Statistical methods for characterizing transfusion-related changes in regional oxygenation using near-infrared spectroscopy (NIRS) in preterm infants

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 shown 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 nonparametric statistical methods to analyze mesenteric rSO(2) (regional oxygenation) produced by NIRS to evaluate oxygenation in intestinal tissues and investigate oxygenation response to red blood cell transfusion (RBC) in preterm infants. Specifically, we present a mean area under the curve (MAUC) measure and a slope measure to capture the mean rSO(2) level and temporal trajectory of rSO(2), respectively. We develop estimation methods for the measures based on multiple imputation and spline smoothing and further propose novel nonparametric testing procedures to detect RBC-related changes in mesenteric oxygenation in preterm infants. Through simulation studies, we show that the proposed methods demonstrate improved accuracy in characterizing the mean level and changing pattern of mesenteric rSO(2) and also increased statistical power in detecting RBC-related changes, as compared with standard approaches. We apply our methods to a NIRS study in preterm infants receiving RBC transfusion from Emory University to evaluate the pre- and post-transfusion mesenteric oxygenation in preterm infants.