J P Mintzer1, B Parvez2, M Chelala2, G Alpan2, E F LaGamma2. 1. Department of Pediatrics, Division of Neonatal-Perinatal Medicine, Stony Brook Children's Hospital, Stony Brook, NY, USA. 2. Department of Pediatrics, Division of Newborn Medicine, The Regional Neonatal Intensive Care Unit, Maria Fareri Children's Hospital at Westchester Medical Center, Valhalla, NY, USA.
Abstract
OBJECTIVE: In extremely premature neonates, data concerning the normal baseline variability of near-infrared spectroscopy (NIRS)-derived regional oxygen saturation (rSO2) are lacking. We sought to determine: 1) the quiescent variability of cerebral, renal, and splanchnic rSO2 in clinically stable, undisturbed very low birth weight neonates and 2) the effects of different data averaging epochs on site-specific variability. STUDY DESIGN: In this prospective, observational study, neonates between 500 and 1250 g underwent seven days of continuous, real-time cerebral, renal, and splanchnic NIRS monitoring starting within the first seventy-two postnatal hours. Demographic, cardiopulmonary, bedside care, and rSO2 data were collected. rSO2 variability was analyzed utilizing data from quiescent periods identified using pre-specified stability criteria. Between- and within-monitoring site comparisons of data averaging methods were made utilizing ANOVA. RESULT: Twenty-four subjects (GA 27 ± 0.3 wk, birth weight 988 ± 34 g; mean ± SEM) were monitored. Coefficients of variation (CoVar = SD/mean) were calculated for each monitoring site using varied data averaging epochs. CoVar was lowest for cerebral, intermediate for renal, and highest for splanchnic rSO2 (P < 0.01). For renal and splanchnic sites, shorter epochs (5- and 15-min) resulted in significantly smaller CoVars [P < 0.01 and P < 0.05, respectively]. Splanchnic variability was highly dependent on epoch length, ranging from 16% over 5 min to 23% over 60 min. CONCLUSION: 1) rSO2 variability differs significantly between monitoring sites and 2) shorter data sampling epochs decrease rSO2 variability. These observations may assist clinicians in operationally defining minimally significant departures to enable medical decision making utilizing this monitoring technique.
OBJECTIVE: In extremely premature neonates, data concerning the normal baseline variability of near-infrared spectroscopy (NIRS)-derived regional oxygen saturation (rSO2) are lacking. We sought to determine: 1) the quiescent variability of cerebral, renal, and splanchnic rSO2 in clinically stable, undisturbed very low birth weight neonates and 2) the effects of different data averaging epochs on site-specific variability. STUDY DESIGN: In this prospective, observational study, neonates between 500 and 1250 g underwent seven days of continuous, real-time cerebral, renal, and splanchnic NIRS monitoring starting within the first seventy-two postnatal hours. Demographic, cardiopulmonary, bedside care, and rSO2 data were collected. rSO2 variability was analyzed utilizing data from quiescent periods identified using pre-specified stability criteria. Between- and within-monitoring site comparisons of data averaging methods were made utilizing ANOVA. RESULT: Twenty-four subjects (GA 27 ± 0.3 wk, birth weight 988 ± 34 g; mean ± SEM) were monitored. Coefficients of variation (CoVar = SD/mean) were calculated for each monitoring site using varied data averaging epochs. CoVar was lowest for cerebral, intermediate for renal, and highest for splanchnic rSO2 (P < 0.01). For renal and splanchnic sites, shorter epochs (5- and 15-min) resulted in significantly smaller CoVars [P < 0.01 and P < 0.05, respectively]. Splanchnic variability was highly dependent on epoch length, ranging from 16% over 5 min to 23% over 60 min. CONCLUSION: 1) rSO2 variability differs significantly between monitoring sites and 2) shorter data sampling epochs decrease rSO2 variability. These observations may assist clinicians in operationally defining minimally significant departures to enable medical decision making utilizing this monitoring technique.
Authors: Ying Guo; Yikai Wang; Terri Marin; Kirk Easley; Ravi M Patel; Cassandra D Josephson Journal: Stat Methods Med Res Date: 2018-07-12 Impact factor: 3.021
Authors: Fieke Terstappen; Nina D Paauw; Thomas Alderliesten; Jaap A Joles; Daniel C Vijlbrief; A Titia Lely; Petra M A Lemmers Journal: PLoS One Date: 2018-09-20 Impact factor: 3.240