Mild Hemolysis in Critical Analytes: Practical Thresholds for Rejection or Acceptance

Abstract

Mild hemolysis represents one of the most frequent sources of preanalytical interference in clinical laboratories, particularly when it affects critical analytes whose alteration may trigger immediate and high-impact therapeutic decisions. The absence of standardized criteria to define practical rejection or acceptance thresholds generates decision variability, increases the risk of pseudo-interferences, and may compromise patient safety. In this context, the objective of this study was to establish operational thresholds supported by technical evidence and advanced statistical modeling to objectively classify mildly hemolyzed samples involving critical analytes. The methodology followed a documentary and methodological design based on a systematic review of governmental reports, national and international regulatory bodies, and specialized scientific literature. A decision matrix by analyte was developed, and advanced statistical procedures were applied, including ROC curve analysis with the Youden index, Deming regression, Shapiro–Wilk normality testing, and nonparametric correlations to model the relationship between hemolysis index and analytical deviation. The findings demonstrated that mild hemolysis should not be managed as a binary event but rather as a graded phenomenon requiring differentiated technical decision zones—acceptance, conditional release, or rejection—based on an optimal balance between diagnostic sensitivity and analytical specificity. Statistical modeling enabled the identification of optimal cut-off points that reduce the risk of pseudohyperkalemia and other clinically relevant interferences, thereby strengthening post-analytical consistency. Accordingly, the integration of automated rules and documented criteria within the laboratory information system enhances traceability, reduces interprofessional variability, and reinforces patient safety within a structured quality management and clinical governance framework.