"In conclusion, this study demonstrates that with the same maternal serum
markers, variations are observed between software packages, with a mean
detection rate of 54.4-66.4.% and a false-positive rate of 2.4-6.8%.
In practice, in a population of 100 000 patients, including 143 cases of
Down syndrome, the least sensitive software will detect 78 cases of Down
syndrome through 2400 amniocenteses, whereas the most sensitive will detect
95 cases through 6800 amniocenteses.
These differences will have an impact on public health policy (24) and
should be minimized.
This may be achieved in different ways: use of the same maternal age-related
risk, definition for each country of the risk at term or at sampling, use of
daily medians, and use of the parameter sets defined by Cuckle(20)."
This is the second problem of the screening with maternal serum markers (the
first is the absence of confidence limits)
--
Ph Coquel, MD
Annecy, France
The full text
http://perso.wanadoo.fr/doc-gyneco/gynet/echonet/compsoft.html
(Clinical Chemistry. 1999;45:1278-1280.)
© 1999 American Association for Clinical Chemistry, Inc.
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Technical Briefs Software for Prenatal Down Syndrome Risk Calculation: A
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Comparative Study of Six Software PackagesFrançoise Muller, Philippe
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Aegerter, Sandrine Ngo, Agnès Fort, Alain Beauchet, Paul Giraudet, and Marc
Dommergues
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Prenatal diagnosis of Down syndrome is based on fetal karyotyping, but
amniocentesis cannot be performed in all patients because of the risk of
fetal loss and the cost.
It, therefore, is usually applied only to high-risk (and generally older)
patients.
Noninvasive assays of maternal serum markers have allowed the extension of
screening to mothers of all ages. Alpha-Fetoprotein (AFP), human chorionic
gonadotropin (hCG) or free ß-hCG, and unconjugated estriol have been
prospectively evaluated during the second trimester in large populations
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11).
Wald et al. (12) proposed an individual risk calculation for Down syndrome,
combining maternal age, maternal serum markers, and gestational age, in
which amniocentesis was proposed when the risk was above a cutoff leading to
a 60% Down syndrome detection rate and a 5% amniocentesis rate.
Software is necessary for risk calculation and should be routinely
validated.
To evaluate the influence of software design on risk calculation, we
compared six software packages [Prenatal Interpretive Software (Maciel Inc.,
distributed in France by Abbott), Prisca (DPC France), DIANASoft (BioChem
ImmunoSystèmes France), T21 (Chiron), PrenatScreen (CIS bio international),
and MultiCalc (Wallac)] in two populations: 529 control patients (ages 18-37
years) selected randomly from 90 000 patients screened in our laboratory, in
accordance with the national maternal age structure of pregnant patients in
France (Institut National des Statistiques et des Etudes Economiques, 1993),
and all 125 Down syndrome-affected pregnancies (patient ages, 20-37 years).
AFP (SFRI) and total hCG (13) were expressed in multiples of the median
(MoM) for five software packages and in IU/L corresponding to the
manufacturer's MoM for the Abbott package.
Gestational age was determined in 99% of cases from ultrasonographic
crown-rump length.
The cutoff defining the at-risk group was 1 in 250 for all software packages
and was calculated at term and not at sampling.
The efficacy of the screening tests was evaluated by determining the number
of true positives [detection rate for Down syndrome in the Down
syndrome-affected population (sensitivity)], and false-positive rate [number
of patients above the cutoff in the control population (specificity)].
Risk calculation for Down syndrome combines at least two factors in all
cases: the risk of Down syndrome related to maternal age and the risk
indicated by maternal serum markers.
The maternal age-related risk at term used in each of the six software
packages is given in Fig.1.
Although all six software packages yielded a similar risk, substantial
variation was apparent, particularly for patients 37 years (1 in 215 to 1 in
273) and 38 years (1 in 167 to 1 in 214) of age.
The percentages of control and Down syndrome-affected pregnancies included
in the at-risk group are shown in Table 1.
The percentage of cases in which amniocentesis was performed was 2.4-6.8%,
and the Down syndrome detection rate was 54.4-66.4%.
Software packages that yielded the lowest amniocentesis rate also gave the
lowest detection rate, and differences in sensitivity and specificity
between the two least sensitive software packages and the four others were
significant (P <0.01).
For all software packages, the proportion of patients at risk depended on
maternal age.
The main differences were observed for patients < 30, for whom the detection
rate was 20-40% and the amniocentesis rate was 1.2-3.1%.
To define how these variations influence individual risk estimates, we
determined the number of patients below or above the cutoff as a function of
the software.
In the control population, all six software packages classified 491 (93%) of
the 529 patients in the not-at-risk group and 5 (0.9%) in the at-risk group.
In the fetal Down syndrome population, 63 of the 125 cases (50.4%) were
detected by all six software packages, and 38 cases (30.4%) were detected by
none (data not shown).
Discordances were therefore observed in 6% of cases in the control
population and in a striking 20% of Down syndrome cases.
The cause of these differences can be analyzed by comparing the likelihood
ratio, i.e., the ratio of prior risk (age-specific) to final risk.
The percentage of patients with a likelihood ratio greater than an arbitrary
value of 1.5 varied from 10% to 20% in the control population and from 62%
to 73% in the Down syndrome population (data not shown).
These large variations demonstrated that factors other than maternal age are
responsible for these differences.
Down syndrome risk factor is derived from two statistical functions (14):
the age-related risk and a risk derived from the biochemical indices.
In all mathematical models tested here, the risk of Down syndrome related to
maternal age is taken into account, according to published values
(15)(16)(17)(18).
However, we observed that for a patient 37 years of age, the risk ranged
from 1 in 215 to 1 in 273, thereby changing the decision concerning
amniocentesis if the cutoff is 1 in 250.
These differences could be minimized by considering maternal age in 1-month
and not 1-year intervals (19).
The likelihood ratio method (14) is used in all six software packages
tested.
However, the detection rate and false-positive rate were markedly different.
The relative weight given to hCG and AFP and to maternal age can explain
these variations.
We tested the weight of each biochemical marker by varying hCG from 1 MoM to
2.5 MoM and AFP from 1 MoM to 0.5 MoM when maternal age, gestational age,
weight, and ethnic background were fixed.
When hCG was within the reference interval (1 MoM), four software packages
(MultiCalc, PrenatScreen, Prenatal Interpretive Software, and T21) gave the
same weight to AFP and two software packages (DIANASoft and Prisca) gave a
lower weight to AFP.
However, a low value for AFP (0.5 MoM) did not assign the patient to the
risk group, the risk varying from 1 in 559 to 1 in 800. When hCG was at 2.5
MoM, and AFP was within the reference interval (1 MoM), this high value of
hCG was not sufficient for all six software packages to place the patient
above the cutoff, the risk varying from 1 in 260 to 1 in 375.
To place the patient at a risk of 1 in 250, the necessary AFP value was 0.7
MoM for PrenatScreen, 0.78 MoM for MultiCalc, and 0.95 MoM for the four
others.
Therefore, the relative weight given to markers depends on the software and
markedly affects the risk estimation.
The weight of maternal age in risk calculation was analyzed for a patient
with AFP at 0.80 MoM and hCG at 2.2 MoM and a maternal age of 20-38 years.
For all software packages, the risk progressively increased with maternal
age, but large variations were observed.
For example, at 20 years, the risk varied from 1 in 710 (PrenatScreen) to 1
in 378 (DIANASoft and Prisca).
Depending on the software, a patient with these marker values would be in
the at-risk population at 30 or 34 years.
Another important factor is the choice of marker distribution parameters
used in the statistical model.
Parameter sets used in software are calculated from relatively few Down
syndrome pregnancies and usually are based on women from a single center.
Cuckle (20) published variances and covariances obtained by metaanalysis of
all studies published to 1995.
If all software packages were to use these parameters, differences will be
diminished.
Biochemical markers are gestational age-dependent, and the results are
expressed in MoM.
This supposes that median values are well defined in large populations and
that gestational age is given with a high degree of precision (21).
In addition to these main factors, other parameters can be taken into
account by the software: choice and number of markers, whether values are
corrected for maternal weight, smoking, ethnic background, or diabetes.
Risk may be calculated at term or during the second trimester.
The fetal death rate in Down syndrome between 15 weeks and term has been
estimated as 18-23% (16)(22), but this rate is not always taken into account
(Chiron T21).
Risk estimation also depends on the extent to which ultrasound is used to
estimate gestational age.
Laboratory-related differences must be added to the discrepancies
attributable to software.
This can lead to great variation in individual risk: Cavalli (23) observed a
risk of 1 in 502 and 1 in 80 for the same patient with a Down
syndrome-affected pregnancy.
The notion of equality between patients, therefore, is clearly flawed.
In conclusion, this study demonstrates that with the same maternal serum
markers, variations are observed between software packages, with a mean
detection rate of 54.4-66.4.% and a false-positive rate of 2.4-6.8%.
In practice, in a population of 100 000 patients, including 143 cases of
Down syndrome, the least sensitive software will detect 78 cases of Down
syndrome through 2400 amniocenteses, whereas the most sensitive will detect
95 cases through 6800 amniocenteses.
These differences will have an impact on public health policy (24) and
should be minimized.
This may be achieved in different ways: use of the same maternal age-related
risk, definition for each country of the risk at term or at sampling, use of
daily medians, and use of the parameter sets defined by Cuckle(20).
However, if we obtain the same value, this does not necessarily mean that it
is the most accurate one.
Acknowledgments
We thank the Association pour la Recherche en Médecine Foetale for financial
support and all of the manufacturers who contributed software packages. We
also thank Laurence Bussière, who was involved in the early stages of this
work.
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