Sample Testing 1. Application of Biochemical Techniques in Sample Testing Biochemical techniques are used in the laboratory for: - Diagnosis : Helps differentia
Sample Testing 1. Application of Biochemical Techniques in Sample Testing Biochemical techniques are used in the laboratory for: - Diagnosis : Helps differentiate diseases based on symptoms and test results. - Screening : Detects diseases before symptoms appear (e.g., newborn screening for phenylketonuria, thyroid deficiency). - Monitoring : Tracks disease progression, drug effects, or therapy response (e.g., glucose levels in diabetes). - Prognosis : Assesses disease susceptibility (e.g., cholesterol levels to predict heart disease). 2. Sample Testing Process Sample testing involves five key phases: A. Prescription and Sample Collection Phase - The physician orders tests, and samples are collected appropriately. B. Preanalytical Phase ( Most error-prone phase ) Involves: - Preparation for sample collection (diet, posture, urine container selection). - Preparation of sampling (defining request, entering request, labeling tubes). - Sampling (patient ID verification, timing, site selection, tourniquet use, needle positioning). - Transport (collecting, moving samples to the lab). - Sample treatment (registration, centrifugation, mixing, identification, extraction). - Storage (time, site, temperature, remixing after storage). C. Analytical Phase - Sample is processed using biochemical techniques (e.g., spectrophotometry, electrophoresis). D. Post-Analytical Phase - Data interpretation, validation, and result verification before reporting. E. Reporting of Results - Final test results are communicated to clinicians for diagnosis and treatment. 3. Challenges in the Preanalytical Phase Errors can occur due to: - Interface between the ward and laboratory (miscommunication, sample mishandling). - Organizational and technical issues (staff training, equipment failures). - Complexity of increasing test requirements . Types of Preanalytical Errors - Patient preparation errors (improper fasting, wrong posture). - Sampling errors (wrong anticoagulant use, incorrect tube selection). - Storage errors (wrong temperature or duration). - Transport errors (delays, exposure to heat/light). - Instrumentation issues (incorrect centrifugation). - Special pretreatment errors (failure to add stabilizers or preservatives). 4. Factors Affecting Sample Stability & Transport To maintain sample integrity, consider: - Time interval between collection and testing. - Proper transport methods (timely delivery, temperature control). - Storage conditions (temperature, duration before analysis). - Sample collection apparatus (correct tubes, anticoagulants). - Reagents and processing techniques (avoiding contamination, degradation). 5. Key Takeaways - Preanalytical errors are the most common cause of inaccurate results. - Proper patient preparation, sampling, transport, and storage ensure reliable test outcomes. - Prevention of errors requires proper training, equipment use, and adherence to protocols. Errors in Clinical Analysis 1. Definition of Biochemical Errors - Errors occur at any stage of the laboratory cycle (from ordering tests to interpreting results). - Can be pre-analytical, analytical, or post-analytical . - Measurement errors depend on:The type of test. - The equipment used. - The technique of the analyst. 2. Classification of Errors Errors are classified based on: A. Quantitative Analysis (Experimentation) Errors - Gross Errors ( Serious errors that require a complete restart ) Examples: Instrument failure, contaminated reagents. - Determinate (Systematic) Errors ( Can be identified and corrected ) Types: Instrumental errors (e.g., miscalibrated balance, faulty pipettes). - Reagent errors (impure chemicals, reactions with glassware). - Personal errors (misreading a burette, improper technique). - Operational errors (loss of material, poor sample handling). - Method errors (incomplete reactions, incorrect sampling). - Detection methods: Comparing results from different methods. - Using blank samples to check for contamination. - Inter-laboratory testing to compare variations. - Indeterminate (Random) Errors ( Uncontrollable and unpredictable errors ) Sources: Personal uncertainty (inability to detect small changes). - Methodological uncertainty (errors in reagent dilution, wrong indicators). - Instrumentation issues (temperature fluctuations, electrical noise). - Minimization strategy: Perform tests in triplicate for accuracy. B. Errors Based on Analytical Control Limits - Unpredictable Errors Systematic Shift ( Consistent deviation from the mean )Happens when multiple results stay on one side of the average value. - Causes: Changes in reagent sensitivity, degraded standards. - Systematic Trend ( Gradual increase or decrease in test results over time ) Causes: Deteriorating reagents, standard solution changes, incomplete reactions. - Predictable Errors Errors that can be controlled and corrected by following proper procedures. 3. Errors Based on the Stage of Analysis - Pre-Analytical Errors ( Before the test is conducted ) Causes: Improper patient preparation, incorrect sample collection, wrong transport/storage. - Analytical Errors ( During the test process ) Causes: Instrument malfunctions, reagent errors, incorrect procedures. - Post-Analytical Errors ( After the test is completed ) Causes: Data misinterpretation, incorrect result reporting. 4. Key Takeaways - Systematic errors affect accuracy , while random errors affect precision . - Gross errors require retesting , while determinate errors can be corrected . - Pre-analytical errors are the most common and must be minimized with proper procedures. - Performing tests in triplicates and using control samples help detect and minimize errors. (Continued) 1. Wild Errors - Definition: Sudden, temporary errors due to unexpected events. - Examples: Using a chipped pipette. - Wrong dilution or improper technique. 2. Predictable Errors - Definition: Irregular errors within a known range due to test method limitations or human factors. - Characteristics: Results deviate from the correct value by varying amounts. - Follow a normal frequency distribution. - Examples: Using the wrong wavelength in spectrophotometry. - Touching the microscope's cover glass with the objective lens. - Improper calibration of reagent dispensers. 3. Errors Based on the Stage of Analysis A. Pre-Analytical Errors (Before Testing) - Definition: Errors occurring before a sample is analyzed, affecting result accuracy or integrity. - Common causes: Haemolysis: Breakdown of red blood cells affecting results. - Misidentification: Incorrect patient or sample labeling. - Sampling errors: Using the wrong tube. - Insufficient or excessive sample volume. - Clotting: Improper handling of blood samples. - Missing requests or samples. - Incorrect patient preparation: Fasting, medication interference. Types of Pre-Analytical Errors 1. Identification Problems - Importance: Ensures correct linking of sample to test request. - Identification process: Verbal patient confirmation. - Matching patient ID to request form. - Labeling samples at bedside for a three-way check . - Lab verification of sample ID with request form. - Key Identification Issues (as per KIMMS Quality Assurance Program): Unlabeled sample. - Mislabeled sample (incorrect or mismatched patient details). - Insufficiently labeled sample (less than two identifiers). - Transfusion labeling errors (missing signature, date, time). - Wrong patient sample (e.g., wrong blood in the tube). - Consequences of Identification Errors: Incorrect patient results leading to:Unnecessary treatment/investigations. - Missed treatment. - Potential fatal outcomes. - Errors often detected by chance , using: Historical blood grouping mismatches. - Significant changes in serial lab testing results. - Inconsistent results with patient diagnosis (e.g., cancer, genetic disorders). - Laboratory rechecks due to collector concerns or missing records. - Error Prevention: Zero-tolerance policy for identification errors. - Reject compromised sa