Process Control: Quality Control for Quantitative Tests 1
Process Control: Quality Control for Quantitative Tests 1 Learning Objectives At the end of this module, participants will be able to: Differentiate accuracy and precision. Select control material for the laboratory. Establish acceptable control limits for a method when only one level of control material is available. Explain the use of a Levey-Jennings chart.
Describe how to correct out of control problems. Quantitative QC - Module 7 2 The Quality Management System Quantitative QC - Module 7 Organization Personnel Equipment Purchasing & Inventory Process Control
Information Management Documents & Records Occurrence Management Assessment Process Improvement Customer Service Facilities & Safety
3 Quantitative Tests measure the quantity of a particular substance in a sample quality control for quantitative tests is designed to assure that patient results are: accurate reliable Quantitative QC - Module 7 4 Implementation steps
establish policies and procedures assign responsibility, train staff select high quality controls establish control ranges develop graphs to plot control values Levey-Jennings charts monitor control values develop procedures for corrective action record all actions taken Quantitative QC - Module 7 5 What is a Control?
material that contains the substance being analyzed include with patient samples when performing a test used to validate reliability of the test system run after calibrating the instrument run periodically during testing Quantitative QC - Module 7 6 Calibrators vs. Controls
Quantitative QC - Module 7 7 Calibrators Controls A substance with a specific concentration. A substance similar to patients samples that has an established concentration. Calibrators are used to set (calibrate) the measuring points on a scale. 1 2
3 Quantitative QC - Module 7 4 5 Controls are used to ensure the procedure is working properly. 4 5 1 2 3 8 Characteristics of Control Materials appropriate for the diagnostic sample values cover medical decision points similar to test sample (matrix)
available in large quantity; ideally enough for one year can store in small aliquots Quantitative QC - Module 7 9 Types of Control Materials may be frozen, freezedried, or chemically preserved requires very accurate reconstitution if this step is necessary Quantitative QC - Module 7 10 Sources of Controls Materials
commercially prepared made in house obtained from another laboratory, usually central or reference laboratory Quantitative QC - Module 7 11 Control Materials ASSAYED Target value predetermined Verify and use Target value not predetermined
UNASSAYED Full assay required before using In-house pooled sera IN-HOUSE Full assay, validation Quantitative QC - Module 7 12 Choosing Control Materials values cover medical decision points similar to the test sample controls are usually available in high, normal, and low ranges Quantitative QC - Module 7 13
Preparation and Storage of Control Material adhere to manufacturers instructions keep adequate amount of same lot number store correctly CONTROL Quantitative QC - Module 7 14 Steps in Implementing Quantitative QC obtain control material
run each control 20 times over 30 days calculate mean and +/1,2,3 Standard Deviations 3SD 2SD 1SD Mean Quantitative QC - Module 7 1SD 2SD 3SD 15 Measurement of Variability Variability is a normal occurrence when a control is tested repeatedly
Affected by: Operator technique Environmental conditions Performance characteristics of the measurement The goal is to differentiate between variability due to chance from that due to error Quantitative QC - Module 7 16 Measures of Central Tendency Although variable, sets of data are distributed around a central value F
r e q u e n c y Measurement Quantitative QC - Module 7 17 Measures of Central Tendency Mode the value which occurs with the greatest frequency Median the value at the center or midpoint of the observations Mean
Quantitative QC - Module 7 the calculated average of the values 18 Not all central values are the same Mean F r e q u e n c y Mode Median Measurement
Quantitative QC - Module 7 19 Symbols Used in Calculations is the sum of (add data points) n = number of data points x1 - xn = all of the measurements through n) __ X represents the mean Quantitative QC - Module 7 (1 20 Calculation of Mean X 1 X 2 X 3 ... X n X n
X = Mean X1 = First measurement X2 = Second measurement Xn = Last measurement in series n = Total number of measurements Quantitative QC - Module 7 21 Example Calculation of Mean: ELISA Tests Run controls 20 times in 30 days. Record both OD and cut off (CO) values for each measurement.
Divide the OD by the CO (OD/CO) for each data point or observation. This standardizes the data. Add the ratios and divide by the number of measurements to get the mean. Quantitative QC - Module 7 22 Data showing outlier mg/dL 2. mg/dL 3. mg/dL 4. mg/dL 5. mg/dL
mg/dL mg/dL mg/dL mg/dL mg/dL mg/dL mg/dL mg/dL mg/dL 23 Normal distribution all values symmetrically distributed around the mean characteristic bell-shaped curve assumed for all quality control statistics Frequency Quantitative QC - Module 7
mean 24 Quality Control is used to monitor the accuracy and the precision of the assay. What are accuracy and precision? Quantitative QC - Module 7 25 Definitions Accuracy The closeness of measurements to the true value Precision The amount of variation in the measurements Bias
Quantitative QC - Module 7 The difference between the expectation of a test result and an accepted reference value 26 Accuracy and Precision Accurate and Precise Precise but Biased Imprecise Accurate = Precise but not Biased Quantitative QC - Module 7 27 Standard Deviation and Probability
+ 1 SD 68.3% of the time X Frequency For a set of data with a normal distribution, a random measurement will fall within: 68.2% 95.5% 99.7% + 2 SD 95.5% of the time + 3 SD 99.7% of the time Quantitative QC - Module 7 -3s- 2s
+3s -1s Mean +1s +2s 28 Standard Deviation (SD) SD is the principle measure of variability used in the laboratory SD (x1 x ) n 1 2
Standard Deviation Statistical Formula Quantitative QC - Module 7 29 Coefficient of Variation The coefficient of variation (CV) is the SD expressed as a percentage of the mean. SD CV x 100 % mean CV is used to monitor precision CV is used to compare methods CV ideally should be less than 5% Quantitative QC - Module 7
30 Levey-Jennings Chart Graphically Representing Control Ranges Quantitative QC - Module 7 31 Statistics for Quantitative QC assay control material at least 20 data points over a 20-30 day period ensure procedural variation is represented calculate mean and + 1, 2 and 3 SD
Quantitative QC - Module 7 32 Draw lines for Mean and SDs (calculated from 20 controls) Chart name: Lot number: 196.5 +3SD 194.5 +2SD 192.5 +1SD MEAN
Days 11 12 13 14 15 16 17 18 19 34 Number of Controls Interpretation depends on number of controls run with patients samples.
Good: If one control: accept results if control is within 2SD unless shift or trend Better: If 2 levels of controls apply Westgard multirule system Quantitative QC - Module 7 35 Detecting error random error: variation in QC results with no pattern- only a cause for rejection if outside 2SDs. systematic error: not acceptable, correct the source of error
Examples: shiftcontrol on one side of the mean 6 consecutive days trendcontrol moving in one direction heading toward an out of control value Quantitative QC - Module 7 36 Levey-Jennings Chart Shift 196.5 +3SD 194.5 +2SD 192.5
+1SD MEAN 190.5 188.5 -1SD 186.5 -2SD 184.6 -3SD Days Quantitative QC - Module 7 37 Levey-Jennings Chart
184.6 -3SD Days Quantitative QC - Module 7 38 Measurement Uncertainty represents a range of values in which the true value is reasonably expected to lie is estimated at 95% coverage the more precise the method, the smaller
the range of values that will fall within 95% for most instances, a range of + or - 2 SDs is accepted as measurement uncertainty that is explained by random variation Quantitative QC - Module 7 39 If QC is out of control STOP testing identify and correct problem repeat testing on patient samples and controls after
correction Do not report patient results until problem is solved and controls indicate proper performance Quantitative QC - Module 7 40 Solving out-of-control problems identify problem refer to established policies and procedures for remedial action Quantitative QC - Module 7
41 Possible Problems degradation of reagents or kits control material degradation operator error failure to follow manufacturers instructions an outdated procedure manual equipment failure calibration error Quantitative QC - Module 7 42 Summary A quality control program for quantitative tests is essential. It should: monitor all quantitative tests
have written policies and procedures, followed by laboratory staff have a quality manager for monitoring and reviewing QC data use statistical analysis, provide for good records provide for troubleshooting and corrective action Quantitative QC - Module 7 43 Key Messages
A QC program allows the laboratory to differentiate between normal variation and error. The QC program monitors the accuracy and precision of laboratory assays. The results of patient testing should never be released if the QC results for the test run do not meet the laboratory target values. Quantitative QC - Module 7 44 Organization
Personnel Equipment Questions? Purchasing & Inventory Process Control Information Management Documents & Records Occurrence Management Assessment
Process Improvement Customer Service Quantitative QC - Module 7 Comments? Facilities & Safety 45
Tools for analyzing (measuring) economic development. And, measuring impact of development policy. Measure: Proportion of economic activity that is related to locality, versus part related to regions external to area. Assess regions performance relative to other regions. Determine which industries...
Tensile stresses away from the ends of unbonded post-tensioned precast coupling beams remain small as a result of the development of a diagonal compression strut. Transverse mild steel reinforcement is needed at the beam ends where localized critical tensile stresses...
menagerie, Mrs. Roylott's will, the recent . engagement of Helen Stoner, the unexpected "remodeling" of Stoke Moran. The gypsy "band" ... And moveless fish in the water gleam,By silver reeds in a silver stream. ...
Immunotoxicity. Nephrotoxicity. Hepatotoxicity. Neuro-immunotoxicity. Phototoxicity. CSIR-INDIAN INSTITUTE OF. TOXICOLOGY RESEARCH. COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH. Models and methods for the toxicity/safety of ENMs and products . In vivo . models . Immunotoxicity.
Goal Setting Why Should You Use Goal Setting? What is a Goal Anyway? According to Merriam-Webster a goal is: the end toward which effort is directed. Goals are dreams and wants except a goal is more specific. The Purpose of...
An inference engine is a software able to infer new facts from a set of asserted facts and rules. Non-ontology Inference engines are also to derive decision based on business rules (rules engine). The reasoner generalizes the concept with a...
May 9, 2017 Nashville Chapter May 9, 2017 2017-18 Nashville Chapter President John Dotson Nashville Chapter Thank You! May 9, 2017 Nashville Chapter May 9, 2017 Mr. Paul Sobel, CIA Vice President and Chief Audit Executive, Georgia-Pacific Former Global Chairman...
Ready to download the document? Go ahead and hit continue!