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Erschienen in:
08.07.2020 | Short Communication
Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data
Erschienen in: European Journal of Pediatrics | Ausgabe 1/2021
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Despite robust evidence in favour of maintaining optimal oxygen saturation targets in the preterm infants, the titration of oxygen is largely dependent on manual observations and transcription. Similarly, notwithstanding the gaining popularity of non-invasive modalities like high-flow nasal therapy, the practices of weaning and escalating support are largely individualized and based on point of care observations. These are often erroneous and lack objectivity. Histogram analysis from patient monitors is an easy and objective way of quantifying vital parameters and their trends. We review the technology and evidence available behind this practice.
Conclusions: Though there are no randomized controlled trials on this practice solely, we identify several quality improvement studies implementing this into practice with benefit. We also cite studies which have implemented histogram analysis in methodology, thus concluding that this is a useful clinical tool worth incorporating into clinical practice to reduce manual errors and bring more objectivity into decisions.
What is Known: • The data from NeOProM (Neonatal Oxygenation Prospective Meta-analysis Collaboration study protocol) indicates that optimal saturation targets for preterm infants born < 28 weeks should be between 91 and 95%. • The most “failsafe” way of maintaining strict compliance to these limits is automated oxygen titration but this is not widely used or available and manual transcription and monitoring are susceptible to error and fatigue. | |
What is New: • Histogram analysis from patient monitors can provide intelligent data on respiratory monitoring and can be incorporated into algorithm to decide on weaning or escalation of respiratory support. • With appropriate training, histogram monitoring by nursing staff can limit fatigue of manual recording of data. |