LMP LECTURE TWO.docx

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Department
Laboratory Medicine and Pathobiology
Course
LMP299Y1
Professor
All Professors
Semester
Winter

Description
LECTURE TWO: LAB TESTS AND THEIR INTERPRETATION CASE ONE  A blood specimen was taken from a 55-year old woman to check her serum potassium  The patient is on diuretics to control her hypertension but she is otherwise feeling well and the check-up did not reveal any problems  The result came back 35mmol/L. (Normal is 3.5 – 5.0)  The physician was not worried. Why? Because there could have been a range of lab testing errors that could have occurred ANALYTICAL FACTORS  Methods ( or assays) for chemicals analysis may affect the final result  The appropriate choice is determined by the Laboratory Professionals  For example: sodium can be measured by flame photometry, electrodes or colorimetrically  Some methods measure sodium concentration and others sodium ion activity which depends on sodium in solution and therefore water content IDEAL METHODS oNeed little or no sample oThe sample must be easily obtained oGive results instantly oCost nothing to do oAccurate oPrecise oFree from interference oAppropriate range for measurement oSensitive for low concentrations ANALYTICAL ERRORS  The methods in the clinical laboratory have some uncertainty in them  Not the “best” methods (due to expense and time) but are cheap, give results quickly and on small samples  Random Errors (or imprecision): small variations in ambient temperature, viscosity of fluids, electrical surges, operator technique, etc  Systematic Errors (or bias): due to differences in standardization and calibration of the methods PRECISION VS. ACCURACY  Precision: how well repeated measurements on the same sample agree with one another - “Can you keep telling the same lies?”  Accuracy: how close a measurement is to the true value - “Can you tell the truth?” STATISTICAL QUALITY CONTROL  Laboratory staff uses Quality Control (QC) to define and monitor for error  A significant portion of tests in a North American laboratory are to do with Quality Assurance  In many clinical situations it is more important for a test to be precise than accurate  The best informed physicians are cognizant (fully informed) of the analytical performance of the laboratories that they use INTERFERENCES  Are constituents in the sample that alter the measurement of the desired analyte and lead to an erroneous result  May be due to cross-reactivity (where molecules that are similar in structure react together), light interference (optical interference), consumption of reagent, non-specific effects, etc.  Common ones that can lead to false results: - Hemolysis (where cells break open) - Lipemia (a lot of lipid production) - Bilirubinemia (icteria) (break down product of RBC metabolism) - Drugs (invitro – can affect your endogenous compounds)  Affect depends on the nature of assay POST-ANALYTICAL FACTORS  A patient is either healthy or sick. The test should be able to help you tell these populations apart  In the management of disease and in prognostication we use the laboratory test to assess if the disease is getting worse or better  Reference Interval (reference or “normal” range): is the range of values in health or disease though its usually for healthy population in order to establish reference intervals  To perform a study: - usually a healthy (or diseased) population is recruited - laboratory test measurements are made  Interval defined by the central 95% of the values derived from the population  Usually the bottom 2.5% and the top 2.5 % are excluded  Not all measurements that are taken are not symmetric, they are skewed IS THE NORMAL REALLY HEALTHY? oNot all populations are the same oRange of results may not have a Gaussian distribution oNo apparent disease does not mean healthy (e.g. serum cholesterol (don’t use reference intervals for cholesterol) or body weight) oStatistical requirement is 120 people (minimum) oMay be influenced by pre-analytical factors (age, sex, etc.); if different subgroup need to created oThe best reference range in health is the patient’s own when healthy! RESULTS OUTSIDE NORMAL RANGE  This could mean: 1) The patient is sick 2) Patient is well but is a statistical outlier 3) Patient is well but is not of the age, sex, race group, etc. of the reference range population 4) Patient is well but is carrying out a proscribed activity such as jogging or eating before the sampling was made (influences the result)  Note that the population’s reference range may not be the healthy reference range CHANGE OR VARIATION?  Need to distinguish between health and disease  But how much imprecision in the analysis? WHAT IS A REAL CHANGE?  When two tests are done, how much difference between the results can there be before the change is significant? - (total variance) = (analytical variance) + (biological variance) - 95% probability limit = 2.80 x total variance  The total variance times 2.80 gives you a number which is the 95% probability limit (19 times out of 20) of the change in the result which can be expected because of analytical imprecision and the individuals biological variation over time CASE TWO oA man on a hunger strike is noted to have his serum albumin change over 3 months from 40 g/L to 30 g/L oIs this a significant change? oThe analytical variance of the test is 3% and the biological variance 10% oAnalytical variance is 3% (i.e. imprecision) - 3% of 40g/L = 1.2 g/L oBiological variance is 10% (i.e. day-to-day changes) 2 2 oTotal variance: = √(Var A2+ VarB)2 = √ (1.2) + (4.0) = 4.2 g/L = @ 95% CI = 4.2 x 2.80 = 11.7 g/L o11.7 g/L is the : this is the change that would have had to occur in order for the value to be statistically significant oChange from 40  30 = 10 g/L; less than RCV CASE THREE  In a shopping mall, a diabetes charity was fund raising by offering laboratory tests to the public  A 12 year boy was found to have a blood glucose of 16 mmol/L  When he got home, soon after the test, a friend of his, a recently diagnosed diabetic, had some urine dipsticks and showed that the boy had glucose in his urine  Later testing at the hospital showed that the boy was not diabetic  What went wrong: - What pre-analytical variables could have affect this result? - What affects glucose levels? - When should a sample be taken? - When is significance of glucose appearing in urine? - What is range for interpretation? - Which test is right, the mall meter or hospital
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