Effect of two different feeding methods on preventing ventilator associated pneumonia in the paediatric intensive care unit (PICU): A randomised controlled study

BACKGROUND

For infants and children who require intubation in the paediatric intensive care unit (PICU), ventilator-associated pneumonia (VAP) is a significant cause of secondary morbidity and mortality linked with extended use of intubation. Nurses are primarily responsible for the prevention of VAP and there are a number of procedures that contribute towards this end. Although enteral nutrition has been reported to be effective in the prevention of VAP, this remains controversial.

OBJECTIVE

To compare and evaluate the effects of intermittent feeding through a nasogastric catheter with those of continuous feeding through a nasoduodenal catheter in preventing VAP in the PICU.

DESIGN

The research design was a randomised, controlled experimental study.

METHODS

Forty paediatric patients were randomised and divided into two groups of 20: one group for nasoduodenal (ND) feeding and the other for nasogastric (NG) feeding. Patients were assessed for the development of VAP using the clinical pulmonary infection score and Centers for Disease Control and Prevention criteria while working in accordance with the VAP prevention bundles introduced within the unit.

RESULTS

The incidence of paediatric VAP was 15%. The rate of VAP in patients who were ND fed was 10%, whereas the rate of VAP in patients who had NG feeding was 20%. No statistically significant difference was observed between the ND- and NG-fed patients (p=0.661).

CONCLUSION

Although the results of our study were not statistically significant, nasoduodenal feeding helped to reduce the incidence of VAP.

Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems.