfunction, but not eGFR, was independently associated with abnormal cardiac mechanics and an increased risk of cardiovascular events. Coronary microvascular dysfunction may mediate the effect of chronic kidney disease on abnormal cardiac function and cardiovascular events in those without overt coronary artery disease.

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Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics.

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Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics.

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