Bmc Pediatr
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Multicenter Study Observational Study
Individual contextual factors in the validation of the Bernese pain scale for neonates: protocol for a prospective observational study.
The Bernese Pain Scale for Neonates (BPSN) is a multidimensional pain assessment tool that is already widely used in clinical settings in the German speaking areas of Europe. Recent findings indicate that pain responses in preterm neonates are influenced by individual contextual factors, such as gestational age (GA), gender and the number of painful procedures experienced. Currently, the BPSN does not consider individual contextual factors. Therefore, the aim of this study is the validation of the BPSN using a large sample of neonates with different GAs. Furthermore, the influence of individual contextual factors on the variability in pain reactions across GA groups will be explored. The results will be used for a modification of the BPSN to account for individual contextual factors in future clinical pain assessment in neonates. ⋯ Understanding and considering the influence of individual contextual factors on pain responses in a revised version of the BPSN will help the clinical staff to more appropriately assess pain in neonates, particularly preterm neonates hospitalized in NICUs. Pain assessment is a first step toward appropriate and efficient pain management, which itself is an important factor in later motor and cognitive development in this vulnerable patient population.
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Multicenter Study Observational Study
Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: study protocol.
Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities. A variety of objective measures have been proposed to better define the optimal time for extubation, but none have proven clinically useful. In a pilot study, investigators from this group have shown promising results from sophisticated, automated analyses of cardiorespiratory signals as a predictor of extubation readiness. The aim of this study is to develop an automated predictor of extubation readiness using a combination of clinical tools along with novel and automated measures of cardiorespiratory behavior, to assist clinicians in determining when extremely preterm infants are ready for extubation. ⋯ The results of this research will provide the quantitative evidence needed to assist clinicians in determining when to extubate a preterm infant with the highest probability of success, and could produce significant improvements in extubation outcomes in this population.