Abstract
Introduction:
Hemolysis is a common preanalytical interference in clinical laboratories, affecting up to 4% of blood samples. It can significantly impact the accuracy of test results, potentially leading to misdiagnosis and inappropriate treatment.
Objective:
To quantify the effects of hemolysis on routine clinical biochemistry parameters by comparing visual assessment with machine-based hemolysis indices (HIs).
Methodology:
This cross-sectional study was conducted at AIIMS, Kalyani, from 2021 to 2023. Residual serum samples were used to assess the impact of mechanically induced hemolysis on 40 biochemical parameters measured using a Vitros 4600 analyzer. A semiquantitative visual grading scale was developed and compared to the analyzer’s quantitative HI thresholds. Discrepancies in sample rejection rates between visual and automated methods were analyzed using the hospital’s electronic health record database.
Statistical Analysis:
Data analysis included calculating rejection rates and comparing differences between visual and HI-based methods for each analyte.
Results:
Visual assessment categorized most samples as “no hemolysis” (91.86%–100%), with mild hemolysis (1.43%–9.09%) being common and severe hemolysis (0.41%–0.62%) rare. HI-based detection showed higher rejection rates for analytes such as potassium (1.58% higher), phosphate (1.07%), and liver enzymes. Conversely, lower rejection rates were observed for creatinine (+0.75%), urea (+0.79%), and electrolytes. Discrepancies exceeding 0.5% were noted for frequently tested analytes, raising the concerns about the reliability of visual assessment for specific parameters.
Conclusion:
Optimizing hemolysis assessment protocols by aligning HI thresholds with analytical sensitivity and clinical needs shows promise for improving laboratory result accuracy and patient care.
Keywords: Hemolysis, hemolysis index, preanalytical error, sample rejection
Résumé
Introduction:
L’hémolyse est une interférence pré-analytique courante dans les laboratoires d’analyses médicales, affectant jusqu’à 4 % des échantillons de sang. Elle peut avoir un impact significatif sur la précision des résultats d’analyse, pouvant potentiellement conduire à un diagnostic erroné et à un traitement inapproprié.
Objectif:
Quantifier les effets de l’hémolyse sur les paramètres de biochimie clinique de routine en comparant l’évaluation visuelle avec les indices d’hémolyse (IH) mesurés par automate.
Méthodologie:
Cette étude transversale a été menée à l’AIIMS, Kalyani, de 2021 à 2023. Des échantillons de sérum résiduels ont été utilisés pour évaluer l’impact de l’hémolyse induite mécaniquement sur 40 paramètres biochimiques mesurés à l’aide d’un analyseur Vitros 4600. Une échelle de notation visuelle semi-quantitative a été développée et comparée aux seuils quantitatifs d’IH de l’analyseur. Les divergences dans les taux de rejet d’échantillons entre les méthodes visuelle et automatisée ont été analysées en utilisant la base de données des dossiers de santé électroniques de l’hôpital. Analyse statistique : L’analyse des données comprenait le calcul des taux de rejet et la comparaison des différences entre les méthodes visuelles et celles basées sur l’IH pour chaque analyte.
Résultats:
L’évaluation visuelle a classé la plupart des échantillons comme « sans hémolyse » (91,86 %–100 %), l’hémolyse légère étant courante (1,43 %–9,09 %) et l’hémolyse sévère rare (0,41 %–0,62 %). La détection basée sur l’IH a montré des taux de rejet plus élevés pour des analytes tels que le potassium (1,58 % de plus), le phosphate (1,07 %) et les enzymes hépatiques. Inversement, des taux de rejet plus faibles ont été observés pour la créatinine (+0,75 %), l’urée (+0,79 %) et les électrolytes. Des divergences dépassant 0,5 % ont été notées pour les analytes fréquemment testés, soulevant des inquiétudes quant à la fiabilité de l’évaluation visuelle pour des paramètres spécifiques.
Conclusion:
L’optimisation des protocoles d’évaluation de l’hémolyse, en alignant les seuils d’IH avec la sensibilité analytique et les besoins cliniques, s’avère prometteuse pour améliorer la précision des résultats de laboratoire et les soins aux patients.
Mots-clés: Hémolyse, indice d’hémolyse, erreur pré-analytique, rejet d’échantillon
INTRODUCTION
Hemolysis is the most common preanalytical interference, encountered during the in vitro analysis because of the breakdown of red blood cells within a blood sample, collected in blood collection tubes at a clinical laboratory setup. Often perceived as insignificant, it can significantly affect the accuracy and reliability of laboratory reportable results, potentially leading to misdiagnosis, inappropriate treatment, and delayed patient care.[1,2]
Hemolysis is estimated to occur in up to 4% of all blood samples received for clinical chemistry analysis.[1] This seemingly low percentage translates to the millions of affected samples annually, highlighting the widespread nature of the impact. The consequences of hemolysis are far-reaching. Hemoglobin, released from lysed red blood cells, acts as an interfering agent or substance, affecting the measurement of various analytes.[3] Potassium, phosphorus, aspartate transaminase (ALT), and alkaline phosphatase are susceptible to hemolysis-induced biases, leading to falsely elevated or depressed values. This can have significant clinical implications, potentially masking underlying clinical conditions or misguiding therapeutic decisions based on inaccurate measurements.[2,4,5]
Visual assessment remains the primary method for hemolysis detection in many laboratories. Although it offers a quick and economical approach, its subjective nature and limited sensitivity raise the concerns about accuracy.[6] Studies have shown that visual assessment often fails to detect mild and moderate hemolysis, potentially leading to false-negative results and erroneous analysis of affected samples. In addition, inter-observer variability can further compromise the reliability of visual assessment, resulting in inconsistencies in hemolysis detection across laboratory personnel.[7]
Advancements in technology have led to the development of automated hemolysis indices (HIs) incorporated into modern laboratory analyzers. These indices, which are calculated based on the sample’s optical or photometric properties, offer a more objective and quantitative approach to hemolysis detection.[8] HI values provide a numerical score indicating the degree of hemolysis, allowing for standardized thresholds for each analyte, minimizing subjectivity and inter-observer variability.[9] The implementation of HI has shown promising results in improving the accuracy and consistency of hemolysis detection, potentially leading to more reliable and clinically relevant laboratory results.
While HI offers significant advantages, its effectiveness can vary depending on the specific analyte and the analyzer platform being used. In addition, the optimal HI threshold for each analyte may require careful evaluation and validation within the context of a particular laboratory’s patient population and analytical protocols. Therefore, further investigation is crucial to understand the performance of HI for different analytes and to establish the optimal thresholds for accurate hemolysis detection and subsequent result interpretation.
This study aims to comprehensively quantify the effects of hemolysis on a wide range of routine clinical biochemistry parameters by the way of comparing visual assessment with machine-based HI values. We have analyzed the rate of hemolysis occurrence across different analytes and investigated the potential discrepancies between visual and HI-based hemolysis detection. By identifying analytes susceptible to hemolysis-induced biases and validating optimal HI thresholds, we aimed to optimize hemolysis detection protocols and contribute to improved accuracy and reliability of clinical chemistry analysis in the presence of hemolysis. Ultimately, this study seeks to mitigate the silent threat of hemolysis and ensure the delivery of accurate and clinically relevant laboratory results for optimal patient care. This article investigates the effects of hemolysis on biochemical parameters, comparing visual assessment with machine-based HIs to identify potential discrepancies and optimize hemolysis detection protocols.
METHODOLOGY
This cross-sectional study was conducted in the Department of Biochemistry, AIIMS, Kalyani, at a tertiary care hospital in Eastern India, during the period from 2021 to 2023. We utilized the residual serum samples and the hospital patient database to assess the magnitude of mechanically induced hemolysis on 40 standard biochemical parameters measured using the Vitros 4600 automated clinical chemistry analyzer. A semi-quantitative visual grading scale was developed to categorize the samples as nonhemolysis, mildly hemolysis, moderately hemolysis, or severely hemolysis. These visual grades were compared to the analyzer’s quantitative HI thresholds to accept or reject the samples. Discrepancies in sample rejection rates between visual and automated methods were analyzed using the electronic health record database. This study was approved by the institutional ethics committee, and patient data were anonymized to ensure confidentiality.
Sample preparation
As shown in Figure 1, the residual serum from the processed blood vacutainer of patients with normal range was divided into two aliquots of equal volume.
Figure 1.
The division of residual serum from processed blood vacutainer
Aliquot labeled I acted as control during the visual assessment and for the analysis of additional parameters which were not in the requisition form
Aliquot labeled II acted as a diluent for serial dilution of the parent vacutainer containing the mechanically hemolyzed sample.
Figure 2: Flow chart shows the blood vacutainer containing residual serum along with separated blood cells were subjected to 20 times aspiration with a 5 ml syringe to produce mechanically induced hemolysis.
Figure 2.
The gradient of hemolysis created mechanically
For the said procedure, a single trained laboratory technician was assigned to avoid inter-individual variation in the mechanical pressure applied for the procedure. This was followed by centrifugation at 3000 rpm for 10 min to isolate severely hemolyzed serum. The artefactual hemolysis serum obtained by the manual intervention was proportionally mixed with serum from an aliquot labeled II to prepare different grades of hemolyzed samples. Supernatant segregated onto four aliquots in volumes of 5, 10, 15, and 20 µL and subjected to serial dilution with 200 µL serum from aliquot labeled II to be diluted in the ratio 40:1, 20:1, 13.3:1, and 10:1.
Forty biochemistry parameters were processed with these four serially diluted serum samples and analyzed for visual versus machine acceptance and rejection rates of hemolysis samples.
Sample analysis and measurements
Analyzer and hemolysis index
All the samples (control and hemolysis dilutions) were analyzed on a Vitros 4600 automated analyzer (Ortho Clinical Diagnostics) equipped with HI capabilities. The analyzer measured each analyte concentration and calculated the corresponding HI value based on the optical and electrical properties of the sample.
Visual assessment
Using visual inspection, ten laboratory personnel with varying experience levels independently assessed the hemolysis degree in each sample (control and hemolyzed samples of different gradients). A four-point scoring system was employed: 0 for no hemolysis, 1 for mild hemolysis, 2 for moderate hemolysis, and 3 for severe hemolysis. The average score of all ten personnel was used for each sample.
Calculation of cutoff hemolysis index value against each visual assessment based hemolysis level
The average HI value was calculated for each hemolysis level based on the visual assessment scores. We assumed cutoff thresholds for classifying the samples based on average Visual Assessment Score as: “No hemolysis” (VA Score below 1), “Mild hemolysis” (VA Score between 1 and 2.5), and “Severe hemolysis” (VA Score above 2.5). To accurately reflect the varying degrees of hemolysis observed both visually and through the HI values, we established three distinct categories: “No hemolysis” for samples with HI <65, “Moderate hemolysis” for those between 65 and 165, and “Severe hemolysis” for samples exceeding 165 HI [Supplementary Table 1]. This allowed us to compare the severity of hemolysis detected by HI with the visual assessment scores directly in our patient sample database.
Supplementary Table 1.
Calculation of mean visual score and relationship with hemolysis indices score
| Tube_Number | HI (Autoanalyzer) | Person_1 | Person_2 | Person_3 | Person_4 | Person_5 | Person_6 | Person_7 | Person_8 | Person_9 | Person_10 | Mean VA score | VA category |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 35 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0.4 | No hemolysis |
| 6 | 51 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.5 | No hemolysis |
| 9 | 57 | 0 | 0 | 2 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0.7 | No hemolysis |
| 7 | 66 | 0 | 2 | 2 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 1.2 | Mild hemolysis |
| 8 | 82 | 2 | 3 | 2 | 2 | 3 | 2 | 2 | 3 | 2 | 2 | 2.3 | Mild hemolysis |
| 1 | 92 | 0 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 0 | 1.1 | Mild hemolysis |
| 10 | 93 | 2 | 2 | 3 | 2 | 3 | 2 | 2 | 3 | 2 | 3 | 2.4 | Mild hemolysis |
| 17 | 125 | 2 | 1 | 1 | 2 | 2 | 3 | 1 | 2 | 3 | 2 | 1.9 | Mild hemolysis |
| 11 | 134 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 3 | 2 | 3 | 2.4 | Mild hemolysis |
| 2 | 152 | 2 | 2 | 3 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | Mild hemolysis |
| 12 | 162 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 18 | 198 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 13 | 218 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 3 | 222 | 2 | 3 | 2 | 3 | 2 | 3 | 3 | 3 | 2 | 3 | 2.6 | Severe hemolysis |
| 4 | 259 | 3 | 3 | 3 | 3 | 3 | 1 | 3 | 3 | 3 | 3 | 2.8 | Severe hemolysis |
| 19 | 263 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 20 | 321 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 14 | 322 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 15 | 434 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
| 16 | 602 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Severe hemolysis |
Final visual category was assigned based on following rule: “No hemolysis” (VA score below 1), “Mild hemolysis” (VA score between 1 and 2.5), “Severe hemolysis” (VA score above 2.5). VA=Visual assessment, HI=Hemolysis index
Quality control
Control samples with known analyte concentrations and hemolysis levels were included in each analytical run to ensure the accuracy and precision of measurements. The personnel performing visual assessment were blinded to each sample’s dilution level and HI values to minimize the potential bias.
Data analysis in patient electronic health record database-hemolysis acceptance or rejection
For each analyte, the number of samples accepted and rejected based on both visual assessment-based cut-off and HI values were recorded. Acceptance thresholds were set based on the manufacturer guidelines and the specific characteristics of each analyte. The rate of discrepancies between visual assessment and HI-based acceptance/rejection was calculated in a sample for each analyte.
RESULTS
Table 1 summarizes the distribution of hemolysis grades identified through visual assessment for all 40 analytes. Most analytes exhibited a high percentage of samples categorized as “no hemolysis” (range 91.86%–100%). Mild hemolysis (1.43%–9.09%) was common, whereas severe hemolysis (0.41%–0.62%) was detected in a tiny proportion of samples, indicating the rarity in the study population. These findings suggest that visual assessment might be sufficient for identifying severe hemolysis, but overall, the situation is less likely to occur in a real-life scenario.
Table 1.
Hemolysis status as per visual method of the test samples
| Assay | Tests performed | No hemolysis, n (%) | Mild hemolysis, n (%) | Severe hemolysis, n (%) |
|---|---|---|---|---|
| Albumin | 9450 | 9206 (97.42) | 202 (2.14) | 42 (0.44) |
| Alkaline phosphatase | 6945 | 6767 (97.44) | 144 (2.07) | 34 (0.49) |
| Alanine transaminase | 7195 | 7030 (97.71) | 134 (1.86) | 31 (0.43) |
| Amylase | 490 | 481 (98.16) | 7 (1.43) | 2 (0.41) |
| Antistreptolysino | 13 | 13 (100) | 0 | 0 |
| Aspartate transaminase | 6977 | 6805 (97.53) | 140 (2.01) | 32 (0.46) |
| Conjugated bilirubin | 7154 | 6943 (97.05) | 167 (2.33) | 44 (0.62) |
| Unconjugated bilirubin | 7154 | 6943 (97.05) | 167 (2.33) | 44 (0.62) |
| Complement 3 | 107 | 105 (98.13) | 1 (0.93) | 1 (0.93) |
| Complement 4 | 69 | 66 (95.65) | 2 (2.9) | 1 (1.45) |
| Calcium | 6423 | 6265 (97.54) | 131 (2.04) | 27 (0.42) |
| Cholesterol | 3470 | 3414 (98.39) | 42 (1.21) | 14 (0.4) |
| Creatine Kinase | 125 | 124 (99.2) | 1 (0.8) | 0 |
| Ammonia | 47 | 46 (97.87) | 1 (2.13) | 0 |
| Chloride | 1750 | 1728 (98.74) | 21 (1.2) | 1 (0.06) |
| Creatinine | 17024 | 16,433 (96.53) | 478 (2.81) | 113 (0.66) |
| C reactive protein | 1402 | 1380 (98.43) | 17 (1.21) | 5 (0.36) |
| Direct HDL | 3401 | 3346 (98.38) | 41 (1.21) | 14 (0.41) |
| Direct LDL | 1749 | 1731 (98.97) | 15 (0.86) | 3 (0.17) |
| Direct total iron binding capacity | 577 | 567 (98.27) | 9 (1.56) | 1 (0.17) |
| Bicarbonate | 149 | 141 (94.63) | 8 (5.37) | 0 |
| Iron | 684 | 669 (97.81) | 12 (1.75) | 3 (0.44) |
| Gamma glutamyl transaminase | 6853 | 6683 (97.52) | 138 (2.01) | 32 (0.47) |
| Glucose | 13,899 | 13,613 (97.94) | 235 (1.69) | 51 (0.37) |
| High sensitive C reactive protein | 22 | 20 (90.91) | 2 (9.09) | 0 |
| Immunoglobulin E | 3640 | 3587 (98.54) | 45 (1.24) | 8 (0.21) |
| Potassium | 16,645 | 15,915 (95.61) | 572 (3.44) | 158 (0.95) |
| Lactate dehydrogenase | 311 | 299 (96.14) | 10 (3.22) | 2 (0.64) |
| Lithium | 60 | 59 (98.33) | 1 (1.67) | 0 |
| Lipase | 467 | 460 (98.5) | 6 (1.28) | 1 (0.21) |
| Magnesium | 384 | 363 (94.53) | 17 (4.43) | 4 (1.04) |
| Sodium | 16,542 | 15,791 (95.46) | 594 (3.59) | 157 (0.95) |
| Phosphorus | 5832 | 5696 (97.67) | 114 (1.95) | 22 (0.38) |
| Rheumatoid factor | 314 | 311 (99.04) | 3 (0.96) | 0 |
| Total bilirubin | 7157 | 6947 (97.07) | 167 (2.33) | 43 (0.6) |
| Total protein | 7084 | 6901 (97.42) | 149 (2.1) | 34 (0.48) |
| Triglyceride | 3456 | 3398 (98.32) | 44 (1.27) | 14 (0.41) |
| Urea | 15,596 | 15,031 (96.38) | 456 (2.92) | 109 (0.7) |
| Uric acid | 5820 | 5714 (98.18) | 91 (1.56) | 15 (0.26) |
| Valproate | 13 | 12 (92.31) | 1 (7.69) | 0 |
HDL=High-density lipoprotein, LDL=Low-density lipoprotein
When the quantitative HI was applied, discrepancies emerged in the sample rejection rates compared to visual grading [Table 2]. Across 40 biochemical assays and 16,645 test samples for potassium, rejection rates were 1.58% higher by HI compared to visual grading methods. Differences near 1% were observed for four additional assays, including phosphate (−1.07%), alkaline phosphatase (−1.01%), aspartate aminotransferase (−0.97%), magnesium (−0.78%), with HI thresholds rejecting more specimens. This suggests that HI is more sensitive for detecting mild and moderate hemolysis than visual assessment, potentially leading to the rejection of additional samples that would otherwise be analyzed.
Table 2.
Samples that met the rejection criteria due to hemolysis, as detected by the visual method and the HIT method
| Assay | Total number of test | Hemolysis by visual method, n (%) | Hemolysis by HIT value, n (%) | Difference in number (%) (Visual - HIT) |
|---|---|---|---|---|
| Potassium | 16645 | 730 (4.39) | 994 (5.97) | −264 (−1.58) |
| Phosphorus | 5832 | 136 (2.33) | 198 (3.4) | −62 (−1.07) |
| Alkaline phosphatase | 6945 | 178 (2.56) | 248 (3.57) | −70 (−1.01) |
| Aspartate transaminase | 6977 | 172 (2.47) | 240 (3.44) | −68 (−0.97) |
| Magnesium | 384 | 21 (5.47) | 24 (6.25) | −3 (−0.78) |
| Lactate dehydrogenase | 311 | 12 (3.86) | 14 (4.5) | −2 (−0.64) |
| Iron | 684 | 15 (2.19) | 17 (2.49) | −2 (−0.3) |
| Uric acid | 5820 | 17 (0.29) | 21 (0.36) | −4 (−0.07) |
| Total bilirubin | 7157 | 46 (0.64) | 47 (0.66) | −1 (−0.02) |
| Antistreptolysino | 13 | 0 | 0 | 0 |
| Creatine kinase | 125 | 0 | 0 | 0 |
| Ammonia | 47 | 0 | 0 | 0 |
| Bicarbonate | 149 | 0 | 0 | 0 |
| High sensitive C reactive protein | 22 | 0 | 0 | 0 |
| Immunoglobulin E | 3640 | 53 (1.45) | 61 (1.67) | −8 (−0.22) |
| Rheumatoid factor | 314 | 0 | 0 | 0 |
| Valproate | 13 | 0 | 0 | 0 |
| Chloride | 1750 | 1 (0.06) | 0 | 1 (0.06) |
| Glucose | 13,899 | 57 (0.41) | 37 (0.27) | 20 (0.14) |
| Direct LDL | 1749 | 3 (0.17) | 0 | 3 (0.17) |
| Direct total iron binding capacity | 577 | 1 (0.17) | 0 | 1 (0.17) |
| Lipase | 467 | 1 (0.21) | 0 | 1 (0.21) |
| Conjugated bilirubin | 7154 | 47 (0.66) | 30 (0.42) | 17 (0.24) |
| Unconjugated bilirubin | 7154 | 47 (0.66) | 30 (0.42) | 17 (0.24) |
| Direct HDL | 3401 | 14 (0.41) | 2 (0.06) | 12 (0.35) |
| C reactive protein | 1402 | 5 (0.36) | 0 | 5 (0.36) |
| Cholesterol | 3470 | 14 (0.4) | 0 | 14 (0.4) |
| Amylase | 490 | 2 (0.41) | 0 | 2 (0.41) |
| Triglyceride | 3456 | 14 (0.41) | 0 | 14 (0.41) |
| Alanine transaminase | 7195 | 32 (0.44) | 0 | 32 (0.44) |
| Calcium | 6423 | 29 (0.45) | 0 | 29 (0.45) |
| Gamma glutamyl transaminase | 6853 | 34 (0.5) | 0 | 34 (0.5) |
| Creatinine | 17024 | 127 (0.75) | 0 | 127 (0.75) |
| Urea | 15,596 | 123 (0.79) | 0 | 123 (0.79) |
| Complement 3 | 107 | 1 (0.93) | 0 | 1 (0.93) |
| Sodium | 16542 | 173 (1.05) | 0 | 173 (1.05) |
| Complement 4 | 69 | 1 (1.45) | 0 | 1 (1.45) |
| Albumin | 9450 | 244 (2.58) | 105 (1.11) | 139 (1.47) |
| Total protein | 7084 | 183 (2.58) | 75 (1.06) | 108 (1.52) |
| Lithium | 60 | 1 (1.67) | 0 | 1 (1.67) |
HDL=High-density lipoprotein, LDL=Low-density lipoprotein, HIT=Hemolysis-Index Threshold
Conversely, some assays showed lower rejection rates with creatinine (+0.75%), urea (+0.79%), sodium (+1.05%), albumin (+1.47%), total protein (+1.52%), and lithium (+1.67%) by HI versus visual grading. A few important parameters, including glucose (+0.14%), direct low-density lipoprotein (+0.17%), direct Total Iron-Binding Capacity (TIBC) (+0.17%), lipase (+0.21%), and bilirubin (+0.24%) show similar phenomena. These discrepancies raise the concerns about the reliability of visual assessment, especially for specific analytes where misclassification might lead to inappropriate sample rejection and potentially demanding another sample collection, which may lead to a waste of resources and a delay in clinical decision-making.
Figure 3 highlights frequently tested analytes (at least 5000 or more in count) with a difference in hemolysis-related sample rejection exceeding 0.5% between the two methods. This emphasizes the potential for discrepancies in hemolysis detection, particularly for analytes such as potassium and phosphates, along with alanine transaminase at one end and sodium, albumin, or total protein at the opposite end of acceptance. These discrepancies raise the concerns about the reliability of visual assessment, especially for specific analytes where misclassification might lead to inappropriate sample rejection and potentially inaccurate patient results.
Figure 3.
Differences in hemolysis-related sample rejection rates between visual assessment and Hemolysis Index (HI) method for frequently tested biochemical assays. The red dots represent the percentage difference in rejection rates (Visual − HI), while the blue bars indicate the relative test volume. Assays include: K+: Potassium, PHOS: Phosphorus, ALKP: Alkaline Phosphatase, AST: Aspartate Transaminase, CREA: Creatinine, UREA: Urea, Na+: Sodium, ALB: Albumin, and TP: Total Protein. Negative values denote higher rejection by HI, while positive values reflect greater rejection by visual assessment. The figure highlights the assay-specific variability in hemolysis detection sensitivity across methods
DISCUSSION
This study provides important insights into the effects of hemolysis on a wide array of biochemical parameters. Our findings reveal discrepancies between visual and automated technology-based hemolysis detection, highlighting the need to optimize the protocols to improve accuracy.
At most of the clinical laboratories, the usual protocol is to reject or re-collect specimens to reduce the interference of hemolysis on test results. This increases the burden of repeat blood collection by phlebotomists, and nonetheless, lab reagent expenditure. In addition to increased turnaround time (TAT), this effects on timely clinical intervention for better patient care. Till date, there are no standardized measures available to eliminate the interference of hemolysis.
The assessment of hemolysis interference using the HI, instead of a visual assessment, enables to opine on the quality of the hemolysis samples which needs to be determined in an accurate and objective manner, enhancing the accuracy of the test results, and excluding the interference caused by hemolysis.[10]
Currently, multiple biochemistry analyzers have a HI defined for the detection of hemolysis. Depending on different methodologies, the hemolysis threshold is varyingly defined, leading to different HI for the same test parameter in different test systems, and hence, the interference of the hemolysis threshold may not be the same.[11]
The hemolysis thresholds of the 40 biochemical parameters examined in this study revealed that hemolysis interfered with 13 of them; thus, the test results of 27 indicators were not affected by hemolysis.
Any correction process adds uncertainty to a result but would make three additional observations:
Firstly the uncorrected results in the presence of interference display the same uncertainty in their difference from the true value as is seen with corrected results. Second, any evidence-based quantitative response to an interferent requires an assessment of the uncertainty of the effect of that interferent. Third, correcting for the effect of the interference reduces the expected uncertainty of the issued results.[12]
Overall, HI demonstrated higher sensitivity for identifying the lower levels of hemolysis compared to subjective visual assessment. This manifested as higher rejection rates for analytes such as potassium, phosphates, and liver enzymes.[13] A prior investigation by Lippi et al. confirmed this phenomenon, showing elevated rejection by HI versus visual methods for potassium, ALT, alanine transaminase, amylase, and gamma-glutamyl transaminase.[3] The semi-quantitative nature of visual grading likely underestimates subclinical hemolysis. Meanwhile, HI can detect free hemoglobin down to 10 mg/dL, enabling the identification of interference below visible discoloration thresholds.[4] Erroneous acceptance of hemolyzed specimens could permit clinically significant interference.
Conversely, lower rejection rates by HI for certain analytes, including sodium, albumin, and creatinine, are concerning. A study by Simundic et al. showed average biases of up to 15% for sodium and 12% for albumin at higher hemolysis levels.[14] A balance must be struck between analytical sensitivity to hemolysis and practical constraints. Judicious alignment of HI thresholds with justifiable margins of analytical error may help mitigate this issue.
Notably, the degree of impact of hemolysis varies substantially between analytes.[15] Markers such as ALT, alanine transaminase, and potassium demonstrated positive bias at high HI. Other parameters, including glucose, creatinine, sodium, and chloride, showed comparatively lesser interference.[16] This variability highlights the need for assay-specific thresholds attuned to the tolerance of individual tests.
The use of HI in day-to-day lab practice also contributed to minimizing the percentage of sample rejections and repeat collection of blood samples, thereby improving the laboratory performance by decreasing the TAT for hemolysis-sensitive parameters using HI technology.[17] The HI estimates the impact of hemolysis of various biochemical analytes present in the processed sample and also supports the lab professional in releasing the results of analytes that are proven to be affected during blood collection procedures from the pediatric age group[18] and critically ill patients.
While a predominantly in vitro investigation, our study provides a framework to examine the effects of hemolysis in the real-world populations. Our grading scheme and proposed HI thresholds can be validated through future patient-based studies to verify effectiveness. Assessing clinical outcomes linked to discordant hemolysis detection between methods can also reveal potential risks requiring intervention.[19] Beyond assay-specific considerations, patient-specific factors may also dictate acceptable hemolysis limits. For a patient with normal potassium, a biased result may require repeat testing. However, for a patient with hyperkalemia, even minor positive interference could suggest a life-threatening outcome, demanding immediate clinical intervention.[20] Integrating assay and patient considerations into hemolysis protocols promises to augment the clinical utility of laboratory data.
The limitations of this study include its single-center nature and the inability to verify standardized hemolysis induction across all the samples. Second, mechanically induced hemolysis may not fully replicate the diverse hemolysis patterns encountered in clinical samples. In addition, while incorporating multiple personnel, the visual approach remains fundamentally subjective. Nonetheless, our findings highlight the key variables requiring optimization under current laboratory constraints to improve harmonization.
CONCLUSION
Our analysis demonstrates discrepancies between visual and HI-based hemolysis detection across a spectrum of chemistry assays. Optimizing hemolysis assessment protocols by judiciously aligning HI thresholds to balance analytical sensitivity and clinical needs shows promise for improving accuracy. Advancement of these quality assurance mechanisms will contribute to enhanced patient care guided by dependable laboratory data.
Data availability statement
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Author contribution
All the authors have significantly contributed to the accomplishment of the study and preparation of the manuscript.
Conflicts of interest
There are no conflicts of interest.
Acknowledgements
The authors would like to thank the laboratory staff at the Department of Biochemistry, AIIMS Kalyani, for their assistance in sample handling and data collection. The authors also acknowledge that Claude 3.5 Large Language Model (Anthropic) was used for Grammer check and proof-correction.
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.