Coupled waveform patterns in the arterial and venous fontan circulation are related to parameters of pulmonary, lymphatic and cardiac function

Ferrari, Margaret R.; Schäfer, Michal; Hunter, Kendall S.; Maria, Michael V. Di

doi:10.1016/j.ijcchd.2022.100429

Cited by 2 publications

(6 citation statements)

References 19 publications

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“…VVCR, mean catheterization pressures (mPAP and SVC mean pressure), VO2max, BNPmax and O2 saturations were also included in the outcomes analysis, all of which have been independently linked to Fontan circulation health and outcomes [16][17][18][19] . We have previously determined that biomarkers indicative of lymphatic function and PLE, specifically aspartate aminotransferase, alkaline phosphatase, cystatin-c and creatinine were associated with caval flow patterns [9], strongly indicating that these parameters may identify, or be a predictive of, which Fontan patients will experience circulatory failure.…”

Section: Clinical Biomarkersmentioning

confidence: 98%

“…Flow profile characteristics were assessed as described previously with slight modifications [9]. Flow waveforms were acquired by precise parallel segmentation of 2D phasecontrast image series in Circle CVi42 (Calgary, Canada).…”

Section: Flow Profile Analysismentioning

confidence: 99%

“…Principal component analysis (PCA) is a data reduction technique that is often applied to large data sets in research [3][4][5][6][7], although it has not yet been applied to the Fontan population outside of waveform analysis [8,9]. Examples where PCA has been utilized to find novel associations in large datasets that would have not have been amenable to more conventional statistical approaches include: Scientists have used PCA to identify patterns of inflammatory and adhesion molecules that contribute to muscle weakness acquired in the intensive care unit [4].…”

Section: Introductionmentioning

confidence: 99%

“…We have previously applied PCA to characterize non-pulsatile cavopulmonary flow waveforms [8,9]; here we expand that approach to include heterogenous biomarkers. Another benefit of using a PCA approach in clinical data analysis is inclusion of correlative/colinear parameters, of which many statistical outcomes models prohibit [5].…”

Section: Introductionmentioning

confidence: 99%

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Application of Principal Component Analysis to Heterogenous Fontan Registry Data Identifies Independent Contributing Factors to Decline

Ferrari,

Schäfer,

Hunter

et al. 2024

Preprint

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Single ventricle heart disease is a severe and life-threatening illness, and improvements in clinical outcomes of those with Fontan circulation have not yet yielded acceptable survival over the past two decades. Patients are at risk of developing a diverse variety of Fontan-associated comorbidities that ultimately requires heart transplant. Our observational cohort study goal was to determine if principal component analysis (PCA) applied to data collected from a substantial Fontan cohort can predict functional decline (N=140). Heterogeneous data broadly consisting of measures of cardiac and vascular function, exercise (VO2max), lymphatic biomarkers, and blood biomarkers were collected over 11 years at a single site; in that time, 16 events occurred that are considered here in a composite outcome measure. After standardization and PCA, principal components (PCs) representing >5% of total variance were thematically labeled based on their constituents and tested for association with the composite outcome. Our main findings suggest that the 6thPC (PC6), representing 7.1% percent of the total variance in the set, is greatly influenced by blood serum biomarkers and superior vena cava flow, is a superior measure of proportional hazard compared to EF, and displayed the greatest accuracy for classifying Fontan patients as determined by AUC. In bivariate hazard analysis, we found that models combining systolic function (EF or PC5) and lymphatic dysfunction (PC6) were most predictive, with the former having the greatest AIC, and the latter having the highest c-statistic. Our findings support our hypothesis that a multifactorial model must be considered to improve prognosis in the Fontan population.

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Section: Clinical Biomarkersmentioning

confidence: 98%

Section: Flow Profile Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Principal Component Analysis to Heterogenous Fontan Registry Data Identifies Independent Contributing Factors to Decline

Ferrari,

Schäfer,

Hunter

et al. 2024

Preprint

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“…The qualitative approach to categorize global ventricular strain–time patterns is ideal for patients with morphologically normal ventricle but is less ideal in patients with complex single ventricle anatomy. Our group has recently employed principal component analysis (PCA) as a method to study variations in physiologic signals such as flow-time curve patterns in Fontan circulation 9 , 10 . This approach provides an unbiased description of the most prevalent modes of standardized time-dependent signals, such as strain curves, which can later be associated with physiologic events occurring throughout the cardiac cycle.…”

Section: Introductionmentioning

confidence: 99%

Myocardial strain-curve deformation patterns after Fontan operation

Schäfer¹,

Mitchell²,

Frank³

et al. 2023

Sci Rep

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Myocardial deformation analysis by cardiac MRI (CMR) yielding global circumferential and longitudinal strain (GCS and GLS) is an increasingly utilized method to accurately quantify systolic function and predict clinical events in patients with Fontan circulation. The purpose of this study was to use principal component analysis (PCA) to investigate myocardial temporal deformation patterns derived from strain–time curves to learn about latent strain features beyond peak values. We conducted the study with specific attention to dominant single left or right ventricle (SLV and SRV) morphologies. Methods and Results: Patients remote from Fontan operation who underwent follow-up CMR were analyzed for standard volumetric and function hemodynamics including myocardial deformation parameters including GCS and GLS. We applied PCA to investigate in an unbiased fashion the strain–time curve morphology and to calculate patient specific shape scores. All variables were subjected to single variable Cox regression analysis to detect composite clinical outcome including death, heart transplant, protein losing enteropathy and plastic bronchitis. A total of 122 patients, (SLV = 67, SRV = 55) with a mean age of 12.7 years underwent comprehensive CMR analysis. The PCA revealed 3 primary modes of strain-curve variation regardless of single ventricle morphology and type of strain investigated. Principle components (PCs) described changes in (1) strain–time curve amplitude, (2) time-to-peak strain, and (3) post-systolic slope of the strain–time curve. Considering only SLV patients, GCS was only CMR variable predictive of clinical events (HR 1.46, p = 0.020). In the SRV group, significant CMR predictors of clinical events were derived indexed end-diastolic (HR 1.02, p = 0.023) and end-systolic (HR 1.03, p = 0.022) volumes, GCS (HR 1.91, p = 0.003) and its related first component score (HR 1.20, p = 0.005), GLS (HR 1.32, p = 0.029) and its third component score (HR 1.58, p = 0.017). CMR derived global strain measures are sensitive markers of clinical outcomes in patients with Fontan circulation, particularly in patients with the SRV morphology. Myocardial strain–time curve morphology specific to SLV and SRV patients inspired by unbiased PCA technique can further aid with predicting clinical outcomes.

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Coupled waveform patterns in the arterial and venous fontan circulation are related to parameters of pulmonary, lymphatic and cardiac function

Cited by 2 publications

References 19 publications

Application of Principal Component Analysis to Heterogenous Fontan Registry Data Identifies Independent Contributing Factors to Decline

Application of Principal Component Analysis to Heterogenous Fontan Registry Data Identifies Independent Contributing Factors to Decline

Myocardial strain-curve deformation patterns after Fontan operation

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