Introduction
- The Quality control of a clinical biochemistry laboratory is an essential part of modern healthcare, offering critical data that influences medical decision-making.
- Through the analysis of various biological samples, such as blood, urine, cerebrospinal fluid, and other body fluids, clinical biochemistry provides insight into the metabolic, biochemical, and enzymatic functions of the human body.
- These laboratories typically work with a wide variety of tests that assess factors such as organ function, disease progression, and potential abnormalities in metabolism or nutrition.
- The importance of a clinical biochemistry laboratory cannot be overstated.
- The laboratory’s ability to analyze biochemical markers can help in diagnosing conditions such as diabetes, kidney disease, liver disorders, cardiovascular diseases, and cancer.
- However, ensuring the accuracy and reliability of the results is critical.
- Laboratory errors, whether due to human, instrumental, or procedural factors, can result in significant misdiagnoses or incorrect treatments.
Sources of Laboratory Errors
- Laboratory errors can occur at any stage of the testing process.
- These errors can broadly be categorised into pre-analytical, analytical, and post-analytical phases.
- Each phase presents its own set of challenges and potential sources of error.
Pre-Analytical Errors
Pre-analytical errors occur before the sample reaches the laboratory for analysis, and they are the most common type of error in clinical laboratories. The following are key sources:
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Incorrect Sample Collection:
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Wrong sample type: Some tests require specific types of samples (e.g., serum, plasma, or urine). Using the wrong sample type can lead to inaccurate results.
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Incorrect volume: A sample that is too small may not provide enough material for analysis, while an excessive volume may lead to over dilution, distorting the analysis.
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Improper containers: Using the wrong tubes or containers (e.g., using a tube with a wrong additive like EDTA instead of heparin) can affect the stability of the sample.
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Sample Handling and Transport:
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Temperature fluctuations: Samples that are not kept at the correct temperature (e.g., freezing or overheating) can lead to the degradation of analytes.
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Prolonged transport times: If a sample is delayed in transit, biochemical components may be altered, affecting the results.
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Patient-related Factors:
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Fasting status: Certain tests require fasting (e.g., blood glucose), and if the patient does not follow instructions, the results may be skewed.
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Medications: The use of drugs, especially certain vitamins or anticoagulants, can alter the concentration of various analytes.
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Physical activity: Heavy exercise before testing can lead to changes in electrolyte levels or hormone concentrations.
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Analytical Errors
Analytical errors occur during the actual testing phase. These errors are often associated with the methodologies, equipment, and personnel involved in the laboratory work:
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Instrumental Issues:
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Malfunctioning equipment: Laboratory instruments can break down, leading to faulty results. For example, an incorrectly calibrated spectrophotometer could lead to erroneous readings of light absorbance.
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Poor calibration: If instruments are not properly calibrated or recalibrated at regular intervals, they may give inaccurate results.
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Inadequate maintenance: Lack of routine maintenance can lead to malfunctioning of essential components of laboratory instruments, compromising test results.
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Reagents and Consumables:
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Expired reagents: Using expired reagents or chemicals can lead to inaccurate or inconsistent results. Reagents must be properly stored and regularly checked for expiration dates.
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Contaminated reagents: If reagents are contaminated, the outcome of the biochemical test may be unreliable.
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Human Error:
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Operator mistakes: These include improper handling of instruments, miscalculation of volumes, incorrect sample dilution, and failure to follow standard procedures (SOPs).
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Failure to recognize interference: Some substances in the sample (like hemoglobin, bilirubin, or lipids) may interfere with assays, leading to false results. Inadequate recognition of these interferences can lead to inaccurate test results.
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Methodological Problems:
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Assay limitations: Some assays may have inherent limitations, such as a narrow detection range or poor specificity for certain analytes.
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Method interference: Biological substances in a sample (e.g., hemolysis, icterus, or lipemia) can interfere with some biochemical assays, leading to erroneous readings.
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Post-Analytical Errors
Post-analytical errors occur after the testing phase, typically during the interpretation or reporting of results. Though less common than pre- and analytical errors, they are equally important in ensuring accurate diagnosis and treatment:
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Data Transcription and Reporting:
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Clerical errors: Mistakes such as entering the wrong patient ID, misreporting results, or transcribing results incorrectly can have serious consequences.
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Mismatched reports: Mixing up results from different patients due to mislabeling or clerical issues can lead to significant misinterpretation.
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Delayed or Inaccurate Reporting:
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Delayed reporting: When results are not conveyed to the physician in a timely manner, it can delay diagnosis and treatment.
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Failure to contextualize results: Without clinical correlation, laboratory results may be misinterpreted. For example, an elevated glucose level could be due to a temporary stress response rather than a sign of diabetes.
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Errors in Interpretation:
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Lack of clinical correlation: Biochemical results should always be interpreted in the context of the patient’s medical history and current clinical status. Misinterpretation can occur if results are considered in isolation.
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Failure to recognize normal biological variability: Factors such as age, gender, and time of day can affect test results. Not considering these variations can lead to errors in interpretation.
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Quality Control
- Quality control (QC) is the cornerstone of a reliable clinical biochemistry laboratory.
- It involves systematic procedures aimed at detecting, reducing, and correcting errors, ensuring that the laboratory produces consistent, accurate, and reproducible results.
- QC activities are implemented at various levels: internal QC, external QC, and laboratory accreditation.
Internal Quality Control (IQC)
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Control Materials:
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Laboratories use control samples with known concentrations of analytes to monitor the accuracy and precision of their tests. These controls are run alongside patient samples during each analytical session. Any deviation from the expected range indicates a potential problem that needs to be addressed.
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Calibration:
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Regular calibration of analytical instruments ensures that they are providing accurate readings. Calibration is performed using traceable standards, and any discrepancies are promptly corrected.
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Preventive Maintenance:
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A planned schedule for preventive maintenance of laboratory equipment helps avoid unanticipated breakdowns. This includes cleaning, checking for wear and tear, replacing parts, and ensuring that the equipment operates within specified tolerances.
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Training and Competency of Personnel:
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Ongoing training ensures that laboratory staff remain proficient in performing tests and interpreting results. Periodic assessments and competency evaluations help identify areas where additional training may be needed.
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Documentation and Standard Operating Procedures (SOPs):
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SOPs provide detailed guidelines for each task and process in the laboratory. Following these protocols ensures consistency and reliability. Proper documentation is essential for auditing purposes and tracking any errors or deviations.
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External Quality Assurance (EQA)
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Proficiency Testing:
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External proficiency testing programs send unknown samples to laboratories for analysis. This allows laboratories to compare their results with those from other laboratories and ensures that they meet national or international standards.
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Inter-Laboratory Comparisons:
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Laboratories often participate in programs where they exchange test results with other labs. This helps to detect discrepancies and encourages continuous improvement.
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Accreditation and Regulatory Standards
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ISO 15189:
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This international standard outlines the requirements for quality management in medical laboratories. Achieving ISO 15189 accreditation demonstrates that a laboratory meets the highest standards of quality, competence, and patient safety.
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Clinical Laboratory Improvement Amendments (CLIA):
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In the United States, CLIA ensures that laboratory testing is accurate, reliable, and timely. Laboratories must adhere to specific guidelines related to equipment, staff qualifications, and testing procedures to achieve CLIA certification.
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Continuous Monitoring and Auditing
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Internal Audits:
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Periodic internal audits assess whether the laboratory is complying with its own SOPs and quality standards. Audits can uncover inefficiencies or areas for improvement.
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Corrective Actions:
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When errors or issues are identified, corrective actions must be taken immediately. These actions might include recalibrating instruments, replacing faulty reagents, or retraining staff to ensure that errors are prevented in the future.
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Quality Control Charts
- Quality Control (QC) charts are fundamental tools used in clinical biochemistry laboratories to monitor the accuracy, precision, and reliability of test results over time.
- They help ensure that laboratory tests are consistent, reproducible, and meet the required standards.
- QC charts not only identify when laboratory processes are working as expected but also help in early detection of any process deviations, ensuring the integrity of diagnostic results.
- In clinical biochemistry, where test results can directly impact diagnosis and treatment, maintaining high-quality standards is critical.
- QC charts allow laboratories to visualise data, monitor test performance, and implement corrective actions promptly.
Types of Quality Control Charts
- There are several types of QC charts used in clinical laboratories, each designed to track different aspects of analytical performance.
- These charts help in the identification of issues such as shifts, trends, or abnormal variations in the testing process.
- Below are the most commonly used QC charts in clinical biochemistry:
Levey-Jennings Chart
- The Levey-Jennings chart is the most commonly used quality control chart in clinical laboratories.
- It plots the results of quality control samples over time, providing a visual representation of whether the laboratory testing process remains stable and within control limits.
- It is particularly useful for monitoring the precision of analytical methods.
Features:
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X-axis: Represents the sequence of test runs or time intervals (e.g., daily, weekly).
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Y-axis: Represents the results of the quality control (QC) sample, often in terms of concentration (e.g., glucose level).
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Mean line (Average line): This line represents the expected or target value for the QC sample.
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Control Limits (± 2 SD or ± 3 SD): These are statistical boundaries that define the range within which the test results should lie. Results that fall within these limits are considered acceptable, while those outside indicate potential issues.
Interpretation:
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Within Control Limits: If QC results stay within the ±2 SD or ±3 SD range, the system is stable and under control.
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Out of Control: If a QC result falls outside the control limits, the testing process may have experienced a malfunction, requiring investigation. This could be due to issues like instrument malfunction, reagent problems, or procedural errors.
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Trend Detection: A gradual shift of results in the same direction (e.g., consistently higher or lower) over multiple runs may signal an underlying issue, such as reagent degradation or instrument drift.
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Random Errors: Results that vary widely from one another, without any consistent trend, may indicate random errors, which can be addressed by improving precision or technique.
Cumulative Sum (CUSUM) Chart
- The Cumulative Sum (CUSUM) chart is designed to monitor small shifts or changes in analytical performance that might not be easily detected using a Levey-Jennings chart.
- The CUSUM chart accumulates deviations from the target value over time, providing a more sensitive method for detecting gradual shifts in the process.
Features:
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X-axis: Represents the sequence of test runs (or time intervals).
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Y-axis: Represents the cumulative sum of deviations from the target value (calculated as the difference between the observed result and the target value).
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Control Limits: Upper and lower limits are set to help detect large deviations and shifts in the process.
Interpretation:
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Trend Detection: Small shifts or deviations accumulate over time. A gradual slope in the CUSUM chart indicates a trend or drift in the testing process.
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Small Changes: CUSUM charts are particularly sensitive to small, persistent shifts in test results that might be missed by other charts, making them ideal for monitoring long-term performance.
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Excessive Cumulative Deviation: A sharp deviation in the slope suggests a significant process change, indicating that the laboratory system is out of control.
Western Electric Rules (WE Rules) Chart
- The Western Electric Rules are a set of decision criteria used to identify out-of-control conditions in QC charts, particularly when combined with Levey-Jennings charts.
- These rules are designed to give more precise guidelines for detecting issues and are often used in high-stakes environments like clinical biochemistry.
Features:
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X-axis: Sequence of test runs or time intervals.
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Y-axis: QC results.
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Decision Criteria: The Western Electric Rules use a series of “if-then” conditions to interpret whether the system is out of control and needs corrective action.
Decision Rules:
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Rule 1 (One Point Outside ± 3 SD): If a QC result falls outside the ± 3 SD limit, this is considered a sign that the system is out of control.
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Rule 2 (Two Consecutive Points Beyond ± 2 SD): If two consecutive QC results fall beyond the ± 2 SD line, this suggests a shift in the system and requires immediate attention.
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Rule 3 (Four Consecutive Points on One Side of the Mean): Four consecutive QC results that fall on the same side of the mean may indicate a gradual shift in the process, which may not be visible on the Levey-Jennings chart.
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Rule 4 (Eight Consecutive Points Within ± 1 SD): If eight consecutive results fall within ± 1 SD, this may suggest an issue with variability and precision.
Range (or Moving Range) Chart
The Range (or Moving Range) Chart monitors the variation between consecutive QC results. This type of chart is especially useful for detecting inconsistencies in precision or the stability of a laboratory test method over time.
Features:
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X-axis: Time or sequence of test runs.
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Y-axis: Represents the difference (range) between consecutive QC results.
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Control Limits: Upper and lower limits are calculated to determine whether the variation between consecutive test results is within acceptable limits.
Interpretation:
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Stable Process: If the range between consecutive test results stays within control limits, it suggests that the testing process is stable and consistent.
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Excessive Variability: If the range increases significantly, this may indicate increased variability, suggesting that the laboratory process needs to be reviewed and improved.
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Precision Monitoring: This chart helps in detecting fluctuations in precision, which is essential for tests that require high levels of accuracy, such as blood glucose or cholesterol testing.
Process Behaviour Chart (PBC)
- The Process Behaviour Chart (PBC) is an adaptation of the Shewhart control chart, used to evaluate the overall stability of a testing process over time.
- It’s similar to a Levey-Jennings chart but places more focus on the ability to detect both gradual and abrupt changes in the analytical process.
Features:
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X-axis: Time or test run number.
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Y-axis: QC test results.
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Control Limits: Upper and lower limits are derived from the historical data and used to identify trends or shifts.
Interpretation:
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Stable Process: If the results fall within the control limits, the process is considered stable, and the laboratory can continue testing with confidence.
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Trend Detection: A rising or falling trend in the results could suggest a system shift or gradual deterioration in performance. If the process deviates outside control limits, corrective action should be taken.
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Identification of Outliers: The PBC helps in identifying abnormal data points that are significantly different from expected values.