Linking statistical shape models and simulated function in the healthy adult human heart

Abstract

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.

Statistical morphological analysis reveals characteristic paraspinal muscle asymmetry in unilateral lumbar disc herniation

“…Note that as SSMs offer statistically independent shape variations and partial correlation can remove the effects of covariates, each correlation in Table S2 represents an independent investigation. Thus, for our exploratory study, multiple comparison correction was not used, like previous statistical shape analysis studies 13 , 16 , 17 . However, we acknowledge that some of the discovered correlations with statistical significance may be subject to chance, and we will further validate these findings with more subjects in our future confirmatory studies.…”

“…Second, the technique allows visualization of individual shape variations, which offer intuitive assessment of the morphometric properties. This technique has been successfully employed to study phenotype-related shape variations and alterations in various organs of the human body, including the heart 13 , brain 14 , 15 , and bones 16 , providing valuable biomarkers for diagnosis and prognosis purposes. More recently, Deane et al 17 investigated the link between intrinsic lumbar spinal bone shape and lumbar disc degeneration using this technique.…”

Sex Differences in Drug-Induced Arrhythmogenesis

“…Prior to this, a critical and logical next step would be to validate our method using our own independent experiments with human adult cardiomyocytes, and ideally, healthy human volunteers. Ultimately, with a view toward precision cardiology, this sex-specific approach forms an important initial step toward identifying the optimal course of care for each individual patient based on personalized block-concentration characteristics and personalized cardiac heart models (Trayanova, 2018 ; Peirlinck et al, 2021 ; Rodero et al, 2021 ).…”

Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta

“…This is at the price of having a very low efficiency if the application of an acceptance function is compulsory; e.g., in the case of considering A µ more than half the feature vectors are disregarded. If we are considering a clinical scenario in which the biomarkers are the result of costly simulations (Rodero et al, 2021;Thamsen et al, 2021), this has to FIGURE 6 | Examples of four synthetic aortas with decreasing feasibility of the biomarkers according to the acceptance functions. From left to right: the first one is accepted by all of the criteria; the second one is rejected by A r , but not by the other acceptance functions; the third one is only accepted by A M and the last one is rejected by all the criteria.…”