Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients

Poleszczuk, Jan; Dębowska, Małgorzata; Dąbrowski, Wojciech; Wójcik-Załuska, Alicja; Załuska, Wojciech; Waniewski, Jacek

doi:10.1371/journal.pcbi.1006417

Cited by 14 publications

(18 citation statements)

References 42 publications

(43 reference statements)

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“…Availability continuous arterial pressure recordings as well as ultrasound of the aorta may provide information regarding blood vessel stiffness (Mackenzie et al, 2002). Aortic flow may also be assessable from peripheral pulse measurements using pulse propagation modeling as performed by Poleszczuk et al (2018). While we have identified that these are some of the limitations of the presented work, the reader can appreciate that the effective clinical treatment of the patient based on the clinicians extensive experience was paramount, and acquiring research quality data was not targeted.…”

Section: Model Limitationsmentioning

confidence: 99%

Arterial Hypertension and Unusual Ascending Aortic Dilatation in a Neonate With Acute Kidney Injury: Mechanistic Computer Modeling

et al. 2019

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BackgroundNeonatal asphyxia caused kidney injury and severe hypertension in a newborn. An unusually dilatated ascending aorta developed. Dialysis and pharmacological treatment led to partial recovery of the ascending aortic diameters. It was hypothesized that the aortic dilatation may be associated with aortic stiffening, peripheral resistance, and cardiovascular changes. Mathematical modeling was used to better understand the potential causes of the hypertension, and to confirm our clinical treatment within the confines of the model’s capabilities.MethodsThe patient’s systolic arterial blood pressure showed hypertension. Echocardiographic exams showed ascending aorta dilatation during hypertension, which partially normalized upon antihypertensive treatment. To explore the underlying mechanisms of the aortic dilatation and hypertension, an existing lumped parameter hemodynamics model was deployed. Hypertension was simulated using realistic literature informed parameter values. It was also simulated using large parameter perturbations to demonstrate effects. Simulations were designed to permit examination of causal mechanisms. The hypertension inducing effects of aortic stiffnesses, vascular resistances, and cardiac hypertrophy on blood flow and pressure were simulated. Sensitivity analysis was used to stratify causes.ResultsIn agreement with our clinical diagnosis, the model showed that an increase of aortic stiffness followed by augmentation of peripheral resistance are the prime causes of realistic hypertension. Increased left ventricular elastance may also cause hypertension. Ascending aortic pressure and flow increased in the simultaneous presence of left ventricle hypertrophy and augmented small vessel resistance, which indicate a plausible condition for ascending aorta dilatation. In case of realistic hypertension, sensitivity analysis showed that the treatment of both the large vessel stiffness and small vessel resistance are more important in comparison to cardiac hypertrophy.Conclusion and DiscussionLarge vessel stiffness was found to be the prime factor in arterial hypertension, which confirmed the clinical treatment. Treatment of cardiac hypertrophy appears to provide significant benefit but may be secondary to treatment of large vessel stiffness. The quantitative grading of pathophysiological mechanisms provided by the modeling may contribute to treatment recommendations. The model was limited due to a lack of data suitable to permit model identification.

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Section: Model Limitationsmentioning

confidence: 99%

Arterial Hypertension and Unusual Ascending Aortic Dilatation in a Neonate With Acute Kidney Injury: Mechanistic Computer Modeling

et al. 2019

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“…these factors may interact with each other, and may affect the cardiovascular system, leading to hypertension, coronary heart disease, arrhythmia, cerebrovascular accident, congestive heart failure, and other cardiovascular diseases (12). Various studies on cardiovascular risk factors in dialysis patients have since been published, and several risk-prediction methods constructed (10,(13)(14)(15)(16)(17). Serum phosphorus (13,(18)(19)(20), calcium (10,19,20), WBC (21,22), CRP (10,16,23,24), and albumin (7,10,13,17) levels were also considered common risk predictors of cardiovascular events.…”

Section: Discussionmentioning

confidence: 99%

Development of long-term cardiovascular disease risk prediction model for hemodialysis patients with end-stage renal disease based on nomogram

You¹,

Gu²,

Chen³

et al. 2021

Ann Palliat Med

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Background: Chronic kidney disease (CKD) is a leading public health problem worldwide. Cardiovascular diseases are the primary cause of death in hemodialysis patients with CKD. Therefore, it is necessary to develop a simple risk assessment tool for cardiovascular events in hemodialysis patients with CKD.Methods: A cohort of 370 hemodialysis patients, who were recruited between January 2015 to September 2019 in south China, were involved in the present study. On the basis of routine blood test indicators and ultrasonic cardiogram parameters, the optimal parameter set was determined and a Cox proportional hazards model coupled with a nomogram was used to predict cardiovascular risk over 3, 5, and 10 years. Predictive performance was evaluated using Harrell's concordance index (C-index) and the area under the receiveroperating characteristic curve (AUROC). The results were validated using both 10-fold cross-validation and hold-out validation (70% training and 30% validation, repeated 100 times). Results:The optimal parameter set consisted of hypertension, diabetes mellitus, age, phosphate, triglyceride, C-reactive protein, white blood cells, and interventricular septum thickness. The timedependent AUROCs for predicting 3-, 5-, and 10-year cardiovascular event occurrence risk were 0.836, 0.845, and 0.869, respectively. The nomogram showed satisfactory prediction performance (C-index: 0.808, 95% confidence interval: 0.773-0.844) and was well-calibrated. The results were further confirmed by 10-fold cross-validation and hold-out validation (C-index: 0.794 and 0.798, respectively).Conclusions: On the basis of several easy-to-detect clinical parameters, we developed a simple and useful nomogram for predicting cardiovascular risk in long-term hemodialysis patients that is of potential value for clinical application.

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“…The patient-specific parameters can be obtained from imaging techniques such as magnetic resonance imaging, computed tomography and ultrasound. A properly personalized model can predict physiological or pathological status more accurately [109, 110]. The personalized modeling in the arterial system can play an increasingly key role in the development of medical instruments.…”

Section: Discussionmentioning

confidence: 99%

A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure

Zhou

Hao

et al. 2019

BioMed Eng OnLine

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The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised.

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Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients

Cited by 14 publications

References 42 publications

Arterial Hypertension and Unusual Ascending Aortic Dilatation in a Neonate With Acute Kidney Injury: Mechanistic Computer Modeling

Arterial Hypertension and Unusual Ascending Aortic Dilatation in a Neonate With Acute Kidney Injury: Mechanistic Computer Modeling

Development of long-term cardiovascular disease risk prediction model for hemodialysis patients with end-stage renal disease based on nomogram

A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure

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