Modeling Cerebral Blood Flow Dependence on Carbon Dioxide and Mean Arterial Blood Pressure in the Immature Brain With Accounting for the Germinal Matrix

Sidorenko, Irina; Turova, Varvara; Botkin, Nikolai D.; Eckardt, Laura; Alves-Pinto, Ana; Felderhoff-Müser, Ursula; Rieger‐Fackeldey, Esther; Kovtanyuk, Andrey E.; Lampe, Renée

doi:10.3389/fneur.2018.00812

Cited by 14 publications

(26 citation statements)

References 48 publications

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“…These are also important to evaluate the risk of brain bleeding. CBF values cannot however be obtained experimentally (with standard clinical measures) but need to be calculated via modelling [9].…”

Section: Discussionmentioning

confidence: 99%

“…This criterion for matching was chosen to make both control and affected groups comparable in terms of the physiological and anatomical factors that critically determine cerebral blood flow. The latter is influenced by the number and size of cerebral blood vessels [9], being dependent on brain weight, as estimated from birth weight [13]. Furthermore, the size of the GM, as the origin of cerebral hemorrhages, depends also on the gestational age and birth weight [14].…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the clinical parameters mentioned, the values taken from clinical records, we included also cerebral blood flow (CBF) as an additional parameter to train and validate the model. CBF values used here correspond to simulated values, derived using a mathematical model developed by the authors in a previous study [9]. Briefly, this model describes mathematically the dependence of CBF on different anatomical and physiological variables, and was adapted from a pre-existing model for the adult brain [17] to the brain of preterm infants.…”

Section: Methodsmentioning

confidence: 99%

“…Vessels' reactivity (the ability to constrict or dilate depending on the pCO 2 content in blood) is modeled by making the diameter of the vessels to depend on the deviation of the pCO 2 level from some nominal value. The myogenic autoregulation mechanism (the change in the diameter of vessels with blood pressure fluctuations) is taken into account by adjusting the model in accordance with published CBF measurements in preterm infants (see [9] for details). Despite the complexity of the model, the mathematical simulation of CBF has been also implemented in a user-friendly software interface not requiring deep mathematical knowledge.…”

Section: Methodsmentioning

confidence: 99%

“…By analyzing clinical records of important parameters in preterm infants, it is possible to retrospectively develop such prognostic models by finding the optimal combination of parameters that makes possible to separate preterm infants with, from those without, bleeding. Preliminary work on the association of reduced or elevated values of various clinical parameters, such as leukocyte (Leu) and thrombocyte (Thro) counts, hematocrit (Ht), C-reactive protein (CRP), arterial oxygen partial pressure (pO 2 ), blood oxygen saturation (SaO 2 ), arterial carbon dioxide partial pressure (pCO 2 ), blood acidity (pH), and mean arterial pressure (MAP), with the occurrence of cerebral hemorrhage, as well as the presence of correlations between some of the parameters, was carried out by the authors in [8,9]. ML-methods have been successfully used for diagnosis and prognosis of various diseases, including stroke (see, for example, [10]).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

et al. 2020

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Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identification of combinations of clinical factors to best differentiate preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral hemorrhage. In the current study, a Random Forest approach is applied to develop such models for extremely and very preterm infants (23-30 weeks gestation) based on data collected from a cohort of 229 individuals. The constructed models exhibit good prediction accuracy and might be used in clinical practice to reduce the risk of cerebral bleeding in prematurity.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

et al. 2020

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Assessing key clinical parameters before and after intraventricular hemorrhage in very preterm infants

et al. 2020

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Intraventricular cerebral hemorrhage (IVH) is one of the most severe complications of premature birth, potentially leading to lifelong disability. The purpose of this paper is the assessment of the evolution of three of the most relevant parameters, before and after IVH: mean arterial pressure (MAP), arterial carbon dioxide pressure (pCO 2), and cerebral blood flow (CBF). Clinical records of 254 preterm infants with a gestational age of 23-30 weeks, with and without a diagnosis of IVH, were reviewed for MAP and arterial pCO 2 in the period up to 7 days before and 3 days after IVH or during the first 10 days of life in cases without IVH. Conclusion: A statistically significant increase in pCO 2 and decrease in MAP in patients with IVH compared with those without were detected. Both the mean values and the mean absolute deviations of CBF were computed in this study, and the latter was significantly higher than in control group. High deviations of CBF, as well as hypercapnia and hypotension, are likely to contribute to the rupture of cerebral blood vessels in preterm infants, and consequently, to the development of IVH. What is Known: • The origin of IVH is multifactorial, but mean arterial pressure, carbon dioxide partial pressure, and cerebral blood flow are recognized as the most important parameters. • In premature infants, the autoregulation mechanisms are still underdeveloped and cannot compensate for cerebral blood flow fluctuations. What is New: • The numerical simulation of CBF is shown to be a promising approach that may be useful in the care of preterm infants. • The mean values of CBF before and after IVH in the affected group were similar to those in the control group, but the mean absolute deviations of CBF in the affected group before and after IVH were significantly higher than that in the control group. Keywords Intraventricular cerebral hemorrhage. Preterm infants. Immature brain. Mean arterial pressure. Arterial carbon dioxide pressure. Cerebral blood flow

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Accounting for arterial and capillary blood gases for calculation of cerebral blood flow in preterm infants

et al. 2022

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One of the most feared neurological complications of premature birth is intraventricular hemorrhage, frequently triggered by fluctuations in cerebral blood flow (CBF). Although several techniques for CBF measurement have been developed, they are not part of clinical routine in neonatal intensive care. A promising tool for monitoring of CBF is its numerical assessment using standard clinical parameters such as mean arterial pressure, carbon dioxide partial pressure (pCO2) and oxygen partial pressure (pO2). A standard blood gas analysis is performed on arterial blood. In neonates, capillary blood is widely used for analysis of blood gas parameters. The purpose of this study was the assessment of differences between arterial and capillary analysis of blood gases and adjustment of the mathematical model for CBF calculation to capillary values. The statistical analysis of pCO2 and pO2 values collected from 254 preterm infants with a gestational age of 23–30 weeks revealed no significant differences between arterial and capillary pCO2 and significantly lower values for capillary pO2. The estimated mean differences between arterial and capillary pO2 of 15.15 mmHg (2.02 kPa) resulted in a significantly higher CBF calculated for capillary pO2 compared to CBF calculated for arterial pO2. Two methods for correction of capillary pO2 were proposed and compared, one based on the mean difference and another one based on a regression model.Conclusion: Capillary blood gas analysis with correction for pO2 as proposed in the present work is an acceptable alternative to arterial sampling for the assessment of CBF. What is Known:• Arterial blood analysis is the gold standard in clinical practice. However, capillary blood is widely used for estimating blood gas parameters.• There is no significant difference between the arterial and capillary pCO2values, but the capillary pO2differs significantly from the arterial one. What is New:• The lower capillary pO2values yield significantly higher values of calculated CBF compared to CBF computed from arterial pO2measurements.• Two correction methods for the adjustment of capillary pO2 to arterial pO2that made the difference in the calculated CBF insignificant have been proposed.

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Modeling Cerebral Blood Flow Dependence on Carbon Dioxide and Mean Arterial Blood Pressure in the Immature Brain With Accounting for the Germinal Matrix

Cited by 14 publications

References 48 publications

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

Assessing key clinical parameters before and after intraventricular hemorrhage in very preterm infants

Accounting for arterial and capillary blood gases for calculation of cerebral blood flow in preterm infants

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