Concluding Information and Courses from MediaLab, Inc.
These are the MediaLab courses that cover Concluding and links to relevant pages within the course.
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| Antibody identification checklist To improve the quality of conclusions when identifying antibodies, a checklist is a simple quality control tool to increase transfusion safety. If a specific antibody pattern cannot be identified with acceptable confidence, or if significant serologic or non-serologic data are inconsistent and cannot be rationalized, further testing will be required.Before concluding that the investigation is complete, unless not applicable, mentally reply to each question in the checklist. If any answer is no, has it been resolved? Antibody Identification Checklist Yes/No/NA 1. For a single antibody, does the reaction pattern fit only one antibody specificity? 2. Is antibody specificity consistent with the results of the initial antibody screen? 3. Are reaction phases consistent with antibody specificity? 4. If multiple antibodies are present, can all reactions be explained by the antibody combination? 5. If the autocontrol is negative, are patient red cells negative for the corresponding antigen(s)? 6. Have additional possible antibodies been excluded by selected red cells? 7. Can all variable reaction strengths be explained? 8. If tested, are antigen-negative donor cells compatible by antiglobulin crossmatch? 9. If there are data that do not fit antibody specificity or if there are results that are improbable, are they explainable? 10. Have all results and conclusions been systematically evaluated for consistency? | View Page |
| The p value in this case This CaseWith the panel done 2 weeks post-transfusion, 5 panel cells that were Jk(a+) reacted and 5 that were Jk(a-) did not. This yields a p value of 0.004, which is less than the standard of 0.05, and therefore is more than acceptable statistically. In other words, an antibody other than anti-Jka would be expected to produce these panel results only 4 times in 1000 (which is pretty unlikely).Th true p value is much lower because many more cells were tested than in the panel alone.Concluding that the antibody is anti-Jka is further strengthened because the patient's red cells type as Jk(a-).Learning points: The most important things to know about statistical tools such as p values are that they Relate to the probability of getting the observed results if the null hypothesis were true (the panel results were due to another antibody) Do not substitute for technical and clinical judgment. | View Page |
| Understanding the "rule of three" In immunohematology textbooks, the "rule of three" is sometimes presented as follows:1. If a patient plasma or serum gives positive results with a minimum of three antigen-positive cells and negative results with a minimum of three antigen-negative cells, concluding that the serum contains an antibody directed against the antigen has a p value of 0.05.2. Therefore, a p value of 0.05 requires at least three positives and three negatives.The first statement is correct but second statement is a misinterpretation of the p value.Three positives and three negatives are required to identify an antibody with a p value of 0.05 ONLY if you have only a 6-cell panel. It does not mean that you always need three positive cells and three negative cells to get p=0.05.For example: A 10-cell panel with eight Jk(a+) cells and two Jk(a-) cells gives a probability of 0.02 if all the positive cells and none of the negative cells react. A 10-cell panel with eight K- cells and two K+ cells gives a probability of 0.02 if all the positive cells and none of the negative cells react. Learning point: You do not need three positive cells and three negative cells to get an acceptable p value of 0.05. | View Page |