Kleihauer–Betke test
Author:
Mikael Häggström [note 1]
The Kleihauer–Betke test stains fetal red blood cells (cells containing HbF) dark reddish-pink, while adult red blood cells will be white to light pink.
Collection
EDTA-containing-tube.
Criteria for fetal cells
A fetal blood cell should be:
- Stained more than approximately half of what is seen in control.
- Not be nucleated or too big (white blood cell are generally also stained).
- Not be too small.
Semi-quantification
This is done for Rh-negative mothers, with the following main indications:
- Fetal demise
- Bleeding due to placenta previa
- Placental abruption
10HPFs (in 40x) are scanned, and fetal RBCs are counted (cells per 10 HPFs, not average per HPF), and classified as:
- 0 - Negative
- 1 - Rare
- 2-5 - Few
- 6-10 - Moderate
- >10 - Abundant
Quantification
This is done for Rh-negative mothers to estimate the number of Rho(D) immune globulin vials to administer.
2000 cells are counted, in order to give a percentage calculated as:
- Fetal RBCs (given in%) = (Fetal RBC count) / (Total cell count) *100
Alternatively, an acceptable estimation can be done by using a micrograph (or a microscopy grid) to estimate the mean number of cells in a certain area, and using the same mean to estimate the number of cells in equally sized areas:
1. Count cells (both adult and fetal) until reaching 100 cells (including each cell in square by square if using a microscopy grid). Take note of how large micrograph area (or how many grid squares) were counted (here designated as x amount), and how many fetal RBCs were counted.
2. Pick another random location (you may randomize again if it is of a significantly different cell density, but do not let your decision be influenced by the number of fetal RBCs in the area or near its edge). Count the total number of cells in the same area size (or same x number of squares), and how many of them are fetal RBCs.
3. Pick another location again, and count the number of cells in the same area size, and how many of them are fetal RBCs.
- If a count of 200-300 cells only shows 0 or 1 stained cell, there only needs to be 1 vial of 300 micrograms Rho(D) immune globulin, and the rest of the steps in this section can be skipped.
| Standard deviation |
Count cells in following number of areas |
|---|---|
| Up to 6 | 3 |
| 7 | 4 |
| 8 | 5 |
| 9 | 6-7 |
| 10 | 8 |
| 11 | 10 |
| 12 | 12 |
- Calculate how much the count for the second and third areas deviated from 100, and take the average thereof, which is the standard deviation. If the standard deviation is higher than 12, count a total of 2000 cells regardless of areas and calculate as per formula above, and the rest of the steps can be skipped.
- Use the table at right to estimate how many areas in total you need to count in order to have a mean number of cells per area with an acceptable confidence interval.
3. Calculate the average of the number of cells per area (or per x number of squares), and assume that number for the rest of the counting.
4. In additional areas, only count the number of fetal cells per area (or x number of squares), until presumably exceeding a total of 2000 cells counted.
Fetal RBCs (given in%) = (Fetal RBC count) / (Presumable total cell count) *100
Example:
-
100 cells are counted, and their area is marked with a rectangle. -
The slide is moved to a new area, and inside the same rectangular area, 84 cells are counted. This is a deviation 16 cells compared to the first count, and therefore, at least 3 more areas should be counted. -
A third area of the same size yields a count of 79 cells,
Calculation of number of vials
Assuming that a vial of 300 micrograms of Rho(D) immune globulin will protect against 30 mL of fetal blood, the number of vials needed to compensate for the fetal-maternal transfusion is calculated as following, rounded up,[1] or rounded to the closest full number and then adding 1.[2]
Number of vials = Fetal RBCs in% * 1.7
Notes
- ↑ For a full list of contributors, see article history. Creators of images are attributed at the image description pages, seen by clicking on the images. See Patholines:Authorship for details.
Main page
References
- ↑ Diann M. Krywko. Kleihauer Betke Test. StatPearls, National Center for Biotechnology Information. Last update: Last Update: January 20, 2020.
- ↑ Practice at Danbury Hospital, Danbury, Connecticut, New England.
Image sources