Specimen Quality in Clinical Chemistry: Preanalytical Variables and Mitigation

Preanalytical variables are the leading source of error in clinical chemistry testing, accounting for an estimated 60% to 70% of all problems occurring in laboratory diagnostics. These errors originate during specimen collection, handling, preparation, and storage, and they can produce inappropriate clinical decisions and unjustifiable increases in healthcare costs. This article provides laboratory students, technicians, researchers, and diagnostic professionals with a practical framework for identifying, preventing, and managing preanalytical errors, with particular attention to hemolysis, lipemia, and icterus.

The Scope of Preanalytical Error in Laboratory Diagnostics

The total testing process in laboratory medicine follows a continuous loop from clinician ordering to result interpretation, often described as the brain to brain cycle. Within this cycle, the preanalytical phase encompasses all steps that occur before the analytical measurement, including test ordering, patient preparation, specimen collection, transport, centrifugation, and storage. According to reliable data, preanalytical errors still account for nearly 60% to 70% of all problems occurring in laboratory diagnostics, most of them attributable to mishandling procedures during collection, handling, preparing, or storing the specimens. Although most of these errors would be intercepted before inappropriate reactions are taken, in nearly one fifth of the cases they can produce inappropriate investigations and unjustifiable increase in costs, while generating inappropriate clinical decisions and causing some unfortunate circumstances.

The clinical laboratory is responsible for reporting accurate and expeditious results, yet the preanalytical phase is directly related to the procedure of specimen collection and is mostly out of the direct control of the laboratory. Most preanalytical errors are related to human factors, which makes education and training programs for phlebotomy teams the most significant and necessary measures to reduce these errors. Standardization and monitoring of preanalytical variables is of foremost importance and is associated with the most efficient and well-organized laboratories, resulting in reduced operational costs and increased revenues.

At a Glance: Key Preanalytical Interferences and Their Effects

The table below summarizes the most common preanalytical interferences encountered in clinical chemistry, their primary sources, affected analytes, and recommended mitigation strategies.

Hemolysis: Mechanisms, Detection, and Management

Hemolysis is a common preanalytical interference in clinical biochemistry, affecting up to 3.3% of routine samples and contributing to approximately 60% of rejected specimens. It results from the release of intracellular constituents, such as hemoglobin and potassium, into the plasma due to cell membrane disruption. Hemolytic samples are a rather common and unfavorable occurrence in laboratory practice, as they are often considered unsuitable for routine testing due to biological and analytical interference.

Mechanisms of Hemolysis Interference

Hemolysis interference operates through several distinct mechanisms. The release of intracellular analytes from erythrocytes artificially elevates measured concentrations of substances that are present in high concentration within red blood cells. Additionally, hemoglobin itself can interfere with spectrophotometric measurements by absorbing light at wavelengths used in many analytical methods. Hemoglobin can also participate in chemical reactions that alter analyte measurements, and the dilutional effect of intracellular fluid can affect results for analytes present in lower concentrations within cells.

Research evaluating the influence of in vitro blood cell lysis on routine clinical chemistry testing found that hemolysis interference appeared to be approximately linearly dependent on the final concentration of blood-cell lysate in the specimen. This generated a consistent trend towards overestimation of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, creatine kinase (CK), iron, lactate dehydrogenase (LDH), lipase, magnesium, phosphorus, potassium, and urea, whereas mean values of albumin, alkaline phosphatase (ALP), chloride, gamma-glutamyltransferase (GGT), glucose, and sodium were substantially decreased.

Detection Thresholds and Clinical Significance

A critical finding from hemolysis research is that clinically meaningful variations of AST, chloride, LDH, potassium, and sodium were observed in specimens displaying mild or almost undetectable hemolysis by visual inspection, defined as serum hemoglobin below 0.6 g/L. This observation underscores the limitation of visual inspection as a reliable method for detecting hemolysis, particularly at low levels that can still produce clinically significant interference.

The hemolysis index (HI) is a spectrophotometric measurement used by modern analyzers to quantify the degree of hemolysis in a specimen. However, the thresholds defined by in vitro diagnostic (IVD) analyzers may not align with clinical needs or biological variation. A descriptive study comparing the hemolysis interference data of 72 clinical chemistry analytes across five major manufacturers found that the compliance of manufacturers with the CLSI guideline varied significantly. Siemens had the highest compliance, reporting hemolysis interference at two analyte levels in 46 of 71 cases, while BioSystems did not report analyte concentrations for any tested parameters. Criteria for significant interference was often missing, and in many cases, manufacturers used arbitrary thresholds, such as 10% bias. Acceptance criteria and reported bias values were inconsistent across manufacturers, with only Abbott and Siemens providing detailed bias data.

Practical Management of Hemolyzed Specimens

Given the limitations of manufacturer declarations, clinical laboratories must independently validate hemolysis thresholds for their specific analytical platforms. The following steps provide a practical approach to managing hemolyzed specimens:

1. Establish baseline hemolysis index thresholds for each analyte based on local validation studies and biological variation data.
2. Implement routine hemolysis index measurement on all chemistry specimens, also those with visible discoloration.
3. Develop clear protocols for specimen rejection versus result reporting with interpretive comments.
4. For irreplaceable specimens, such as neonatal samples, consider correction models where validated.
5. Document all hemolysis-related decisions and communicate with clinical teams when results are affected.

A specific-neonatal hemolysis correction model has been developed for accurate potassium assessment in blood samples with in vitro hemolysis. This prospective study analyzed 134 neonatal blood specimens with postnatal age of 7 days or less. Controlled hemolysis induction established the potassium-hemoglobin relationship, with specimens allocated to model development and validation cohorts. The training cohort established a neonatal-specific potassium release coefficient of 0.28 mmol/L per gram of hemoglobin and revealed a strong linear correlation between hemoglobin concentration and potassium elevation. Independent validation confirmed clinical utility, demonstrating comparable corrected and baseline potassium concentrations with excellent correlation. The model improved hypokalemia detection from 8.9% to 26.7%, effectively resolving pseudo-normalization artifacts.

Lipemia: Sources, Interference Mechanisms, and Control

Lipemia refers to the presence of excess lipids, particularly triglycerides and chylomicrons, in the specimen. This condition causes the sample to appear turbid or milky and can interfere with a wide range of analytical methods.

Sources of Lipemia

Lipemia most commonly results from specimen collection from non-fasting patients, particularly within several hours after a fatty meal. Other sources include parenteral lipid administration, certain metabolic conditions such as hypertriglyceridemia, and delayed processing that allows chylomicrons to accumulate. The degree of lipemia can vary substantially between patients and even between specimens from the same patient collected at different times.

Interference Mechanisms

Lipemic specimens interfere with laboratory measurements through several mechanisms. The turbidity caused by lipid particles scatters light, which affects spectrophotometric and turbidimetric assays. Lipids can also occupy volume in the specimen, leading to dilutional effects on measured analytes. Additionally, lipoproteins can bind to certain analytes, affecting their measurement, and lipid particles can interfere with separation processes such as centrifugation and filtration.

Detection and Quantification

The lipemia index is a spectrophotometric measurement that quantifies the degree of turbidity in a specimen. Modern analyzers can measure this index automatically and flag specimens that exceed established thresholds. However, the interpretation of lipemia indices requires consideration of the specific analytical methods being used, as different methods have varying susceptibility to lipemic interference.

Management Strategies

Several approaches can be used to manage lipemic specimens:

1. Collect specimens after an appropriate fasting period when clinically feasible.
2. Use ultracentrifugation to clear lipids from the specimen before analysis.
3. Employ lipid-clearing agents or solvents where validated for specific assays.
4. Use analytical methods that are resistant to lipemic interference.
5. Document the lipemia index and communicate with clinical teams when results may be affected.

Icterus: Bilirubin Interference and Spectral Overlap

Icterus refers to the presence of elevated bilirubin in the specimen, which imparts a yellow to orange color. This condition can arise from hemolytic disease, hepatic dysfunction, biliary obstruction, or neonatal jaundice.

Interference Mechanisms

Bilirubin interferes with laboratory measurements primarily through spectral overlap, as bilirubin absorbs light at wavelengths used in many analytical methods. This is particularly problematic for methods that use endpoint or fixed-time measurements without adequate blanking. Bilirubin can also participate in chemical reactions that alter analyte measurements, particularly in methods that involve oxidation-reduction reactions.

Detection and Quantification

The icterus index measures the degree of bilirubin interference in a specimen. Like hemolysis and lipemia indices, this measurement is typically performed spectrophotometrically by modern analyzers. The icterus index can be used to identify specimens that may produce unreliable results for bilirubin-sensitive analytes.

Management Strategies

Management of icteric specimens involves:

1. Using analytical methods with appropriate bilirubin blanking or resistance.
2. Employing bilirubin oxidase or other bilirubin-removal techniques where validated.
3. Interpreting results with consideration of the icterus index.
4. Communicating with clinical teams when bilirubin interference may affect result interpretation.

Cross-Interference Among Hemolysis, Icterus, and Lipemia Indices

A critical consideration in specimen quality assessment is that hemolysis, icterus, and lipemia can interfere with each other's measurement. Research using endogenous patient-derived samples has demonstrated significant cross-interference among these indices. High level hemolysis remained largely unaffected by other indices, but hemolysis in the range of 2.5 to 10.0 g/L caused a 20% to 150% false increase in lipemia and a 10% to 60% false decrease in icterus. Conversely, lipemia in the range of 25 to 100 index led to a 20% to 150% false increase in icterus and reduced low level of hemolysis by 10% to 30%. Similarly, icterus at 42.75 to 171 micromol/L resulted in a 15% to 45% false reduction in low levels of hemolysis.

These findings demonstrate that cross-interference among HIL indices can lead to misinterpretation if indices are assessed independently. A rule-based, threshold-dependent algorithm incorporating these interactions has been proposed to enhance accuracy in sample rejection decisions. Implementation of such integrated approaches can reduce unnecessary sample rejections and improve the reliability of specimen quality assessment.

Specimen Stability: Time, Temperature, and Tube Type Effects

The stability of analytes in specimens is influenced by multiple preanalytical variables, including the delay before processing, storage as whole blood or serum or plasma, the storage temperature, and the type of tube used for collection and storage.

Stability of Routine Biochemistry Analytes

A comprehensive study of the preanalytical stability of 81 analytes examined the variables of delay before processing, storage as whole blood or serum or plasma, storage temperature, and tube type. The mean difference between assays for samples from 10 subjects was calculated with samples being kept under different storage conditions and for different times between sampling and analysis, up to 24 hours for biochemistry, coagulation, and hematology, and up to 72 hours for hormonology. Most of the analytes investigated remained stable up to 24 hours under all storage conditions prior to centrifugation. However, some analytes were significantly affected either by delay, tube type, or temperature, such as potassium, inorganic phosphorus, magnesium, lactate dehydrogenase, glucose, lactate, mean corpuscular volume, mean corpuscular hemoglobin, activated partial thromboplastin time, insulin, C-peptide, parathyroid hormone, osteocalcin, C-telopeptide, and adrenocorticotropic hormone.

Recent Stability Data

A more recent study determined the preanalytical stability of 65 analytes in whole blood, serum, and plasma using a standardized approach. Blood samples were collected from 30 healthy volunteers into five vacutainers, including serum separator tubes, lithium heparin, K2-EDTA, or sodium fluoride with potassium oxalate. Several conditions were tested, including delayed centrifugation with storage of whole blood at room temperature for 8 hours, delayed centrifugation with storage of whole blood at room temperature or 4 degrees Celsius for 24 hours, and immediate centrifugation with storage of plasma or serum at room temperature for 24 hours. The majority of the analytes evaluated remained stable across all vacutainer types, temperatures, and timepoints tested. Glucose, potassium, and aspartate aminotransferase, among others, were significantly impacted by delayed centrifugation, having been found to be unstable in whole blood specimens stored at room temperature for 8 hours.

Practical Implications for Specimen Handling

These stability data have important practical implications for laboratory operations. Laboratories should establish clear protocols for specimen processing times based on the stability characteristics of the analytes being measured. For analytes that are particularly unstable, such as potassium, glucose, and certain hormones, rapid processing is essential. For other analytes that remain stable despite delayed processing, unnecessary repeat phlebotomy can be avoided, reducing patient discomfort and healthcare costs.

Preanalytical Variables in Specialized Testing

While routine clinical chemistry testing has well-established preanalytical considerations, specialized testing modalities present additional challenges that require specific attention.

Fecal Calprotectin Measurement

Fecal calprotectin is a non-invasive marker of gut inflammation frequently used to guide therapeutic decisions in patients with inflammatory bowel diseases. Each step of fecal calprotectin measurement can influence the results, leading to misinterpretations and potentially impacting the management of patients. There is high heterogeneity between fecal calprotectin measurements and no current method is universally accepted as a standard. An international consensus of 14 physicians with expertise in the field from 11 countries formulated nine statements addressing the pre-analytical and analytical phases of fecal calprotectin measurement. Based on the available evidence, quantitative tests should be preferred for measuring fecal calprotectin. Furthermore, fecal calprotectin measurement, if possible, should always be performed with the same method, and factors influencing fecal calprotectin levels should be taken into account when interpreting the results.

Plasma Proteomics

Bead-based enrichment is a promising strategy to improve depth in plasma proteomics by overcoming the dynamic range barrier. However, its robustness against pre-analytical variation has not been sufficiently characterized. A systematic evaluation of five plasma proteomics workflows, including three bead-based methods, a neat workflow, and a precipitation protocol, found that bead-based approaches enhance detection of low-abundance proteins but can be highly susceptible to systematic bias from platelet and peripheral blood mononuclear cell contamination. This can inflate results by thousands of proteins. A perchloric acid-based workflow showed resistance to erythrocyte and platelet-derived contamination. Centrifugation conditions, anticoagulant choice, and buffer-bead combinations modulate contamination profiles, and bias can be mitigated by optimized sample handling.

Cerebrospinal Fluid Biomarkers

Oligoclonal bands have long been a reliable marker of intrathecal IgG synthesis in multiple sclerosis, valued for their high diagnostic sensitivity, unique patient fingerprints, clonality differentiation, semi-quantitative analysis, and pre-analytic robustness. However, they present challenges in standardization, labor-intensity, method variability, examiner dependency, and limited data on non-IgG immunoglobulins. Quantitative kappa free light chain measurement provides rapid, examiner-independent, and cost-effective assessment across all immunoglobulin classes but might have lower specificity and lacked consensus on standardized interpretation in recent years. Both oligoclonal bands and kappa free light chains have unique strengths and limitations that complement each other, potentially serving as complementary markers for evaluating intrathecal immunoglobulin synthesis in multiple sclerosis diagnosis.

Insulin Measurement and Hemolysis

Hemolysis is a well-known preanalytical source of interference in insulin measurement, primarily due to the release of insulin-degrading enzyme from erythrocytes. Given the pH-dependent activity of insulin-degrading enzyme, research has investigated whether blood acidification could reduce hemolysis-induced insulin degradation. In serum samples, insulin concentrations declined progressively with increasing hemolysis, with the lowest hemolysis group already exhibiting a 14.1% decrease exceeding the desirable bias of 10.5%. At the highest hemolysis level, the reduction reached 90.8%. FC-Mix plasma maintained stable median insulin concentrations across all hemolysis levels, with biases ranging from -0.3% to -1.8%, within desirable bias limits. Direct citrate buffer acidification attenuated hemolysis-induced insulin decline, resulting in biases of -4.8% and -6.3% at 0.4 and 0.5 molar concentrations, respectively, below the desirable limits. Acidification of blood using FC-Mix tubes or direct citrate buffer addition markedly reduces hemolysis-induced negative interference in insulin measurement.

Quality Control Materials for Specimen Integrity Checks

Clinical laboratories rely on analyte-specific quality control materials to monitor test or instrument performance, but quality control materials evaluating specimen integrity checks are infrequently implemented. Using commercially available specimen integrity materials, a study evaluated the Bio-Rad Liquichek Serum Indices product on Roche cobas c701 analyzers at a large academic medical center. Target arbitrary values for the hemolysis, icterus, and lipemia quality control materials were 200, 20, and 500, respectively. Across four c701 instruments, all quality control materials performed well, with coefficients of variation of 1.76% or less for hemolysis, 4.51% for icterus, and 3.46% for lipemia quality control. The Bio-Rad Liquichek Serum Indices product can serve as an effective means of monitoring specimen integrity checks in a manner congruous with existing quality control programs.

Quality Improvement Interventions to Reduce Preanalytical Errors

Multiple studies have demonstrated that targeted quality improvement interventions can substantially reduce preanalytical errors and specimen rejection rates.

Phlebotomy Education and Competency Validation

A cross-sectional study conducted to investigate the types and frequencies of preanalytical errors in a hospital laboratory categorized errors into four main categories: rejected sample, error related to test ordering, misidentification, and others. Several activities were performed for quality improvement, including education and training programs for phlebotomy teams. The rates of preanalytical errors decreased from 0.42% in the pre-intervention period to 0.32% in the post-intervention period. The rejected sample category accounted for the highest rates in both periods. In the questionnaires, the overall average score after the intervention was 71.5, which was a significant increase from 46.0 in the pre-intervention period. Each clinical laboratory has various types of preanalytical errors due to the complexity of the healthcare environment, and targeted intervention including a quality improvement program and its continuous maintenance should be conducted to reduce preanalytical errors and to improve patient safety.

Multidisciplinary Quality Improvement Initiative

A quality improvement initiative aimed at reducing rejected laboratory samples observed significant numbers of rejected samples from the emergency department and inpatient units due to hemolysis. A total of 1.43% of the blood samples were rejected, which was considerably higher than the target of 0.4%. The project aimed to reduce the percentage of rejected blood samples by 50% in the emergency department and the coronary intensive care unit. The team identified preanalytical errors as the primary reason for rejections. A multidisciplinary team tested several changes, including phlebotomy education, competency validation by direct observations, the use of appropriate consumables for sampling, and physician education for proper orders. The percentage of rejected blood samples dropped from 1.43% to 0.47%, which was a statistically significant reduction. Using a quality improvement approach for the detailed analyses of specimen rejection rates and related issues helped to formulate efficient plans to target this issue.

Quality Control Circle Practices

Quality Control Circles have been introduced to medical institutions to improve process efficiency and reduce errors. A Quality Control Circle initiative implemented in a clinical laboratory from July 2021 to August 2022 comprised members from the clinical laboratory, nursing department, and administration. The initiative followed the Plan-Do-Check-Act cycle and involved multiple quality control methods, including flowchart analysis, Pareto analysis, and Fishbone diagrams. The monthly specimen rejection rate decreased from an average of 1.13% before the intervention to 0.27% after the intervention. The most significant factors contributing to specimen rejection were identified as lack of sample collection information and blood clotting. Targeted interventions, such as appointing specimen collection liaisons, establishing a quality control team, and providing training on blood collection procedures, were implemented. These measures resulted in a notable decrease in the proportion of rejected specimens due to the identified factors.

Laboratory Automation and Auto-Verification

Total laboratory automation has mechanized tube handling, sample preparation, and storage in general chemistry, immunoassay, hematology, and microbiology, and removed most of the tedious tasks involved in those processes. However, there are still many tasks that must be performed by humans who monitor the automation lines. The complexity of the automated laboratory is increasing through further platform consolidation and expansion of the reach of molecular genetics into the core laboratory space. This will likely require rapid implementation of enhanced real time quality control measures, and these solutions will generate a significantly greater number of failure flags. To capitalize on the benefits that an improved quality control process can deliver, it will be important to ensure that an automation process is implemented simultaneously with enhanced, real time quality control measures and auto-verification of patient samples in middleware. The best solution may be to automate those critical decisions that still require human intervention and therefore include quality control as an integral part of total laboratory automation.

Common Failure Patterns in Preanalytical Error Management

Despite the availability of evidence-based guidance, laboratories commonly encounter recurring failure patterns in preanalytical error management. Recognizing these patterns is essential for developing effective prevention strategies.

Inadequate Phlebotomy Technique

Improper venipuncture technique remains a leading cause of hemolysis and specimen rejection. Prolonged tourniquet application, excessive fist clenching, using needles that are too small, and applying excessive suction during collection can all cause hemolysis. Vigorous mixing of specimens after collection can also cause cell damage. Laboratories should ensure that all phlebotomy staff receive comprehensive training and undergo regular competency validation through direct observation.

Delayed Processing and Transport

Delayed centrifugation and processing can affect analyte stability, particularly for potassium, glucose, and certain enzymes. Specimens that remain at room temperature for extended periods before processing are at risk for cellular metabolism continuing, which can alter analyte concentrations. Laboratories should establish clear protocols for transport times and processing delays and monitor compliance with these protocols.

Inadequate Specimen Identification

Misidentification of specimens can lead to serious patient safety events. Errors can occur at collection, during transport, or at the time of analysis. Laboratories should implement robust patient identification procedures, including verification of patient identity at the time of collection and use of barcode technology where available.

Inappropriate Specimen Collection Tubes

Using the wrong collection tube for the requested tests can lead to erroneous results due to anticoagulant or additive interference. Laboratories should provide clear guidance on tube selection for each test and ensure that phlebotomy staff are trained on proper tube selection.

Incomplete Mixing of Anticoagulated Specimens

Failure to adequately mix specimens collected in anticoagulant tubes can lead to clot formation and specimen rejection. The most significant factors contributing to specimen rejection have been identified as lack of sample collection information and blood clotting. Laboratories should provide clear instructions on proper mixing procedures and monitor compliance.

Records and Measurements for Preanalytical Quality Monitoring

Effective management of preanalytical variables requires systematic documentation and monitoring of quality indicators. The following measurements should be tracked routinely:

1. Specimen rejection rate, categorized by reason for rejection
2. Hemolysis index distribution across specimen types and collection locations
3. Lipemia and icterus index distributions
4. Time from collection to receipt in laboratory
5. Time from receipt to centrifugation
6. Time from centrifugation to analysis
7. Frequency of misidentified specimens
8. Frequency of incorrect tube selection
9. Frequency of insufficient specimen volume
10. Turnaround time outliers and their causes

These measurements should be analyzed regularly to identify trends and areas for improvement. The preanalytical phase should be monitored using quality indicators that are harmonized across laboratories where possible. The European Federation of Clinical Chemistry and Laboratory Medicine working group for the preanalytical phase has developed strategies for harmonization of the preanalytical phase, and European surveys have examined how laboratories monitor this phase.

Biosafety Considerations in Specimen Handling

Laboratory personnel handling blood specimens must follow established biosafety protocols to prevent occupational exposure to bloodborne pathogens. The World Health Organization Laboratory Biosafety Manual provides guidance on safe handling of biological specimens, including proper use of personal protective equipment, safe sharps handling, and appropriate decontamination procedures. All laboratory staff should receive training on biosafety practices and demonstrate competency before handling patient specimens.

Specimen processing activities, including centrifugation, aliquoting, and analysis, can generate aerosols that may contain infectious agents. These activities should be performed in appropriate containment equipment where indicated. Specimen rejection and disposal procedures should also follow established biosafety protocols to prevent exposure during waste handling.

Professional Escalation Criteria

Laboratory professionals should escalate preanalytical issues to appropriate personnel when certain conditions are present. The following situations warrant escalation:

1. Recurrent hemolysis from a specific phlebotomist or collection location despite retraining
2. Specimen rejection rates exceeding established thresholds for consecutive months
3. Evidence of systematic errors in specimen identification
4. Discrepancies between manufacturer hemolysis interference claims and local validation results
5. Clinical complaints related to preanalytical errors
6. Quality control failures involving specimen integrity materials
7. Changes in analytical platforms that require reassessment of interference thresholds

Escalation should follow established institutional protocols and include documentation of the issue, analysis of contributing factors, and implementation of corrective actions.

Limitations of Current Knowledge and Practice

Several limitations in current knowledge and practice should be acknowledged. Manufacturer instructions for hemolysis interference often lack completeness and consistency, making it crucial for clinical laboratories to independently validate hemolysis thresholds. A harmonized approach, incorporating manufacturer data, research, and local validation, is essential to improve laboratory quality.

Preanalytical variables are not routinely documented in the biospecimen research literature. Future studies using biobanked biospecimens should describe in detail the preanalytical handling of biospecimens and analyze and interpret the results with regard to the effects of these variables. The quality of a study will depend on the integrity of the biospecimens, and preanalytical preparations should be planned with consideration of the effect on downstream analyses.

There is currently significant variability in the literature regarding the pre-analytical stability of various analytes. Standardized approaches to stability testing, such as those used in recent studies, can help reduce this variability and provide more reliable guidance for specimen handling.

Frequently Asked Questions

What is the difference between endogenous and exogenous interference in clinical chemistry testing?

Endogenous interference arises from substances naturally present in the patient's specimen, such as hemoglobin from hemolysis, bilirubin from icterus, or lipids from lipemia. Exogenous interference comes from substances introduced during collection or processing, such as anticoagulants, preservatives, or contaminants from collection tubes. Research has demonstrated significant analytical discrepancies between artificially simulated and genuine clinical samples, particularly in lipemic samples, highlighting the importance of using endogenous samples when validating interference.

How can laboratories determine appropriate hemolysis index thresholds for their analytical platforms?

Laboratories should not rely solely on manufacturer declarations, as these often lack completeness and consistency. A harmonized approach incorporating manufacturer data, research findings, and local validation is essential. Laboratories should perform their own interference studies using clinically relevant samples and compare results against biological variation criteria. The CLSI C56-A guideline provides a framework for hemolysis interference testing, and compliance with this guideline varies significantly among manufacturers.

What analytes are most affected by hemolysis and what is the direction of the effect?

Hemolysis causes overestimation of alanine aminotransferase, aspartate aminotransferase, creatinine, creatine kinase, iron, lactate dehydrogenase, lipase, magnesium, phosphorus, potassium, and urea. Mean values of albumin, alkaline phosphatase, chloride, gamma-glutamyltransferase, glucose, and sodium are substantially decreased. Clinically meaningful variations of AST, chloride, LDH, potassium, and sodium can occur even with mild or almost undetectable hemolysis.

Can hemolysis correction models be used for irreplaceable specimens?

Correction models can be useful for irreplaceable specimens, such as neonatal samples where repeat collection is difficult or impossible. A validated neonatal-specific model uses the potassium release coefficient of 0.28 mmol/L per gram of hemoglobin to correct for hemolysis-induced pseudohyperkalemia. However, correction models must be validated for the specific population and analytical platform before use, and results should be interpreted with caution.

How does lipemia interfere with laboratory measurements?

Lipemia interferes through light scattering caused by turbidity, which affects spectrophotometric and turbidimetric assays. Lipids can also occupy volume in the specimen, leading to dilutional effects, and lipoproteins can bind to certain analytes. The lipemia index quantifies the degree of turbidity, and management strategies include fasting collection, ultracentrifugation, and use of lipemia-resistant analytical methods.

What is the recommended approach to monitoring specimen integrity checks?

Laboratories should implement routine measurement of hemolysis, icterus, and lipemia indices on all chemistry specimens. Commercial quality control materials for serum indices can be used to monitor the performance of these measurements in a manner congruous with existing quality control programs. These materials have demonstrated acceptable performance with coefficients of variation of 1.76% or less for hemolysis, 4.51% for icterus, and 3.46% for lipemia.

How can cross-interference among hemolysis, icterus, and lipemia indices affect sample rejection decisions?

Cross-interference among HIL indices can lead to misinterpretation if indices are assessed independently. Hemolysis can cause false increases in lipemia and false decreases in icterus, while lipemia can cause false increases in icterus and reduce low levels of hemolysis. A rule-based, threshold-dependent algorithm incorporating these interactions can enhance accuracy in sample rejection decisions and reduce unnecessary sample rejections.

What quality improvement interventions have been shown to reduce specimen rejection rates?

Phlebotomy education, competency validation by direct observations, use of appropriate consumables, and physician education for proper orders have been shown to reduce specimen rejection rates. Quality Control Circle initiatives following the Plan-Do-Check-Act cycle have reduced monthly specimen rejection rates from 1.13% to 0.27%. Targeted interventions such as appointing specimen collection liaisons, establishing quality control teams, and providing training on blood collection procedures have been effective.

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References and Further Reading

• Laboratory Quality Management System Handbook. World Health Organization.
• Laboratory Biosafety Manual. World Health Organization.
• Assay Guidance Manual. National Center for Advancing Translational Sciences.
• Bioanalytical Method Validation Guidance. U.S. Food and Drug Administration.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Influence of hemolysis on routine clinical chemistry testing.. Clinical chemistry and laboratory medicine, 2006.
• Preanalytical quality improvement: from dream to reality.. Clinical chemistry and laboratory medicine, 2011.
• International consensus on methodological issues in standardization of fecal calprotectin measurement in inflammatory bowel diseases.. United European gastroenterology journal, 2021.
• Pre-analytical drivers of bias in bead-enriched plasma proteomics.. EMBO molecular medicine, 2025.
• Oligoclonal bands and kappa free light chains: Competing parameters or complementary biomarkers?. Autoimmunity reviews, 2025.
• Stability study of 81 analytes in human whole blood, in serum and in plasma.. Clinical biochemistry, 2012.
• Preanalytical variables affecting the integrity of human biospecimens in biobanking.. Clinical chemistry, 2015.
• The stability of 65 biochemistry analytes in plasma, serum, and whole blood.. Clinical chemistry and laboratory medicine, 2024.
• Cross-interference of hemolysis, icterus and lipemia: A study with real patient samples.. 2026.
• A comprehensive dataset comparing endogenous and exogenous HIL interference across diverse clinical immunoassays.. 2026.
• Selective Inhibition of Insulin-Degrading Enzyme Eliminates Hemolysis Interference in Serum Insulin Measurements. 2026.
• Hemolysis index. Can we uncritically trust manufacturer declarations?. 2026.
• Effect of blood acidification on hemolysis-induced insulin degradation.. 2026.
• A specific-neonatal hemolysis correction model for accurate potassium assessment in blood samples with &lt,i&gt,in vitro&lt,/i&gt, hemolysis.. 2026.
• The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance. Clinical Chemistry and Laboratory Medicine, 2022.
• Evaluation and Operationalization of Commercial Serum Indices Quality Control Material in the Clinical Laboratory.. Clinica chimica acta, international journal of clinical chemistry, 2021.
• Reduction of pre-analytical errors in the clinical laboratory at the University Hospital of Korea through quality improvement activities.. Clinical Biochemistry, 2019.
• A Quality Improvement Initiative to Reduce Rejected Laboratory Samples and Enhance Specimen Acceptability.. Joint Commission Journal on Quality and Patient Safety, 2021.
• Quality Control Circle Practices to Reduce Specimen Rejection Rates. Journal of Multidisciplinary Healthcare, 2024.
• Classification of reasons for rejection of biological specimens based on pre-preanalytical processes to identify quality indicators at a university hospital clinical laboratory in Turkey.. Clinical Biochemistry, 2014.
• Quality Assurance in the Clinical Virology Laboratory. Encyclopedia of Virology, 2021.
• European survey on preanalytical sample handling - Part 1: How do european laboratories monitor the preanalytical phase? on behalf of the european federation of clinical chemistry and laboratory medicine (EFLM) working group for the preanalytical phase (WG-PRE). Biochemia Medica, 2019.
• Laboratory handbook on preanalytical phase and its implementation in the clinical laboratory. Klinicka Biochemie A Metabolismus, 1997.
• The EFLM strategy for harmonization of the preanalytical phase. Clinical Chemistry and Laboratory Medicine, 2018.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.