BACKGROUND: Accuracy of serum neuron-specific enolase (NSE) measurement is paramount, particularly in the context of neurological outcome prognostication. However, NSE measurements are compromised by even slight hemolysis, as it is abundant in red blood cells (RBCs). We derived and validated an individualized hemolysis correction equation in an attempt to reduce the current rejection rate of 14% at our institution. METHODS: Intracellular NSE was measured in RBC lysates to determine concentration variability. A correction equation was derived, accounting for both RBC-derived NSE false-elevation and hemoglobin-derived signal quenching. The performance of this individualized correction was evaluated in intentionally hemolyzed samples and accuracy was compared to a generalized correction. RESULTS: Significant inter-individual variability of RBC NSE was observed, with an almost two-fold range (15.7-28.5 ng NSE/mg Hb, p<0.001); intra-individual variability was insignificant. The individualized hemolysis correction equation derived: NSE(corr)=NSE(meas)-(Hb(serum))(NSE(RBCs/Hb))+0.0844(Hb(serum))+1.1 corrected 95% of the intentionally hemolyzed samples to within ±5 ng/ml of corresponding baseline NSE concentrations, compared to 74% using a generalized formula. CONCLUSIONS: The individualized hemolysis correction provides increased accuracy in the estimation of true serum NSE concentrations for hemolyzed samples, compared to a generalized approach, by accounting for inter-individual RBC NSE variability. Incorporating this correction should reduce sample rejection rates and overall health care costs.
BACKGROUND: Accuracy of serum neuron-specific enolase (NSE) measurement is paramount, particularly in the context of neurological outcome prognostication. However, NSE measurements are compromised by even slight hemolysis, as it is abundant in red blood cells (RBCs). We derived and validated an individualized hemolysis correction equation in an attempt to reduce the current rejection rate of 14% at our institution. METHODS: Intracellular NSE was measured in RBC lysates to determine concentration variability. A correction equation was derived, accounting for both RBC-derived NSE false-elevation and hemoglobin-derived signal quenching. The performance of this individualized correction was evaluated in intentionally hemolyzed samples and accuracy was compared to a generalized correction. RESULTS: Significant inter-individual variability of RBC NSE was observed, with an almost two-fold range (15.7-28.5 ng NSE/mg Hb, p<0.001); intra-individual variability was insignificant. The individualized hemolysis correction equation derived: NSE(corr)=NSE(meas)-(Hb(serum))(NSE(RBCs/Hb))+0.0844(Hb(serum))+1.1 corrected 95% of the intentionally hemolyzed samples to within ±5 ng/ml of corresponding baseline NSE concentrations, compared to 74% using a generalized formula. CONCLUSIONS: The individualized hemolysis correction provides increased accuracy in the estimation of true serum NSE concentrations for hemolyzed samples, compared to a generalized approach, by accounting for inter-individual RBC NSE variability. Incorporating this correction should reduce sample rejection rates and overall health care costs.
Authors: Min-Kyoo Shin; Edwin Vázquez-Rosa; Yeojung Koh; Matasha Dhar; Kalyani Chaubey; Coral J Cintrón-Pérez; Sarah Barker; Emiko Miller; Kathryn Franke; Maria F Noterman; Divya Seth; Rachael S Allen; Cara T Motz; Sriganesh Ramachandra Rao; Lara A Skelton; Machelle T Pardue; Steven J Fliesler; Chao Wang; Tara E Tracy; Li Gan; Daniel J Liebl; Jude P J Savarraj; Glenda L Torres; Hilda Ahnstedt; Louise D McCullough; Ryan S Kitagawa; H Alex Choi; Pengyue Zhang; Yuan Hou; Chien-Wei Chiang; Lang Li; Francisco Ortiz; Jessica A Kilgore; Noelle S Williams; Victoria C Whitehair; Tamar Gefen; Margaret E Flanagan; Jonathan S Stamler; Mukesh K Jain; Allison Kraus; Feixiong Cheng; James D Reynolds; Andrew A Pieper Journal: Cell Date: 2021-04-13 Impact factor: 41.582
Authors: Daniel G Weber; Katarzyna Gawrych; Swaantje Casjens; Alexander Brik; Martin Lehnert; Dirk Taeger; Beate Pesch; Jens Kollmeier; Torsten T Bauer; Georg Johnen; Thomas Brüning Journal: Dis Markers Date: 2017-02-21 Impact factor: 3.434
Authors: Eric Peter Thelin; Emma Jeppsson; Arvid Frostell; Mikael Svensson; Stefania Mondello; Bo-Michael Bellander; David W Nelson Journal: Crit Care Date: 2016-09-08 Impact factor: 9.097