Quantifying Neonatal Sucking Performance: Promise of New Methods

Gilson J Capilouto, Tommy J Cunningham, David R Mullineaux, Eleonora Tamilia, Christos Papadelis, Peter J Giannone, Gilson J Capilouto, Tommy J Cunningham, David R Mullineaux, Eleonora Tamilia, Christos Papadelis, Peter J Giannone

Abstract

Neonatal feeding has been traditionally understudied so guidelines and evidence-based support for common feeding practices are limited. A major contributing factor to the paucity of evidence-based practice in this area has been the lack of simple-to-use, low-cost tools for monitoring sucking performance. We describe new methods for quantifying neonatal sucking performance that hold significant clinical and research promise. We present early results from an ongoing study investigating neonatal sucking as a marker of risk for adverse neurodevelopmental outcomes. We include quantitative measures of sucking performance to better understand how movement variability evolves during skill acquisition. Results showed the coefficient of variation of suck duration was significantly different between preterm neonates at high risk for developmental concerns (HRPT) and preterm neonates at low risk for developmental concerns (LRPT). For HRPT, results indicated the coefficient of variation of suck smoothness increased from initial feeding to discharge and remained significantly greater than healthy full-term newborns (FT) at discharge. There was no significant difference in our measures between FT and LRPT at discharge. Our findings highlight the need to include neonatal sucking assessment as part of routine clinical care in order to capture the relative risk of adverse neurodevelopmental outcomes at discharge.

Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Figures

Figure 1
Figure 1
Diagram of nfant Feeding Solution (NFANT Labs, Atlanta, Georgia).
Figure 2
Figure 2
Suck pattern excerpt from a representative full-term subject. Points indicate the start, end, and peak of the suck events, generated from the custom suck algorithm employed in this study. Indicated suck features are used to generate metrics for analyses.
Figure 3
Figure 3
Comparison of coefficient of variation for peak amplitude (COVPkA), duration (COVD), frequency (COVF), and smoothness (COVSM) at initial exam (IE) and discharge exam (DE) for the high-risk preterm (HRPT) versus full-term (FT) group. *Significant differences in mean values for each exam in comparison to FT; +significant differences in mean values between HRPT and low-risk preterm (LRPT) groups; #changes in exams values within groups.

Source: PubMed

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