The automated processing algorithm to correct the test result of serum neuron-specific enolase affected by specimen hemolysis

Introduction Serum neuron-specific enolase (NSE) is an important tumor marker for small cell lung cancer and neuroblastoma. However, the test of serum NSE compromised by specimen hemolysis is presented as a falsely higher result, which seriously disturbs clinical decision. This study aimed to establish a solution integrated with laboratory information system to clear the bias from hemolysis on serum NSE test. Methods The reference range of serum hemolysis index (HI) was first established, and specimen hemolysis rate was compared between HI test and visual observation. NSE concentration in serum pool with normal HI was spiked with serial diluted lysates from red blood cells to deduce individual corrective equation. The agreement between individual corrective equation and original NSE test was assayed by Bland and Altman plots. Results The high HI existed in 32.6% of specimens from patients. The NSE median of hemolyzed specimens was significant higher than the baseline (p = 0.038), while the corrected NSE median had no difference compared with the baseline (p = 0.757). The mean difference of corrected NSE and initial NSE was 1.92%, the SD of difference was 5.23%, and furthermore, the difference was independent of tendency of HI (Spearman r = -0.069, p = 0.640). The 95% confidence interval of mean difference (from -8.33% to 12.17%) was less than the acceptable bias range (+/- 20%). Conclusion The agreement between individual correction equation and NSE assay was satisfied. Our automated processing algorithm for serum NSE could provide efficient management of posttest data and correct positive bias from specimen hemolysis.

Automated summary

This study aimed to establish a solution integrated with laboratory information system to clear the bias from hemolysis on serum NSE test. The results showed that the agreement between individual correction equation and NSE assay was satisfactory, indicating the effectiveness of the automated processing algorithm in correcting positive bias from specimen hemolysis.