Brief Scientific Reports
Analysis of Manual Reticulocyte Counting
DEBORAH A. PEEBLES, H(ASCP), ALAN HOCHBERG, B.S.E.E., AND THOMAS D. CLARKE. PH.D.
Peebles, Deborah A., Hochberg, Alan, and Clarke, Thomas D.:
Analysis of manual reticulocyte counting. Am J Clin Pathol
76: 713-717, 1981. A statistical appraisal of manual reticulocyte enumeration was extensively investigated. Statistically,
the counting variability among technologists was significantly
worse than expected for a counting (Poisson) process. The proportional error associated with each technologist can exceed
30%. The technologist-to-technologist variation is the major
source of inaccuracy at all reticulocyte levels and is attributed
to the consistent application of individual criteria in reticulocyte identification. Although results may be clinically useful,
it is extremely difficult to obtain manual results with sufficient
accuracy to serve as a "reference" reticulocyte method. (Key
words: Reticulocyte; Statistical error study.)
APPROXIMATELY four million reticulocyte tests
were requested by clinicians in the United States in
1979.8 Notwithstanding, significant discrepancies in the
designation of normal value limits appear among reports by Miale,10 Wintrobe 12 and Deiss.4 Although the
reticulocyte count is used as an indicator of erythropoietic activity and has diagnostic and prognostic value,
particularly in acute hemorrhage and hemolytic anemia, or in response to iron, vitamin B12 and folic acid
therapy, its clinical usefulness may be routinely compromised due to manual counting inaccuracies.
The inherent limitations of the reticulocyte count
have been associated with distributional variability of
the blood smear,9 staining variations,4 limited number
of reticulocytes actually counted 5 and differences in
morphologic identification by the technologist.6 Cognizant of these deficiencies, the present study attempts
to define and characterize the expected statistical error
of manual reticulocyte enumeration and its limitation
as a reference method.
Materials and Methods
For each of 14 EDTA whole blood specimens, four
to ten hours old, reticulocyte blood films were prepared
Received December 15, 1980; received revised manuscript and accepted for publication June 9, 1981.
Address reprint requests to Ms. Peebles: Ortho Diagnostic Systems,
410 University Avenue, Westwood, Massachusetts 02090.
Ortho Diagnostic Systems, Westwood,
Massachusetts
after staining with New Methylene Blue1 using a Corning Larc spinner to minimize distributional inconsistency.9
Reticulocyte slides from the 14 patient specimens
were counted repetitively during a five week study, by
seven experienced medical technologists employed at
four different hospital hematology laboratories in the
Boston area. Each technologist counted the same four
slides per specimen (7 technologists X 4 counts X 14
specimens). The 14 specimens included a distributional
range of reticulocytes from approximately 0-15%.
The microscopic slides were coded using random
number nomenclature of enforce a blind study. The
technologists were requested to perform reticulocyte
counts in their usual clinical manner expressed as a
percentage of 1,000 erythrocytes per spun slide.2 The
seven technologists claimed to identify a reticulocyte
as an erythrocyte containing granular or reticulum-like
inclusions with appropriate staining characteristics. 2
The present study was conducted at Ortho Diagnostic
Systems. Each technologist counted at least 14 slides
at each sitting. Nikon model SC microscopes with 100X
oil objectives (excluding the aid of any reticle) and Clay
Adams two-unit laboratory counters were used.
Results
The mean reticulocyte value derived by a given technologist was plotted against the "grand" mean obtained
from the seven technologists for each of the 14 specimens arranged from low to high counts. Fig. 1 illustrates the technologist linearity and the effect of proportional error at different reticulocyte levels. Each
technologist is identified by a capital letter. The minimal crossing of the individual regression lines suggests
that a technologist who counts higher on elevated reticulocyte samples also counts higher on low reticulocyte samples and vice versa. The graphics suggest that
0002-9173/81/0011/0713 $00.75 ©American Society of Clinical Pathologists
713
PEEBLES ET AL.
714
A.J.C.P. • November 1981
RETICULOCYTE COUNTS
7 TECHNOLOGISTS / 14 SAMPLES
<
u
FIG. I. Seven technologists individual means plotted against the "grand"
mean for each of the 14
specimens. Each technologist is identified by a capital
letter.
(ft
H
(ft
5
o
o
III
GRAND MEAN
each of the seven technologists possess individual morphologic identification criteria for enumeration of reticulocytes and that these criteria are consistently applied irrespective of reticulocyte level.
Z score computation is useful for normalization of
results to illustrate where each count falls in relationship to the mean. The formula used to compute Z scores
is: (Tech x-"grand" x) -r- SD of "Tech" x. Technologist
Z scores for the 14 specimens were plotted in consecutive order from low to high to display proportional
error at all reticulocyte levels with the exception of an
apparent 0.0% reticulocyte specimen (Fig. 2). The results are consistent with those found in Fig. 1.
The seven technologists' correlation coefficients,
slopes, and y-intercepts for the reticulocyte values from
14 specimens are shown in Table I. Random error is
reflected in the variability of counts about the regression
line. The r values show that the technologists' values
correlate acceptably with the "grand" mean. Since the
correlation coefficient is inversely related to the magnitude of the random error, Technologist F displays the
greatest random error and is thus less consistent or more
variable than the other six technologists.
Proportional error influences the slope but does not
impact upon the correlation coefficient. The variation
between the slopes of the seven regression lines reflects
the magnitude of each technologist's proportional error.
Constant error can be assessed by the departure from
BRIEF SCIENTIFIC REPORT
Vol. 76 • No. 5
715
TECHNOLOGISTS Z SCORES
+2.0
FlG. 2. Seven technologists Z scores for the 14
specimens arranged from
low to high.
-2.0
1
2
3
4
5
6
7
8
9
101112
1314
SAMPLE
(Arranged Lowest to Highest)
an anticipated y-intercept. Since the seven independent
regression lines essentially passed through the origin,
the effect of constant error appears to be inconsequential to the total error analysis.
A total of 14 specimens were tested. The two way
analysis of variance (ANOVA) 3 was performed on each
specimen independently. A data set for each specimen
consisted of reticulocyte counts obtained by each of
seven technologists from the same four slides. The results are listed in Table II. The null hypothesis that
technologist means are equal is rejected (p < 0.01) by
the large F values that occur for ten of the specimens
in 'Technologist Variation.' Specimen #1, with a mean
reticulocyte count of 0.05%, resulted in a very small F
value for technologist variation which is consistent with
the data in Fig. 1. The hypothesis of equality of tech-
nologists means can be rejected for specimens #8, #11,
and #13 at the 0.05 level but not at the 0.01 level.
The F values corresponding to the slide variation are
negligible. The small slide variation further supports
the premise of largely uniform cellular distribution of
the spun slides. Thus, the assumption is made that the
large differences among technologists do not depend
significantly on slide variation.
Confidence limits for the 14 specimens were derived
from the four reticulocyte counts obtained by each of
the seven technologists. In Fig. 3, the mean reticulocyte
percentage is located on the x axis and the 95% confidence limits are indicated by error bars. Since the
ANOVA provides least square estimates of the variance, the standard deviation of the mean was calculated
from the variances estimated from the technologist,
PEEBLES ET AL.
716
slide and residual mean squares (MS) in the following
manner: Technologist variance equals (Tech MS-Residual MS) -r 4; Slide variance equals (Slide MS-Residual MS) -r 7; Residual variance equals Residual MS.
Thus, the total variance of seven technologists counting
four slides is the sum of these three variances. The standard deviation is the square root of the sum of the
calculated variances and the 95% confidence limits are
derived from the mean plus or minus two standard deviations.
Discussion
Several investigators have provided data and conclusions concerning the inhereni: error of the manual reticulocyte count. Only one, or perhaps two, of the many
possible sources of error were considered by a particular
report. A complete error study of the manual reticulocyte counting method is required to determine its
usefullness and limitations as a reference method in
evaluation and calibration (standardization) of automated analyzers in addition to the consequences of in-
Table I. Correlation Coefficients, Slopes, and Yintercepts for the Seven Technologists versus the
"Grand" Mean Value Determined for All Counts
Technologist
r
Slope
Y-intercept
A
B
C
D
E
F
G
0.984
0.977
0.957
0.969
0.969
0.925
0.973
1.076
0.880
1.371
0.860
0.766
1.154
0.910
0.168
0.020
0.294
-0.386
-0.327
0.176
0.060
Table 2. Two Way Analysis of Variance (ANOVA)
F Values
Specimen 1
Specimen 2
Specimen 3
Specimen 4
Specimen 5
Specimen 6
Specimen 7
Specimen 8
Specimen 9
Specimen 10
Specimen 11
Specimen 12
Specimen 13
Specimen 14
Degrees of freedom
Significant value of
Fat 0.01
Significant value of
F at 0.05
Technologist
Variation
Slide
Variation
0.56
5.45
5.09
5.06
8.95
10.95
13.43
3.57
9.37
6.23
3.38
17.78
3.98
5.84
6, 18
1.15
1.67
1.03
0.39
0.69
1.39
0.23
0.73
0.64
0.42
0.19
1.46
0.53
0.81
3, 18
4.01
5.09
2.66
3.16
Mean
Reticulocyte
Count (%)
0.05
0.98
1.16
1.68
1.90
2.44
3.21
4.28
5.34
5.80
8.86
10.15
12.91
13.92
A.J.C.P. • November 1981
accurate manual reticulocyte results in clinical application.
In the present study, the topics of technologist and
slide variation emerged foremost in importance as contributors to manual counting error. May and Sage 9
compared the variability of the wedge smear to that of
the spun slide. The spun slide resulted in a more homogeneous distribution of reticulocytes and erythrocytes that the wedge smear. The small F values for slide
variation, as determined by the two way ANOVA in
the present study, support that a largely uniform distribution of cells existed on the spun slides used for
counting. It may be concluded that the spun slides contributed insignificantly to the large differences among
technologist mean values.
Miale 10 surmises that in addition to variability due
to technical error, manual counting error results from
the random distribution of reticulocytes among mature
erythrocytes.'This is true of any counting process. Furlong5 further explains how reticulocyte counting error
is inversely related to the total number of cells examined. If very few reticulocytes are present (approximately 1.0%) large numbers of cells must be counted
to obtain reasonable precision in order to expect 95%
confidence limits within 0.1% of the mean. In contrast,
specimen #1, with a mean reticulocyte count of 0.05%,
resulted in an insignificant F value for technologist variation indicating that technologist means were similar.
Specimen #2, with a mean reticulocyte count of 0.98%,
resulted in a large F value for technologist variation
( P < 0 . 0 1 ) which indicates that differences in means
exist. From the current study technologist results agreed
more closely when the reticulocyte count approached
zero but significant disagreement occurred at approximately 1% and above.
Gilmer and Koepke6 report that the excessive variance between technologists' reticulocyte counts is a
problem of whether or not a technologist identifies and
counts erythrocytes which contain a "single dot" of reticulum as reticulocytes. According to their study, the
extremely mature reticulocyte is the most controversial
to identify morphologically by College of American
Pathologists (CAP) participants, yet is shown by Seip"
and Lowenstein7 to comprise almost two-thirds of the
circulating reticulocytes. In the present study, technologists defined general staining characteristics of reticulocytes in similar terms. However, criteria for the final
stage of a "countable" reticulocyte differed. Disagreement occurred as to whether one granule, two granules,
fine granular, or fine filamentous reticulum must be
present in the final stage. Such differences in endpoint
discrimination may be reflected in the results of the two
way ANOVA. The F values obtained for technologist
variation were large enough (/ > <0.01) in ten sample
cases to support the conclusion that the differences
717
BRIEF SCIENTIFIC REPORT
Vol. 76 • No. 5
among technologist means are greater than expected
for a Poisson counting process.
Discrimination between reticulum and other granular
erythrocyte inclusions is a potential contributor to varied results due to individual identification criteria. Deiss
and Kurth 4 studied the possibility of falsely increased
reticulocyte counts due to staining of both reticulum
and siderotic granules by new methylene blue. Erroneously high reticulocyte counts were obtained postsplenectomy, in sickle cell disease and acquired hemolytic anemia because cells containing siderotic
granules were counted as reticulocytes.
Perusal of the literature does not present a clear picture of cellular detail in the definition of the reticulocyte
maturation endpoint or the distinction of other granulation from reticulum using new methylene blue; thus
providing justification for the large statistical variation
in reticulocyte counts among technologists. The large
F ratios in this study support the conclusion that technologists consistently apply their own individual criteria
in morpholigic identification and enumeration of reticulocytes whereby critically compromising enumeration
accuracy.
The expected range of reticulocyte counts generated
by seven technologists each performing four replicates
for the 14 specimens is illustrated by Fig. 3. For example, if a particular specimen has a mean reticulocyte
value of 5.3%, the assumption can be made that in 95%
of cases the real value for the specimen resides within
a range of 3.1 to 7.6%. Although the counting precision
could be improved by increasing the number of replicates performed by each technologist, a 4000 cell count
per technologist yields a range which is extremely wide
and unacceptable for a reference method. The actual
differences between the results of individual technologists greatly exceed the expected error for a Poisson
counting process; therefore, it is extremely difficult to
obtain results with sufficient accuracy to provide a reference reticulocyte method for standardization or calibration of automated reticulocyte analyzers.
Summary
A statistical evaluation of the New Methylene Blue
manual reticulocyte count revealed that the inherent
inaccuracy of the method is attributable to a large proportional error among technologists. They possess minimal constant error as indicated by the zero y-intercepts
and negligible random error. There was no evidence of
non-linearity for any technologist. The source of the
large variation between technologists, as shown by the
large F value among technologist mean reticulocyte
results, is the consistent application of individual criteria in morphologic identification and enumeration of
reticulocytes.
9 5 % CONFIDENCE LIMITS
Mean Reticulocyte Value (%)
FIG. 3. Confidence Intervals of 95%. Example of a mean reticulocyte
value of 5.3%, produced by seven technologists each counting four
slides, for which an independent count has 95% confidence of falling
between 3.1% and 7.6%.
When 95% confidence limits are derived with seven
technologists performing four replicates each, the ranges
in which the true value would reside remain unacceptably wide when compared to the expected Poisson
counting accuracy. Because technologist-to-technologist variation is significantly worse than that expected
for a Poisson counting process, it is extremely difficult
to obtain manual results with sufficient accuracy to
serve as a reference reticulocyte method.
References
I. Brecher G: New methylene blue as a reticulocyte stain. Am J
Clin Pathol 19:895, 1949
2. Brown BA: Hematology: principles and procedures, Lea and Febiger, Philadelphia, 1973, pp 77-81
3. DeGroot MH: Probability and statistics. Addision-Wesley Co.,
1975, pp. 545-552
4. Deiss A, Kurth D: Circulating reticulocytes in normal adults as
determined by the new methylene blue method. Am J Clin
Pathol 53:481-484, 1970
5. Furlong MB: Interpreting the reticulocyte count. Post Grad Med
54(4):207-21l, 1973
6. Gilmer PR, Koepke JA: The reticulocyte, an approach to definition. Am J Clin Pathol 66:262-267, 1976
7. Lowenstein LM: The mammalian reticulocyte. Int Rev Cytol
8:135-174, 1959
8. Luning Prack Associates Audits, workload tables, Montvale, New
Jersey, volume two 1979, p 735
9. May JA, Sage BH: Spinnerfilmsfor reticulocyte counts. Am J
Med Technol 42(10): 357-360, 1976
10 Miale JB: Laboratory medicine hematology. Fifth edition. St.
Louis, CV Mosby Co., 1977, pp 561-562
I I Seip M: Reticulocyte studies. Acta Med Scand suppl 282:9-164,
1953
12 Wintrobe MM: Clinical hematology. Seventh edition. Philadelphia, Lea and Febiger, 1974, pp 119-120