Quality Control in Clinical Chemistry: Best Practices and Troubleshooting

Clinical chemistry laboratories depend on internal quality control (IQC) and external quality assessment (EQA) to confirm that patient results are accurate, precise, and clinically usable. Quality control in this setting means analyzing stable control materials alongside patient samples, plotting those control values on Levey-Jennings charts, applying Westgard rules to detect unacceptable error, and taking documented corrective action when a run is declared out of control. This article explains the core procedures, statistical tools, troubleshooting workflows, and documentation practices that laboratory students, technicians, researchers, and diagnostic professionals need to build and maintain a defensible QC program.

At a Glance

Quality control in clinical chemistry is a continuous process that combines statistical monitoring with medical relevance. The table below summarizes the main QC components, their purpose, and the actions they trigger.

Clinical laboratories are expected to deliver accurate, reliable, and timely results that support disease screening, diagnosis, and monitoring. Six sigma principles and metrics allow laboratories to assess process quality and determine the level of QC needed to achieve the desired quality. A study of 21 clinical chemistry parameters on a COBAS 6000 analyzer used IQC and proficiency testing results to calculate sigma metrics over six months. Excellent performance at or above six sigma was found for amylase pancreatic, amylase total, HDL, magnesium, AST, triglyceride, total bilirubin, and ALT at both control levels. Urea, creatinine, and chloride failed to meet minimal sigma performance at both levels. ALP, direct bilirubin, total protein, albumin, glucose, potassium, and phosphate showed sigma values between 3 and 6. The authors concluded that stringent IQC strategies are not mandatory for analytes scoring at or above six sigma, but continuous monitoring is required for renal function tests and process improvement should be designed for parameters with poor sigma values (Evaluation of Sigma Metrics and Westgard Rule Selection and Implementation of Internal Quality Control in Clinical Chemistry Reference Laboratory, Ethiopian Public Health Institute).

The Purpose of Quality Control in Clinical Chemistry

Quality control exists to detect analytical errors before they reach the clinician and affect patient care. The total testing process includes preanalytical, analytical, and postanalytical phases, and QC primarily addresses the analytical phase. Internal quality control involves control samples with known target values for all parameters used in clinical practice. Daily IQC allows the laboratory to monitor the accuracy and precision of results. Appropriate IQC strategies have been accepted in medical laboratories for decades and are included in international recommendations and guidelines. The term IQC strategy, also called a quality control plan, refers to the types of IQC materials to be measured, the frequency of IQC events, the number of concentration levels in each IQC event, and the IQC rules to be used. A scientifically sound IQC strategy must follow two principles. First, statistical follow-up of IQC results using Levey-Jennings control charts and Westgard rules. Second, determination of limits based on medical considerations and definition of analytical goals (Internal Quality Controls in the Medical Laboratory: A Narrative Review of the Basic Principles of an Appropriate Quality Control Plan).

The World Health Organization Laboratory Quality Management System Handbook provides a framework for implementing quality systems across the entire laboratory, including document control, equipment maintenance, and corrective action procedures. Laboratories should align their QC practices with this systems approach to ensure that quality is managed consistently across all phases of testing (Laboratory Quality Management System Handbook).

Internal Quality Control Materials and Their Selection

IQC materials must be stable, commutable with patient samples, and available at clinically relevant concentrations. Most laboratories use commercial lyophilized or liquid controls at two or three concentration levels. The control matrix should resemble the patient sample matrix as closely as possible to detect matrix-related interferences.

Commercial controls are the standard choice because they are characterized by the manufacturer and provide target values for multiple analytes. However, cost can be a barrier in resource-limited settings. A study evaluated pooled human serum as an in-house glucose QC material compared with commercial IQC. The in-house material was prepared from pooled serum and analyzed on a fully automated chemistry analyzer. Mean glucose was 185.2 plus or minus 8.4 mg/dL, indicating acceptable precision between measurements. The prepared material was stable for approximately five months without significant change in glucose concentration. The study concluded that pooled serum is a cost-effective method for in-house quality control, especially in resource-limited laboratories, and that room temperature, 2 to 8 degrees Celsius, and minus 20 to minus 30 degrees Celsius storage of human serum samples for glucose analysis is viable with stable concentrations for up to 30 days (Validation of the efficacy of pooled serum for serum glucose inhouse quality control material in comparison with commercial internal quality control in clinical chemistry laboratory).

When selecting IQC materials, consider the following factors:

• Concentration levels should bracket the medical decision points for each analyte
• Material should be stable for the entire lot period under the storage conditions used
• The matrix should be free of interfering substances that would not be present in patient samples
• The laboratory should have enough material to last through the lot without frequent changes
• Each new lot must be validated before it replaces the previous lot

Levey-Jennings Charts and Control Limits

The Levey-Jennings chart is the primary graphical tool for monitoring IQC results. Shewhart introduced industry to the concept of three standard deviation limits for quality control, and Levey, Jennings, Henry, and Westgard adapted this idea to clinical laboratory medicine. Westgard formulated a system of rules to enable clinical laboratory scientists to decide whether tests were in control and reportable or out of control. The mathematical basis is that a result beyond three standard deviations has a probability of less than 1 in 370 of occurring by chance alone, which is an indication for corrective laboratory action (Probability and the Westgard Rules).

To construct a Levey-Jennings chart, the laboratory establishes a mean and standard deviation for each control level. This is typically done by analyzing the control material over 20 to 30 separate runs. The chart plots control values on the y-axis against time or run number on the x-axis. Horizontal lines mark the mean, plus or minus one, two, and three standard deviations. Each new control value is plotted as it is generated, and the pattern of points is evaluated against Westgard rules.

A unique application of the Levey-Jennings chart was developed for human papillomavirus detection using PCR membrane hybridization. The means and standard deviations of positive rates were calculated, and the chart was plotted with rules for out of control and warning. In 466 batches, the positive rate exceeded the 1 plus 2SD rule 24 times without consecutive exceedance, which was considered in control. When the positive rate exceeded the 1 plus 3SD rule eight times with consecutive exceedance, it was considered out of control. Further examination revealed that out of control detections had undesirable random error, indicating that contamination may have occurred due to improper operation. This approach demonstrates that Levey-Jennings charts can be adapted beyond traditional quantitative chemistry to monitor process indicators such as positivity rates (A unique Levey-Jennings control chart used for internal quality control in human papillomavirus detection).

Westgard Rules and Their Application

Westgard rules are a set of decision criteria applied to control values to determine whether an analytical run is in control or out of control. The rules are named for James Westgard, who developed the multirule system. A personal recollection of how the Westgard QC rules got their name is available in the clinical chemistry literature (How the Westgard QC Rules Got Their Name: A Personal Recollection).

The commonly used Westgard rules include:

• 1-3s: One control value exceeds the mean plus or minus three standard deviations. This rule detects random error.
• 2-2s: Two consecutive control values exceed the same mean plus or minus two standard deviation limit. This rule detects systematic error.
• R-4s: One control value exceeds the mean plus two standard deviations and another exceeds the mean minus two standard deviations within the same run. This rule detects random error.
• 4-1s: Four consecutive control values exceed the same mean plus or minus one standard deviation limit. This rule detects systematic error.
• 10x: Ten consecutive control values fall on the same side of the mean. This rule detects systematic error.

Quality control rules can be represented by points on a ROC curve when the probability of error detection is plotted against the probability of false rejection while varying only the control limit. This approach allows selection of the optimal control limit analogous to choosing the optimal operating point on a ROC curve. The decision requires knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs, and the cost of false alarm. ROC curve analysis showed that for rules based on N equals 2, mean rules outperform Westgard rules because the ROC curve of the mean rules lay above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during an out of control error condition than comparable Westgard rules (The diagnostic accuracy of quality control rules).

Sigma Metrics for QC Rule Selection

Sigma metrics provide a quantitative measure of method performance that guides QC rule selection. The sigma value is calculated from the total allowable error, the bias, and the coefficient of variation. Higher sigma values indicate better method performance and allow less stringent QC rules. Lower sigma values require more stringent QC rules and more frequent control measurements.

A practical application of Westgard Sigma rules with run size evaluated 18 routine chemical detection methods. Sigma values were calculated based on bias, allowable total error, and coefficient of variation, and appropriate QC rules were selected. At IQC material level 1, seven of 18 assays achieved five sigma, and five assays showed world-class performance. At IQC material level 2, 14 of 18 assays achieved five sigma, and thirteen assays showed world-class performance. The quality goal index was calculated for items with performance below five sigma to determine the main causes of poor performance and guide quality improvement. The authors concluded that Westgard Sigma rules with run size are an effective tool for evaluating biochemical assay performance and can be used to select QC strategies that reduce patient risk (Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings).

A three-step road map to optimal Westgard QC rules was derived from the Westgard OPSpecs Charts QC planning tool and Sigma Metrics formulas. Every Westgard rule has its own sigma value. The road map is based on Sigma Metrics that define world class quality, at which no further effort to increase quality needs to be taken. Clinical chemical tests can be classified as good when quality is at or above world class, bad when quality is below world class but controllable with Westgard QC rules, and ugly when quality is not controllable with Westgard QC rules alone. The use of the road map leads to fast and easy implementation of optimal Westgard QC rules (Practical application of Sigma Metrics QC procedures in clinical chemistry).

Biological variation data can be used to set analytical performance specifications. The European Federation of Clinical Chemistry and Laboratory Medicine regularly updates biological variation data. A study derived analytical performance specifications for tumor markers from EFLM biological variation data and applied Westgard Sigma Rules to establish IQC rules. Precision was calculated from IQC data, and bias was obtained from the relative deviation of EQA group mean values and laboratory-measured values. Total allowable error was derived using EFLM biological variation data. Sigma metrics varied with the level of total allowable error. Most tumor markers except neuron-specific enolase reached three sigma or better based on the minimum total allowable error. With desirable and optimal total allowable error as quality goals, almost all analytes had sigma values below three. Setting the minimum total allowable error as the quality goal allowed each analyte to match IQC multirules and numbers of control measurements according to sigma values (Practical application of European biological variation combined with Westgard Sigma Rules in internal quality control).

External Quality Assessment and Proficiency Testing

External quality assessment, also called proficiency testing, involves analyzing blind samples provided by an external agency and comparing results with peer laboratories. EQA detects systematic bias that may not be apparent from IQC alone because IQC materials are analyzed under the same conditions as patient samples and may share the same matrix effects.

The CDC VITAL-EQA program provided external quality assurance for serum retinol measurements from 2003 to 2006. This program demonstrated the value of sustained EQA participation for nutritional biochemistry markers (The CDC VITAL-EQA program, external quality assurance for serum retinol, 2003-2006).

EQA results should be reviewed promptly after receipt. Unacceptable results require investigation to identify the root cause. Common causes include calibration drift, reagent lot changes, control material deterioration, and calculation errors. The laboratory should document the investigation and any corrective action taken.

Troubleshooting Out of Control Results

When a control value violates a Westgard rule, the laboratory must stop reporting patient results and begin troubleshooting. The following decision tree provides a structured approach to corrective action.

Step 1: Confirm the Violation

Verify that the control value was correctly entered and plotted. Check for transcription errors, wrong control level identification, or incorrect lot assignment. If the violation is confirmed, proceed to the next step.

Step 2: Examine the Pattern

Review the Levey-Jennings chart for the pattern of recent control values. A single point beyond three standard deviations suggests random error. Multiple consecutive points on one side of the mean suggest systematic error. A gradual drift suggests reagent or calibration deterioration.

Step 3: Check the Analytical System

Inspect the instrument for obvious problems. Check reagent levels, expiration dates, and storage conditions. Verify that the correct reagents are loaded and that the calibration is current. Check the sample probe, cuvette, and other mechanical components for blockages or damage. Maintenance and troubleshooting procedures for automated clinical chemistry analyzers have been documented for instruments such as the COBAS C 311 and the Siemens Dimension EXL 200 (EVALUASI PROSES MAINTENANCE, CONTROLLING DAN TROUBLESHOOTING PADA AUTOMATED CLINICAL CHEMISTRY ANALYZER COBAS C 311 DI LABORATORIUM RUMAH SAKIT UNIVERSITAS AIRLANGGA, MEKANISME TROUBLESHOOTING PADA CLINICAL CHEMISTRY ANALYZER SIEMENS DIMENSION EXL 200 DI RSUD Dr. SOETOMO SURABAYA).

Step 4: Analyze a Fresh Control

Open a new vial of control material and analyze it. If the fresh control is in control, the original control vial may have deteriorated or been contaminated. If the fresh control is also out of control, the problem is in the analytical system.

Step 5: Check Calibration

If the fresh control is out of control, recalibrate the method and repeat the control analysis. If the control is in control after recalibration, the calibration had drifted.

Step 6: Document and Report

Document the investigation, the root cause if identified, and the corrective action taken. Record the patient results that were affected and determine whether they need to be repeated. Release patient results only after the system is confirmed to be in control.

Quality Control Frequency and Run Size

The frequency of QC events and the number of control measurements per event should be based on the sigma value of each method. Methods with high sigma values can be monitored less frequently with fewer control measurements. Methods with low sigma values require more frequent monitoring and more control measurements.

Westgard Sigma rules with run size were used to establish IQC standards that reduce patient risk. The sigma values of each assay were calculated based on bias, allowable total error, and coefficient of variation, and appropriate QC rules were selected. This approach allows laboratories to match QC frequency to method performance instead of applying a one size fits all strategy (Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings).

An online calculator for Westgard internal quality control frequency has been applied in general chemistry IQC. This tool helps laboratories determine the appropriate QC frequency based on their method performance data (Application of Westgard internal quality control frequency on-line calculator in general chemistry internal quality control).

Multivariate Quality Control Approaches

Traditional QC uses univariate charts that monitor each control level separately. However, when multiple control levels are analyzed in the same run, the results are often correlated. Multivariate control charts can account for this correlation and provide more efficient error detection.

A control chart based on Hotelling T-squared multivariate statistics was used to monitor an immunoenzymatic assay for plasma levetiracetam. The chart incorporated a multi-level QC system with three concentration levels and included the analytical performance specification for therapeutic drug monitoring. Data comprised 84 consecutive triplets of values for the three QC levels. The initial 59 triplets were used to estimate the variance-covariance matrix and vector of means in phase I. These estimates were applied to calculate Hotelling T-squared for the remaining 25 triplets in phase II. The three QC levels showed significant correlations with r values greater than 0.6 in both control phases. The Hotelling T-squared chart detected no out of specification states compared with 12 out of control signals from individual Levey-Jennings charts monitoring the QC levels separately. The integration of the analytical performance specification into the chart provided additional insights into process quality. The authors concluded that the Hotelling T-squared multivariate chart is effective for IQC of laboratory tests and is advantageous over multiple individual univariate charts because it ensures the correct level of false positive and false negative alarms (Use of Hoteling's T2 multivariate control chart for effective monitoring of a laboratory test with a 3-level quality control scheme).

Quality Control in Multiplex and High-Throughput Methods

Multiplex methods present unique QC challenges because multiple analytes are measured simultaneously from a single sample. A quality assurance program for multiplex quantitative clinical chemistry proteomics was designed for a laboratory-developed multiplex apolipoprotein test measuring nine serum apolipoproteins in 23,376 samples across four LC-MS/MS systems. The ISO 15189 accredited laboratory setting and the total testing process served as the foundation. Quality assurance was organized in three steps: system suitability testing, IQC evaluation with adjusted Westgard rules to fit a multiplex test, and interpeptide agreement analysis. Data were semi-automatically evaluated with a custom R script. Between-run coefficients of variation ranged from 1.9 percent for apolipoprotein B to 6.2 percent for apolipoprotein a, with an average interpeptide agreement Pearson r of 0.981. This study demonstrated the feasibility of high-throughput LC-MS/MS applications in large clinical trials (Quality Assurance for Multiplex Quantitative Clinical Chemistry Proteomics in Large Clinical Trials).

For multiplex methods, the laboratory must decide how to apply Westgard rules when multiple analytes are measured in the same run. Options include applying rules to each analyte individually, applying rules to a summary statistic, or using multivariate approaches. The choice depends on the correlation between analytes and the clinical consequences of reporting incorrect results for each analyte.

Records and Documentation

Documentation is a critical component of any QC program. The laboratory must maintain records of control values, Levey-Jennings charts, Westgard rule violations, investigations, corrective actions, and EQA results. These records provide evidence that the laboratory is monitoring quality and taking appropriate action when problems occur.

The World Health Organization Laboratory Quality Management System Handbook emphasizes the importance of document control and record keeping in the quality management system. Laboratories should establish procedures for creating, reviewing, approving, distributing, and archiving documents. QC records should be retained for a defined period and be available for review by auditors and inspectors (Laboratory Quality Management System Handbook).

The laboratory result release process should be aligned with ISO 15189 requirements. A study on improving the laboratory result release process in light of ISO 15189:2012 described the changes needed to ensure that results are released only when quality requirements are met (Improving the Laboratory Result Release Process in the Light of ISO 15189:2012 Standard).

Common Failure Patterns in QC Programs

Several recurring problems undermine QC programs in clinical chemistry laboratories.

Inadequate Control Material Handling

Control materials that are improperly stored, reconstituted, or handled produce variable results that do not reflect the true performance of the analytical method. Laboratories must follow the manufacturer instructions for storage, reconstitution, and handling. Each new lot of control material must be validated before it is put into use.

Incorrect Mean and Standard Deviation

The mean and standard deviation used for Levey-Jennings charts must be established from sufficient data collected under stable conditions. Using manufacturer target values without verifying them on the local instrument can lead to false rejections or missed errors. Using too few data points to establish the mean and standard deviation can produce unstable control limits.

Ignoring Trends and Shifts

A single control value within two standard deviations may not trigger a Westgard rule violation, but a series of values trending in one direction indicates a developing problem. Laboratories should review Levey-Jennings charts regularly for trends and shifts, also for rule violations.

Delayed Troubleshooting

When a control value violates a rule, the laboratory must act immediately. Delaying investigation while continuing to report patient results increases the risk that unacceptable results reach clinicians. The laboratory should have a written procedure for out of control events that defines responsibilities and timelines.

Poor Documentation

Failure to document control values, investigations, and corrective actions makes it impossible to demonstrate that the laboratory is managing quality. It also prevents the laboratory from identifying recurring problems and implementing preventive action.

Safety Considerations in QC Procedures

Quality control procedures involve handling control materials that may be of human origin. Laboratories must follow biosafety practices to protect staff from potential exposure to bloodborne pathogens. The World Health Organization Laboratory Biosafety Manual provides guidance on risk assessment, containment, and safe handling of biological materials (Laboratory Biosafety Manual).

Control materials should be handled with the same precautions as patient samples. Personal protective equipment, including gloves and laboratory coats, should be worn when handling controls. Work surfaces should be cleaned and disinfected after QC procedures. Waste control materials should be disposed of according to the laboratory waste management procedures.

Method Validation and QC Integration

Quality control is only meaningful when the analytical method has been properly validated. Method validation establishes the performance characteristics of the method, including accuracy, precision, specificity, and detection limits. The U.S. Food and Drug Administration Bioanalytical Method Validation Guidance describes the expectations for validating bioanalytical methods used in regulatory studies. While this guidance is primarily directed at pharmaceutical development, the principles apply to clinical chemistry methods (Bioanalytical Method Validation Guidance).

The Assay Guidance Manual from the National Center for Advancing Translational Sciences provides detailed information on assay development and validation, including quality control considerations (Assay Guidance Manual).

For genomic and molecular methods, analytical validation includes assessment of sensitivity, precision, and reproducibility. A validation study of a genomic newborn screening workflow implemented strict quality control thresholds for sequencing, coverage, and contamination. Longitudinal monitoring confirmed consistent performance across more than 5,900 samples. Automation of DNA extraction improved scalability, and a panel redesign enhanced coverage and selection of targeted regions. By focusing on known pathogenic and likely pathogenic variants, the workflow minimized false positives and maintained clinical actionability (Analytical Validation of a Genomic Newborn Screening Workflow).

Sigma Metrics in Hematology and Other Disciplines

Sigma metrics are increasingly applied beyond clinical chemistry. A retrospective quality assessment evaluated IQC performance of five hematological parameters using sigma metrics. Three-level QC materials were analyzed daily for hemoglobin, WBC, RBC, hematocrit, and platelets. Sigma values were calculated using total allowable error values from Clinical Laboratories Improvement Act guidelines. Hemoglobin and WBC exhibited sigma values greater than 6, indicating excellent analytical performance. RBC and platelets demonstrated acceptable performance with sigma values between 4 and 6. Hematocrit showed a marginal sigma value of 3.74, suggesting the need for improvement in its quality control processes. None of the analytes recorded a sigma value below 3 (Sigma Metric Evaluation of Hematological Parameters: A Retrospective Quality Assessment).

This cross-disciplinary application demonstrates that sigma metrics provide a common language for evaluating analytical performance across laboratory sections.

Professional Escalation Criteria

Laboratory staff should know when to escalate QC problems to supervisors or other responsible personnel. Escalation is appropriate when:

• The root cause of an out of control event cannot be identified after following the troubleshooting procedure
• The same method repeatedly fails QC despite corrective action
• An EQA result is unacceptable and the investigation does not identify a clear cause
• A QC problem affects patient results that have already been reported
• A method has a sigma value below 3 and requires more stringent QC than the laboratory can implement

The laboratory should have a defined escalation pathway that identifies who to contact and what information to provide. Escalation should be documented in the QC records.

Limitations of Quality Control

Quality control has inherent limitations that laboratory professionals must understand. IQC materials are not identical to patient samples. Matrix effects can cause control materials to behave differently from patient samples, leading to false rejections or missed errors. Control materials may not detect all interferences that affect patient samples, such as hemolysis, lipemia, or bilirubinemia.

EQA has limitations as well. EQA samples are distributed a few times per year and may not reflect the performance of the method between distributions. EQA results are affected by the peer group composition, and laboratories using different methods may have different performance expectations.

Sigma metrics depend on the total allowable error chosen. Different total allowable error sources, such as biological variation data or regulatory limits, can produce different sigma values for the same method. Laboratories should document the source of total allowable error used for sigma calculations.

The National Center for Biotechnology Information provides literature resources that can help laboratories stay current with QC best practices and emerging approaches (NCBI Literature Resources).

Frequently Asked Questions

What is the difference between internal quality control and external quality assessment?

Internal quality control involves analyzing stable control materials alongside patient samples in each run to monitor precision and accuracy. External quality assessment involves analyzing blind samples provided by an external agency and comparing results with peer laboratories to detect systematic bias. IQC is performed continuously, while EQA is typically performed several times per year.

How do I establish the mean and standard deviation for a new lot of control material?

Analyze the new control material over 20 to 30 separate runs under stable conditions. Calculate the mean and standard deviation from these data. Verify that the calculated values are consistent with the manufacturer target values. If the calculated mean differs significantly from the manufacturer target, investigate the cause before using the new lot.

What should I do when a control value violates a Westgard rule?

Stop reporting patient results for the affected analyte. Confirm the violation by checking for data entry errors. Examine the Levey-Jennings chart for the pattern of recent values. Inspect the analytical system for obvious problems. Analyze a fresh control vial. If the fresh control is out of control, recalibrate and repeat the control analysis. Document the investigation and corrective action.

How do I choose which Westgard rules to apply?

The choice of Westgard rules depends on the sigma value of the method. Methods with high sigma values can use simpler rules such as 1-3s. Methods with lower sigma values require multirules such as 1-3s, 2-2s, R-4s, 4-1s, and 10x. Calculate the sigma value for each method and select rules that provide adequate error detection with acceptable false rejection rates.

What is a sigma value and how is it calculated?

The sigma value quantifies method performance by combining bias, imprecision, and allowable total error. It is calculated as the difference between the total allowable error and the bias, divided by the coefficient of variation. Higher sigma values indicate better performance. Sigma values above 6 indicate world class quality, values between 3 and 6 indicate acceptable performance with appropriate QC, and values below 3 indicate poor performance that may not be controllable with QC rules alone.

How often should I run quality control materials?

QC frequency should be based on the sigma value of each method. Methods with high sigma values can be monitored less frequently. Methods with low sigma values require more frequent monitoring. At minimum, run two levels of control at the beginning of each day or each run. Increase frequency for methods with sigma values below 4 or when the method has a history of problems.

What is the quality goal index and how is it used?

The quality goal index is a calculation used to determine whether poor method performance is primarily due to imprecision or bias. It guides quality improvement by identifying which aspect of method performance needs attention. If the quality goal index indicates imprecision is the main problem, focus on reducing variability. If bias is the main problem, focus on calibration and standardization.

Can I use pooled patient serum as an in-house quality control material?

Pooled serum can be used as an in-house QC material, particularly in resource-limited settings where commercial controls are too expensive. A study showed that pooled serum glucose controls were stable for approximately five months and provided acceptable precision. However, in-house materials must be validated for stability, target values, and performance before use, and they may not be suitable for all analytes.

Related Diagnostic Guides

• Control Charts in Laboratory Quality Control: Levey-Jennings Plots and Westgard Rules
• Quality Control in the Microbiology Laboratory: Key Practices for Reliable Results
• No Template Control (NTC) in qPCR: Setup, Interpretation, and Troubleshooting
• qPCR Plate Setup: Best Practices for Reproducible Results
• RNA Extraction Using TRIzol Reagent: Protocol, Troubleshooting, and Best Practices

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.
• Evaluation of Sigma Metrics and Westgard Rule Selection and Implementation of Internal Quality Control in Clinical Chemistry Reference Laboratory, Ethiopian Public Health Institute.. Indian journal of clinical biochemistry : IJCB, 2022.
• Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings.. Journal of clinical laboratory analysis, 2021.
• Probability and the Westgard Rules.. Annals of clinical and laboratory science, 2003.
• Practical application of European biological variation combined with Westgard Sigma Rules in internal quality control.. Clinical chemistry and laboratory medicine, 2022.
• Quality Assurance for Multiplex Quantitative Clinical Chemistry Proteomics in Large Clinical Trials.. The journal of applied laboratory medicine, 2024.
• How the Westgard QC Rules Got Their Name: A Personal Recollection.. Clinical chemistry, 2018.
• The diagnostic accuracy of quality control rules.. Scandinavian journal of clinical and laboratory investigation, 2024.
• Practical application of Sigma Metrics QC procedures in clinical chemistry.. Clinical chemistry and laboratory medicine, 2011.
• Development and Characterization of Dual-Platform Lyophilized Plasma-Based Quality Control Materials for Hepatitis C Virus Antibody Testing. 2026.
• Use of Hoteling's T&lt,sup&gt,2&lt,/sup&gt, multivariate control chart for effective monitoring of a laboratory test with a 3-level quality control scheme.. 2025.
• Sigma Metric Evaluation of Hematological Parameters: A Retrospective Quality Assessment.. 2025.
• Internal Quality Controls in the Medical Laboratory: A Narrative Review of the Basic Principles of an Appropriate Quality Control Plan.. 2024.
• Validation of the efficacy of pooled serum for serum glucose inhouse quality control material in comparison with commercial internal quality control in clinical chemistry laboratory.. 2024.
• A unique Levey-Jennings control chart used for internal quality control in human papillomavirus detection.. 2022.
• Analytical Validation of a Genomic Newborn Screening Workflow.. 2025.
• Improving the Laboratory Result Release Process in the Light of ISO 15189:2012 Standard.. Clinica chimica acta, international journal of clinical chemistry, 2021.
• The CDC VITAL-EQA program, external quality assurance for serum retinol, 2003-2006.. Clinica chimica acta, international journal of clinical chemistry, 2008.
• EVALUASI PROSES MAINTENANCE, CONTROLLING DAN TROUBLESHOOTING PADA AUTOMATED CLINICAL CHEMISTRY ANALYZER COBAS C 311 DI LABORATORIUM RUMAH SAKIT UNIVERSITAS AIRLANGGA. 2019.
• MEKANISME TROUBLESHOOTING PADA CLINICAL CHEMISTRY ANALYZER SIEMENS DIMENSION EXL 200 DI RSUD Dr. SOETOMO SURABAYA. 2017.
• Brief communication: Probability and the Westgard rules. Annals of Clinical and Laboratory Science, 2003.
• Application of Westgard internal quality control frequency on-line calculator in general chemistry internal quality control. Chinese Journal of Laboratory Medicine, 2022.

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