Handling confounding variables in statistical shape analysis - application to cardiac remodelling

Bernardino, Gabriel; Benkarim, Oualid; Garza, María Sanz‐de la; Prat‐González, Susanna; Bijnens, Bart; Crispi, F.; Sitges, Marta; Butakoff, Constantine; Craene, Mathieu De; Ballester, Miguel Ángel González

doi:10.1016/j.media.2020.101792

Cited by 10 publications

(16 citation statements)

References 33 publications

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“…Ventricular Shape Statistical Analysis Ventricular shape analysis followed a previously described framework. 20 This framework enables identification of the main regional shape pattern that encodes the differences between 2 populations and enables quantification of the amount of that pattern in each individual. Ventricular shape variability between the groups was assessed by principal component analysis applied to the end-diastolic surfaces obtained from CMR.…”

Section: Discussionmentioning

confidence: 99%

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Exercise Capacity in Young Adults Born Small for Gestational Age

et al. 2021

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Being born small for gestational age (SGA), approximately 10% of all births, is associated with increased risk of cardiovascular mortality in adulthood, but mechanistic pathways are unclear. Cardiac remodeling and dysfunction occur in fetuses SGA and children born SGA, but it is uncertain whether and how these changes persist into adulthood.OBJECTIVE To evaluate baseline cardiac function and structure and exercise capacity in young adults born SGA. DESIGN, SETTING, AND PARTICIPANTSThis cohort study conducted from January 2015 to January 2018 assessed a perinatal cohort born at a tertiary university hospital in Spain between 1975 and 1995. Participants included 158 randomly selected young adults aged 20 to 40 years born SGA (birth weight below the 10th centile) or with intrauterine growth within standard reference ranges (controls). Participants provided their medical history, filled out questionnaires regarding smoking and physical activity habits, and underwent incremental cardiopulmonary exercise stress testing, cardiac magnetic resonance imaging, and a physical examination, with blood pressure, glucose level, and lipid profile data collected. EXPOSURE Being born SGA. MAIN OUTCOMES AND MEASURES Cardiac structure and function assessed by cardiac magnetic resonance imaging, including biventricular end-diastolic shape analysis. Exercise capacity assessed by incremental exercise stress testing. RESULTS This cohort study included 81 adults born SGA (median age at study, 34.4 years [IQR, 30.8-36.7 years]; 43 women [53%]) and 77 control participants (median age at study, 33.7 years [interquartile range (IQR), 31.0-37.1 years]; 33 women [43%]). All participants were of White race/ethnicity and underwent imaging, whereas 127 participants (80% of the cohort; 66 control participants and 61 adults born SGA) completed the exercise test. Cardiac shape analysis showed minor changes at rest in right ventricular geometry (DeLong test z, 2.2098; P = .02) with preserved cardiac function in individuals born SGA. However, compared with controls, adults born SGA had lower exercise capacity, with decreased maximal workload (mean [SD], 180 [62] W vs 214 [60] W; P = .006) and oxygen consumption (median, 26.0 mL/min/kg [IQR, 21.5-33.5 mL/min/kg vs 29.5 mL/min/kg [IQR, 24.0-36.0 mL/min/kg]; P = .02). Exercise capacity was significantly correlated with left ventricular mass (ρ = 0.7934; P < .001).CONCLUSIONS AND RELEVANCE This cohort of young adults born SGA had markedly reduced exercise capacity. These results support further research to clarify the causes of impaired exercise capacity and the potential association with increased cardiovascular mortality among adults born SGA.

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Section: Discussionmentioning

confidence: 99%

“…19 Ventricular shape differences between those born SGA and controls were assessed by SSA. 7,10,20 Statistical shape analysis was used to compute a reference shape for each population (controls and SGA) separately to compare them. Next, the specific remodeling in each individual (biventricular remodeling score) was estimated.…”

Section: Key Pointsmentioning

confidence: 99%

Exercise Capacity in Young Adults Born Small for Gestational Age

et al. 2021

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“…The covariates used in this study to investigate the impact of diversity in the classification of ASD may have a confounding effect in how the brain scanning measurements are used to predict diagnostic categories. In fact, numerous earlier studies have tried to account for nuisance sources by deconfounding the imaging-derived variables in what is often thought as a data-cleaning step (Alfaro-Almagro et al, 2020; Bernardino et al, 2020; Bzdok et al, 2020; Snoek et al, 2019). For instance, considering the sex of the subjects, we could find that males are more prone to be classified as ASD than females.…”

Section: Methodsmentioning

confidence: 99%

The Cost of Untracked Diversity in Brain-Imaging Prediction

Benkarim

Paquola

Park

et al. 2021

Preprint

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Brain-imaging research enjoys increasing adoption of supervised machine learning for single-subject disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

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“…"Wearables" was the smallest single group with fou (14%) studies [20,25,28,37]. The final six (21%) unassigned studies [4,19,24,33,36,39] wer Of the 49 studies that remained, 1 study was excluded from the review due to issues with accessing the full manuscript, leaving 48 studies to be included for full-text readings and to form the dataset for this review. However, during the full-text readings, a further 20 studies were excluded: 16 were excluded as they were deemed to be not relevant to the review, and the other 4 were excluded due to concerns about their quality, i.e., being vague and having an unclear description of either their methodology or approach used to develop their models, how the evaluation criteria were presented, and why certain metrics were used over others.…”

Section: Study Subgroupsmentioning

confidence: 99%

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

Bellfield

Ortega‐Martorell

Lip

et al. 2022

JCDD

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Background: Intense training exercise regimes cause physiological changes within the heart to help cope with the increased stress, known as the “athlete’s heart”. These changes can mask pathological changes, making them harder to diagnose and increasing the risk of an adverse cardiac outcome. Aim: This paper reviews which machine learning techniques (ML) are being used within athlete’s heart research and how they are being implemented, as well as assesses the uptake of these techniques within this area of research. Methods: Searches were carried out on the Scopus and PubMed online datasets and a scoping review was conducted on the studies which were identified. Results: Twenty-eight studies were included within the review, with ML being directly referenced within 16 (57%). A total of 12 different techniques were used, with the most popular being artificial neural networks and the most common implementation being to perform classification tasks. The review also highlighted the subgroups of interest: predictive modelling, reviews, and wearables, with most of the studies being attributed to the predictive modelling subgroup. The most common type of data used was the electrocardiogram (ECG), with echocardiograms being used the second most often. Conclusion: The results show that over the last 11 years, there has been a growing desire of leveraging ML techniques to help further the understanding of the athlete’s heart, whether it be by expanding the knowledge of the physiological changes or by improving the accuracies of models to help improve the treatments and disease management.

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Handling confounding variables in statistical shape analysis - application to cardiac remodelling

Cited by 10 publications

References 33 publications

Exercise Capacity in Young Adults Born Small for Gestational Age

Exercise Capacity in Young Adults Born Small for Gestational Age

The Cost of Untracked Diversity in Brain-Imaging Prediction

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

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