1. · Lecture 31 & 32 : Scope of clinical biochemistry ط Uses of clinical biochemistry tests ط Diagnosis, Prognosis, Screening, Monitoring ط Reporting results ط Near patient testing M3 1-4; Z6 434 – 40

2. Use of Biochemical Tests: Provide biochemical information For the management of patients To guide a clinical decision (if accurate) Bio tests use in Relation to diseases that have obvious metabolic basis (e.g. diabetes) Biochem changes consequence of disease (e.g. renal failure) Diagnosis, prognosis, monitoring & screening (fig 1.1)

3. Diagnosis Possible disorder, bases on: Patient history Clinical signs during examination Combined with previous to bring a short-list of possibilities) Results of the Investigation Correct selection for confirmation or prove false Response to treatment Incomplete allow initiation (e.g. hypoglycemia)

4. Prognosis Likely outcome of a disease (prediction) Tests can be for information of diagnosis or prognosis e.g. serial measurement of plasma creatinine conc. In renal failure indicates time for dialysis Tests can give information of risk factors for developing a condition e.g. increase of plasma cholesterol indicates risk of coronary artery disease

5. Monitoring History / Response to treatment Need availability of analyte (e.g. glucose in diabetes) To follow course of illness To monitor effect of treatment Detect complications of drug (in use or trial): e.g hypokalemia during diuretics

6. Screening Detect disease in groups e.g. mass screening of newborns (e.g. PKU)

7. Analysis The Ideal analytical Method is: accurate, precise, sensitive, & specific The method need to be: cheap, simple & quick to perform Lab staff are required to achieve rigorous quality control No test is Ideal … but must be sufficient & reliable to be used

8. Reporting Analysis  QC check  Report Computers used for: Store data (results) & data processing (reports) working with analyzers (on- off-line) Allow trends in data to be picked out at ease and at glance

9. Near-Patient testing Not all analytes performed in central lab(bedside, clinic) Regent sticks: for urine testing of glucose, protein, bilirubin, ketones, nitrites (infections) Blood testing: for glucose, cholesterol, hydrogen ion (pH), gases, certain drugs ….ect Pateints can monitor the glucose at home Used: rapid intervention or change treatment Results need to be reliable as man lab results require training & adherence to protocols supervised by main lab

10. · Lecture 37& 38 : Quality control Assurance ط Quality control, sample analysis and reporting results ط Precision and accuracy ط reference value, Gaussian distribution ط Specificity, sensitivity and predictive value of tests B3 27- 37 ; C4 28 - 40 ; M3 4-10

11. Sample Analysis: Test request form should include: name / sex / # / DOB / Dr. / Diagnosis / tests …. Collected & transported according to specific procedures: Repeated tests for standardization:

12. Sample Analysis: Test request form should include: name / sex / # / DOB / Dr. / Diagnosis / tests …. Some analytes effected by variables fig 1.2 Treatment (drug) to assess results: e.g. estrogen causes increase in thyroxin Collected & transported according to specific procedures: Samples must be appropriate for test request (plasma / serum), since some are critical Serum: e.g. protein electrophoresis Plasma: e.g. rennin activity Samples are potentially infectious:urine / spinal fluids / blood …. Hemolysis must be avoided / intervenes therapy contamination / correct preservative / correct lable / transport (if necessary refrigeration / freezing)

13. Sample Analysis: Repeated tests for standardization: Clustering distribution (Gaussian) fig 1.4 Scattered distribution can be assessed by SD (less scatter for analytes that are strict regulation as glucose & Ca++) SD = √M2M= total / number SD: random variation / probability of distribution / spread of values Variation can be assessed as CV CV = SD / M X 100

14. Ideal Analytical Method: fig 1.3 Accurate: gives correct result (closest to true values) Precise: same if repeated (less scatter), analysis of same sample by different methods Degree of variation (imprecision) can be assessed using same method Imprecision can be expressed as CV Sensitive: measures low concentrations of analytes Specific: not subject for interference by other substances

15. Interpretation of Results: Is it normal ?is it different from previous?Is it consistent with findings? Normal ? Distribution of values from repeated measurement of the same quantity close to mean Calculate SD to establish normal range of analytes from large number of healthy people Lab uses numbers as reference range to avoid using normal range Further investigations are important for decision in patient management Before making a decision more information on PV predictive value

16. Interpretation of Results: Is it normal ?is it different from previous?Is it consistent with findings? Normal ? Distribution of values from repeated measurement of the same quantity close to mean Bell-shape Gaussian Distribution Curve of variables fig 1.4: Zero Skewed distribution 95% within normal range ±2 SD Calculate SD to establish normal range of analytes from large number of healthy people Only identify the range of values occur most in individuals Normal range assessed when physiological factors affect analytes conc. (in population: sex / age.. ect)

17. Interpretation of Results: Is it normal ?is it different from previous?Is it consistent with findings? Normal ? Lab uses numbers as reference range to avoid using normal range Tests results could be abnormal for an individual yet within reference range Further investigations are important for decision in patient management To avoid false negativity Abnormal may fall within 5% The more tests are performed the greater probability of getting correct result Before making a decision more information on PV predictive value Probability related to pathological process

18. Interpretation of Results: Is it normal ?is it different from previous?Is it consistent with findings? Different ? To compare results: decide if difference is significant, depending on: Precision of the assay (SD) – QC Natural biological variations (MSD) – 10WKs Probability of significant difference at the level of p <0.05 is more than 2x SD

19. Interpretation of Results: Is it normal ?is it different from previous?Is it consistent with findings? Consistent ? If results are consistent with clinical findings Evidence in favor of diagnosis If not:explanation is required: Mistake in collection, labeling, analysis, reporting Repeat test with new sample If still same: Checking sensitivity & specificity of test should be considered Clinical diagnosis may be reviewed

20. At Home False Negative False Positive Efficiency Predictive Value