According to Gram Research analysis, artificially created blood sample interference doesn’t always match real patient samples, especially for fatty blood. A 2026 research article published in Scientific Data found significant analytical discrepancies between fake and real interference samples across multiple blood tests, particularly in lipemic (fatty) samples. The researchers provided correction formulas and reference charts to help clinical laboratories better validate their tests and reduce unnecessary sample rejections.
When doctors take blood samples, sometimes the results aren’t accurate because of things like hemolysis (broken red blood cells), lipemia (fatty blood), or icterus (yellowing from bilirubin). Researchers created a comprehensive dataset comparing how these interference factors affect different types of blood tests, especially immune system markers. By testing both real patient samples and artificially created ones, they discovered that fake samples don’t always behave like real ones—particularly with fatty blood. This research gives laboratories practical tools and correction formulas to improve test accuracy and stop rejecting good samples unnecessarily.
Key Statistics
A 2026 research article in Scientific Data comparing endogenous and exogenous interference across immunoassays found that lipemic samples showed the largest discrepancies between artificially created and real patient samples, challenging current laboratory validation practices.
Researchers measured interference effects on eight different biomarkers including interleukins, tumor markers, and bone health indicators across electrochemiluminescence and flow fluorescent laboratory platforms, revealing platform-specific interference patterns.
The study provided correction formulas specifically for hemolysis-sensitive biomarkers, offering clinical laboratories practical tools to potentially save samples that would otherwise be rejected due to hemolysis interference.
The Quick Take
- What they studied: How three common blood sample problems (hemolysis, lipemia, and icterus) affect the accuracy of different types of blood tests used in hospitals and clinics.
- Who participated: The study analyzed blood samples tested on two major laboratory platforms: electrochemiluminescence machines and flow fluorescent machines. They compared real patient samples with artificially created interference samples.
- Key finding: Artificially created interference samples don’t always behave the same way as real patient samples, especially when blood is fatty. This means labs need better methods to validate their tests.
- What it means for you: If your blood test gets rejected because of hemolysis, lipemia, or icterus, labs now have better tools to determine if the sample is actually unusable or if they can still get accurate results. This could mean fewer unnecessary repeat blood draws.
The Research Details
Researchers created a large dataset by measuring how hemolysis (broken red blood cells), lipemia (excess fat), and icterus (bilirubin yellowing) interfere with blood test results. They tested multiple biomarkers—including immune system proteins called interleukins, tumor markers, and bone health indicators—on two different types of laboratory machines.
The key innovation was comparing two types of interference samples: endogenous samples (real patient blood with natural interference) and exogenous samples (artificially created interference added to normal blood). This allowed them to see if the fake samples behaved the same way as real ones.
They measured concentrations of eight different biomarkers across varying degrees of interference, then created reference charts and mathematical correction formulas that labs could use to determine whether a sample should be rejected or if results could still be trusted.
Currently, many clinical laboratories use artificially created interference samples to validate their tests. If these fake samples don’t accurately represent real patient samples, labs might be rejecting good samples or accepting bad ones. This research shows that artificial models—especially for fatty blood—don’t always match reality. By providing correction formulas and reference charts based on real data, labs can make better decisions about sample quality without unnecessarily wasting patient samples or requiring repeat blood draws.
This is a data-focused research article published in Scientific Data, a peer-reviewed journal. The strength of this work lies in its comprehensive dataset comparing real versus artificial samples across multiple laboratory platforms and biomarkers. The main limitation is that the study doesn’t specify the exact number of samples analyzed, making it harder to assess the statistical power. However, the practical tools provided (correction formulas and reference charts) are based on measured data rather than theory, which increases their clinical utility.
What the Results Show
The research revealed significant differences between how artificially created interference samples and real patient samples affect blood test results. Most notably, lipemic samples (those with excess fat) showed the largest discrepancies between artificial and real samples. This means that when labs use fake fatty samples to validate their tests, they may not be accurately predicting how real fatty patient blood will interfere with results.
The study measured eight different biomarkers across both laboratory platforms. These included immune system proteins (interleukins), tumor markers (NSE and ProGRP), and bone health indicators (osteocalcin and P1NP). The interference patterns varied depending on which biomarker was being measured and which laboratory platform was used.
Because of these findings, the researchers provided correction formulas specifically for hemolysis-sensitive biomarkers. These formulas allow labs to mathematically adjust results when hemolysis is present, potentially saving samples that would otherwise be rejected.
The study also found that different laboratory platforms (electrochemiluminescence versus flow fluorescent) responded differently to the same interference factors. This means that correction formulas developed for one type of machine may not work for another. The research provides platform-specific reference charts to address this issue. Additionally, the data showed that some biomarkers are more sensitive to interference than others, which helps labs prioritize which tests need the most careful validation.
Previous research has documented that hemolysis, lipemia, and icterus can interfere with blood tests, but most validation studies relied on artificially created samples. This research is significant because it directly compares artificial samples to real patient samples, revealing that the artificial models don’t always predict real-world behavior—particularly for lipemic interference. This finding challenges current laboratory validation practices and suggests that existing protocols may need updating.
The study doesn’t specify the total number of samples analyzed, which makes it difficult to assess statistical confidence in the findings. Additionally, while the research covers multiple biomarkers and platforms, it may not represent all possible blood tests used in clinical practice. The correction formulas provided are specific to the platforms and biomarkers tested, so labs using different equipment or measuring different markers would need to validate whether these formulas apply to their situation. Finally, the study focuses on analytical performance but doesn’t address clinical outcomes—meaning we don’t know if using these correction formulas actually improves patient care.
The Bottom Line
Clinical laboratories should review their current interference validation protocols and consider whether they’re using artificial samples that may not represent real patient blood. If available, labs should implement the correction formulas provided in this research for hemolysis-sensitive biomarkers. This could reduce unnecessary sample rejections while maintaining test accuracy. Confidence level: Moderate to High for laboratories using the same platforms and biomarkers tested in this study.
Clinical laboratory directors and technicians should care about this research because it directly affects their daily operations and sample rejection rates. Patients benefit indirectly by potentially avoiding unnecessary repeat blood draws. Doctors and hospitals should care because more accurate blood tests lead to better diagnostic decisions. This research is less relevant for home-based testing or point-of-care devices, which use different technology.
Changes to laboratory protocols based on this research could be implemented immediately, as the correction formulas and reference charts are ready to use. However, labs should validate these tools with their own equipment and patient populations before fully implementing them. Patients might notice reduced sample rejection rates within weeks to months of labs adopting these new protocols.
Frequently Asked Questions
Why do blood tests sometimes get rejected because of hemolysis or lipemia?
Hemolysis (broken red blood cells) and lipemia (excess fat) can interfere with how laboratory machines read blood test results, making them inaccurate. Labs reject these samples to avoid giving false results, but new research shows some samples can still be salvaged using correction formulas.
Are artificial blood samples used to test laboratory equipment the same as real patient blood?
No. A 2026 study found significant differences, especially with fatty blood samples. Artificial interference samples don’t always behave like real patient blood, which means labs may be using outdated validation methods that don’t accurately predict real-world performance.
Can laboratories fix blood test results that have hemolysis or lipemia interference?
Sometimes, yes. The 2026 research provides correction formulas for hemolysis-sensitive biomarkers that allow labs to mathematically adjust results instead of rejecting the sample. However, this depends on the type of test and laboratory equipment being used.
Will this research reduce the number of times I need to have my blood drawn?
Potentially. If your lab adopts these new correction formulas and reference charts, they may reject fewer samples unnecessarily, reducing repeat blood draws. However, this depends on whether your specific laboratory implements these protocols.
What should I do if my blood samples keep getting rejected?
Ask your lab about their interference validation protocols and whether they use correction formulas. Inform the phlebotomist about your history of hemolysis or lipemia. Proper collection technique and timing can reduce these issues. If problems persist, ask your doctor about alternative testing options.
Want to Apply This Research?
- If you’re tracking health markers that are sensitive to hemolysis (like immune system proteins), note the date and time of your blood draw and whether your sample was rejected or accepted. Track how many repeat draws you need over time—improved lab protocols should reduce this number.
- When scheduling blood work, ask your lab about their interference validation protocols. If you have a history of hemolysis or lipemia (which can run in families), inform the phlebotomist so they can use appropriate collection techniques. You can also ask whether your lab uses correction formulas for interference, which might prevent unnecessary repeat draws.
- Over a 6-12 month period, track whether you experience fewer rejected samples or repeat blood draws. If your lab implements these new protocols, you should see improvement in sample acceptance rates. Keep records of any biomarkers that are repeatedly problematic, as this information helps your lab refine their approach.
This research describes laboratory analytical methods and validation protocols. It does not provide medical advice or diagnose any condition. If your blood tests are repeatedly rejected or if you have concerns about test accuracy, consult with your healthcare provider or laboratory director. The correction formulas and reference charts described in this research are intended for use by clinical laboratories and should be validated for each specific laboratory setting before implementation. Individual results may vary based on laboratory equipment, procedures, and patient factors.
This research translation is published by Gram Research, the science division of Gram, an AI-powered nutrition tracking app.