Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants
Ying Guo, Yikai Wang, Terri Marin, Easley Kirk, Ravi M. Patel,, Cassandra D. Josephson

TL;DR
This paper introduces new statistical methods to analyze NIRS data for assessing intestinal oxygenation changes in preterm infants during blood transfusions, improving detection accuracy and power.
Contribution
The paper develops novel statistical measures and estimation techniques for analyzing mesenteric rSO2, enhancing the reliability of NIRS data interpretation in neonatal care.
Findings
Proposed MAUC and slope measures effectively characterize rSO2 levels and trends.
Methods show improved accuracy over standard approaches in simulations.
Application to clinical data demonstrates practical utility in detecting transfusion-related changes.
Abstract
Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that provides non-invasive and continuous evaluation of regional tissue oxygenation in real-time. In recent years, NIRS has show promise as a useful monitoring technology to help detect relative tissue ischemia that could lead to significant morbidity and mortality in preterm infants. However, some issues inherent in NIRS technology use on neonates, such as wide fluctuation in signals, signal dropout and low limit of detection of the device, pose challenges that may obscure reliable interpretation of the NIRS measurements using current methods of analysis. In this paper, we propose new statistical methods to analyse mesenteric rSO2 (regional oxygenation) produced by NIRS to evaluate oxygenation in intestinal tissues and investigate oxygenation response to red blood cell transfusion (RBC) in preterm infants. We present…
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