Detecting and Handling Hemolysis Using Serum Indices

The majority of detected clinical laboratory errors originate in the preanalytic phase of testing. Hemolysis is one of the most prevalent preanalytic errors, often leading to rejected samples because of interference. Hemolysis, defined as the release of hemoglobin and other intracellular components from erythrocytes into serum or plasma, occurs upon damage to the cell membrane.

Most cases of hemolysis start with in vitro disruption of erythrocytes during blood collection. This includes vein trauma during puncture, insufficient drying of alcohol at the cleansed site prior to venipuncture, use of an incorrect needle size, under-filling of tubes, excessive tourniquet time, and syringe collections. Other in vitro causes of hemolysis include improper sample handling, transport, and storage. A variety of pathological conditions can cause in vivo hemolysis, including autoimmune hemolytic anemia, transfusion reactions, mechanical rupture of erythrocytes due to artificial heart valves, and the use of ventricular assist devices.

Hemolysis influences the accuracy and reliability of routine chemistry testing by two different mechanisms. The first is through the release of analytes found in high concentrations in erythrocytes. Two analytes greatly impacted by hemolysis are potassium and lactate dehydrogenase, in which their concentrations in erythrocytes are more than 20 times and 150 times higher than it is in serum, respectively.

The second mechanism by which hemolysis affects test accuracy and reliability is through interference from hemoglobin itself. Due to its red color, hemoglobin absorbs light between 340–440 nm and 540–580 nm, allowing it to interfere with assays across a broad range of wavelengths. Spectrophotometric measurement of direct bilirubin is a common example of this type of interference. However, multiple mechanisms of hemoglobin interference exist and may cause positive or negative bias in assays.

Traditionally, labs detected hemolysis by visual inspection of serum or plasma. Specimens with a light pink hue indicate slight hemolysis, whereas deep red specimens represent gross hemolysis. However, even with trained observers, visual assessment of the degree of hemolysis can be highly subjective and unreliable. 

Fortunately, serum indices on contemporary chemistry analyzers enable automated semi-quantitative assessments of hemolysis, icterus, and lipemia, thereby significantly improving the process for assessing specimen integrity. For example, a hemolysis index (HI) on the Roche cobas c 501 analyzer uses bichromatic wavelength pairs (600/570 nm) and calculation formulas that include corrections to compensate for the spectral overlap due to lipemia.

Assay manufacturers provide instrument- and test-­specific HI limits for interference in their instructions for use. Laboratories can use middleware rules to set and customize serum HI thresholds for each analyte. This enables laboratory technologists to readily identify specimens with clinically significant interferences prevent autoverification of results, and intervene as needed. Depending on the laboratory’s procedures, interventions might include re-collecting a specimen, searching for an alternate non-hemolyzed specimen collected at the same time, or reporting results with an interpretive comment that clearly indicates how results will be affected by hemolysis.

While manufacturer-provided HI limits serve as a starting point for determining specimen acceptability, they are typically defined using single analyte concentrations. In our laboratory, we have established analyte concentration-specific hemolysis thresholds for assays with low hemolysis limits, such as aspartate aminotransferase (AST). In the case of AST, we increase the HI threshold as the AST activity in the sample increases, because greater hemolysis is required before we observe a clinically significant bias. By implementing these analyte concentration-specific HI thres­holds, we have significantly reduced our specimen rejection rates and re-collection­ rates due to hemolysis.

Clinical laboratories commonly encounter hemolyzed specimens which, if not managed appropriately, influence the reliability of patient results. Consequently, labs need robust, systematic processes in place for identifying these samples and for consistently quantifying the degree of hemolysis. With automated hemolysis detection using HI and rule-based algorithms, labs can readily do so. Also, by establishing concentration-specific hemolysis thresholds, labs may realize significant drops in specimen rejection and re-collection rates. These changes offer additional benefits, including improved turnaround time and cost savings from reduced blood collections.


Brooke Katzman, PhD, is a clinical chemistry fellow at Mayo Clinic in Rochester, Minnesota.+Email: [email protected]

Nikola Baumann, PhD, is co-director of the Central Clinical Laboratory and director of central processing at Mayo Clinic in Rochester, Minnesota.
+Email: [email protected]