Journal of pediatric surgery · 2017
A novel multimodal computational system using near-infrared spectroscopy predicts the need for ECMO initiation in neonates with congenital diaphragmatic hernia.
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Cruz Stephanie M, Lau Patricio E, Rusin Craig G, Style Candace C, Cass Darrell L, Fernandes Caraciolo J, Lee Timothy C, Rhee Christopher J, Keswani Sundeep, Ruano Rodrigo, Welty Stephen E, Olutoye Oluyinka O
Аннотация
BACKGROUND/PURPOSE: The purpose of this study was to develop a computational algorithm that would predict the need for ECMO in neonates with congenital diaphragmatic hernia (CDH).
METHODS: CDH patients from August 2010 to 2016 were enrolled in a study to continuously measure cerebral tissue oxygen saturation (cStO2) of left and right cerebral hemispheres. NIRS devices utilized were FORE-SIGHT, CASMED and INVOS 5100, Somanetics. Using MATLAB©, a data randomization function was used to deidentify and blindly group patient's data files as follows: 12 for the computational model development phase (6 ECMO and 6 non-ECMO) and the remaining patients for the validation phase.
RESULTS: Of the 56 CDH patients enrolled, 22 (39%) required ECMO. During development of the algorithm, a difference between right and left hemispheric cerebral oxygenation via NIRS (ΔHCO) was noted in CDH patients that required ECMO. Using ROC analysis, a ΔHCO cutoff >10% was predictive of needing ECMO (AUC: 0.92; sensitivity: 85%; and specificity: 100%). The algorithm predicted need for ECMO within the first 12h of life and at least 6h prior to the clinical decision for ECMO with 88% sensitivity and 100% specificity.
CONCLUSION: This computational algorithm of cerebral NIRS predicts the need for ECMO in neonates with CDH.
LEVEL OF EVIDENCE: II.
METHODS: CDH patients from August 2010 to 2016 were enrolled in a study to continuously measure cerebral tissue oxygen saturation (cStO2) of left and right cerebral hemispheres. NIRS devices utilized were FORE-SIGHT, CASMED and INVOS 5100, Somanetics. Using MATLAB©, a data randomization function was used to deidentify and blindly group patient's data files as follows: 12 for the computational model development phase (6 ECMO and 6 non-ECMO) and the remaining patients for the validation phase.
RESULTS: Of the 56 CDH patients enrolled, 22 (39%) required ECMO. During development of the algorithm, a difference between right and left hemispheric cerebral oxygenation via NIRS (ΔHCO) was noted in CDH patients that required ECMO. Using ROC analysis, a ΔHCO cutoff >10% was predictive of needing ECMO (AUC: 0.92; sensitivity: 85%; and specificity: 100%). The algorithm predicted need for ECMO within the first 12h of life and at least 6h prior to the clinical decision for ECMO with 88% sensitivity and 100% specificity.
CONCLUSION: This computational algorithm of cerebral NIRS predicts the need for ECMO in neonates with CDH.
LEVEL OF EVIDENCE: II.