Dataset Information

Joint Modeling of Longitudinal Markers and Time-to-Event Outcomes: An Application and Tutorial in Patients After Surgical Repair of Transposition of the Great Arteries.

ABSTRACT: Most patients with congenital heart disease survive into adulthood; however, residual abnormalities remain and management of the patients is life-long and personalized. Patients with surgical repair of transposition of the great arteries, for example, face the risk to develop neoaortic valve regurgitation. Cardiologists update the prognosis of the patient intuitively with updated information of the cardiovascular status of the patient, for instance from echocardiographic imaging. Usually a time-dependent version of the Cox model is used to analyze repeated measurements with a time-to-event outcome. New statistical methods have been developed with multiple advantages, of which the most prominent one being the joint model for longitudinal and time-to-event outcome. In this tutorial, the joint modeling framework is introduced and applied to patients with transposition of the great arteries after surgery with a long-term follow-up, where repeated echocardiographic values of the neoaortic root are evaluated against the risk of neoaortic valve regurgitation. The data are analyzed with the time-dependent Cox model as benchmark method, and the results are compared with a joint model, leading to different conclusions. The flexibility of the joint model is shown by adding the growth rate of the neoaortic root to the model and adding repeated values of body surface area to obtain a multimarker model. Lastly, it is demonstrated how the joint model can be used to obtain personalized dynamic predictions of the event. The joint model for longitudinal and time-to-event data is an attractive method to analyze data in follow-up studies with repeated measurements. Benefits of the method include using the estimated natural trajectory of the longitudinal outcome, great flexibility through multiple extensions, and dynamic individualized predictions.

SUBMITTER: Baart SJ

PROVIDER: S-EPMC8598112 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo use 4D-flow MRI to describe systemic and non-systemic ventricular flow organisation and energy loss in patients with repaired d-transposition of the great arteries (d-TGA) and normal subjects.MethodsPathline tracking of ventricular volumes was performed using 4D-flow MRI data from a 1.5-T GE Discovery MR450 scanner. D-TGA patients following arterial switch (n = 17, mean age 14 ± 5 years) and atrial switch (n = 15, 35 ± 6 years) procedures were examined and compared with subjects with normal cardiac anatomy and ventricular function (n = 12, 12 ± 3 years). Pathlines were classified by their passage through the ventricles as direct flow, retained inflow, delayed ejection flow, and residual volume and visually and quantitatively assessed. Additionally, viscous energy losses (ELv) were calculated.ResultsIn normal subjects, the ventricular flow paths were well ordered following similar trajectories through the ventricles with very little mixing of flow components. The flow paths in all atrial and some arterial switch patients were more irregular with high mixing. Direct flow and delayed ejection flow were decreased in atrial switch patients' systemic ventricles with a corresponding increase in residual volume compared with normal subjects (p = 0.003 and p < 0.001 respectively) and arterial switch patients (p < 0.0001 and p < 0.001 respectively). In non-systemic ventricles, arterial switch patients had increased direct flow and decreased delayed ejection fractions compared to normal (p = 0.007 and p < 0.001 respectively) and atrial switch patients (p = 0.01 and p < 0.001 respectively). Regions of high levels of mixing of ventricular flow components showed elevated ELv.Conclusions4D-flow MRI pathline tracking reveals disordered ventricular flow patterns and associated ELv in d-TGA patients.Key points• 4D-flow MRI can be used to assess intraventricular flow dynamics in d-TGA patients. • d-TGA arterial switch patients mostly show intraventricular flow dynamics representative of normal subjects, while atrial switch patients show increased flow disorder and different proportions of intraventricular flow volumes. • Flow disruption and disorder increase viscous energy losses.

Dataset Information

Joint Modeling of Longitudinal Markers and Time-to-Event Outcomes: An Application and Tutorial in Patients After Surgical Repair of Transposition of the Great Arteries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets