Introduction
 Top of page
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
The most effective marker of trisomy 21 is fetal nuchal translucency (NT), measured ultrasonographically at 11–14 weeks of gestation using standardized conditions (www.fetalmedicine.com/nuchal.htm)1. In the first description of fetal NT in 1992 we used a fixed cutoff value of 3.0 mm but we subsequently established that in normal pregnancies fetal NT increases with gestation2, 3. Consequently, in screening for trisomy 21 the use of a fixed cutoff approach is wrong because it provides inaccurate patientspecific risk estimates. To take account of this gestational variation in NT we expressed the measured fetal NT as the difference from the normal median NT at the measured crown–rump length (CRL), which is the deltaNT3. The median NT in unaffected pregnancies was derived by regressing log_{10}(NT) on gestational age, and likelihood ratios for trisomy 21 were calculated from the relative frequency of trisomy 21 and unaffected pregnancies at any one given deltaNT1, 3. In the deltaNT approach patientspecific risks for trisomy 21 are calculated by multiplying the a priori maternal age risk with the likelihood ratio of the observed deltaNT1, 3, 4.
In screening using maternal serum biochemical markers, a different approach has been used to take account of gestational variation in marker levels. This method involves converting the measured concentration into a multiple of the median (MoM) of unaffected pregnancies of the same gestational age5, 6. Essentially, the Gaussian distributions of log_{10}MoMs in trisomy 21 and unaffected pregnancies are derived and the ratio of the heights of the distributions at a particular MoM, which is the likelihood ratio for trisomy 21, is used to modify the a priori maternal agerelated risk to produce a patientspecific risk. When more than one marker is used account is taken of the correlation between markers in affected and unaffected pregnancies6. When firsttrimester screening using fetal NT and maternal serum biochemistry became feasible it was suggested that fetal NT could be considered like any other biochemical variable, converted to a MoM and used in a multivariate Gaussian model7.
For the Gaussian MoM approach to be valid and to provide accurate individual patientspecific risks for trisomy 21 across the 11–14week window it is necessary to be able to demonstrate that:
 (a)
either NT MoM or some transformation of NT MoM has a Gaussian distribution;
 (b)
the standard deviation (SD) of the MoM in the transformed domain is constant; and
 (c)
the median MoM in trisomy 21 pregnancies is a constant proportion of the median for unaffected pregnancies.
The aim of this study was to assess whether in screening for trisomy 21 by fetal NT thickness the delta or the MoM approach is the most appropriate method for calculating accurate individual patientspecific risks.
Results
 Top of page
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
In the unaffected pregnancies, the distribution of NT MoM and log_{10}(NT MoM) was not Gaussian, whilst in the trisomy 21 pregnancies, probably because of small numbers, the distribution did not significantly depart from Gaussian form (Table 1). The median NT MoM was 1.00 for the unaffected pregnancies and 2.18 for the trisomy 21 pregnancies. In the unaffected pregnancies, there was a clear deviation from linearity in the probability plot at MoM values of 2.25 or more and at 0.45 MoM or less (Figure 1). In the trisomy 21 pregnancies, there was an adequate linear fit with some departure at the tail ends (Figure 2).
Table 1. Tests for Gaussianarity of log_{10}(NT MoM) in unaffected and trisomy 21 pregnanciesStatistic  Unaffected cases (n = 128 030)  Trisomy 21 cases (n = 428) 


Mean log_{10}(NT MoM)  − 0.0030  0.3281 
SD log_{10}(NT MoM)  0.1246  0.2292 
Kolmogorov–Smirnov coefficient (probability)  0.033 (< 0.0005)  0.039 (0.130) 
There was a gestational agedependent departure from a Gaussian distribution in log_{10}(NT MoM) and this is illustrated by examining the ratio of the difference between the 97.5th and 2.5th percentiles of log_{10}(NT MoM) and the difference between the 75th and 25th percentiles. If the distribution was Gaussian the ratio would have been constant (2.9) over the complete gestational age range, but as shown in Figure 3 there are systematic departures from this figure. Other ratios are also shown for completeness.
The withingestational day SD of log_{10}(NT MoM) in the unaffected pregnancies changed significantly with gestation (SD log_{10}(NT MoM) = 1.198 − 0.0234 × GA + 0.000126 × GA2, where GA is the gestational age in days; P < 0.0005; Table 2, Figure 4). In the trisomy 21 pregnancies, there was no significant change in SD with gestation (P = 0.248), probably because of the small number of cases examined (Table 2).
Table 2. Unaffected and trisomy 21 pregnancies at each gestational day and standard deviation of log_{10}(NT MoM) by gestational day (unaffected) or by grouped day (trisomy 21)Gestation (days)  Unaffected cases*  Trisomy 21 cases 

n  SD  n  SD 


77  1729  0.1478   
78  3845  0.1390  9  
79  3029  0.1346  6  0.2500 
80  5466  0.1353  21  
81  6460  0.1351  28  0.2137 
82  7310  0.1314  27  
83  8365  0.1277  25  0.2239 
84  9246  0.1240  34  
85  9675  0.1210  29  0.2377 
86  9849  0.1217  47  
87  9312  0.1155  38  0.2519 
88  8928  0.1147  33  
89  8108  0.1178  22  0.2133 
90  6963  0.1173  28  
91  6244  0.1149  35  0.2238 
92  6147  0.1142  16  
93  3827  0.1149  12  0.2172 
94  3050  0.1122  10  
95  2390  0.1162  2  
96  2024  0.1164  1  
97  1287  0.1173  5  0.1859 
98  21  0.1389   
In the trisomy 21 pregnancies, log_{10}(NT MoM) decreased significantly with gestation (log_{10}(NT MoM) = 0.929 − 0.00696 * GA, P = 0.009; Figure 5). The median NT MoM was 2.53 at 11 weeks, 2.12 at 12 weeks (P = 0.043) and 1.94 at 13 weeks (P = 0.008). In contrast, the deltaNT did not change significantly with gestation; P = 0.224; Figure 6). The median deltaNT in trisomy 21 was 1.89 mm.
The distribution of the log_{10}(NT MoM) for the unaffected fetuses is shown in Figure 7; this is the distribution of values that produced the normal plot shown in Figure 1. As can be seen from the superimposed normal density, there are serious departures from a Gaussian distribution throughout the log_{10}(NT MoM) range. A onedimensional nonparametric density estimate using the same data is shown in Figure 8 with the same Gaussian curve superimposed for comparison. The nonparametric density estimate is acceptably smooth and captures the distributional pattern of the data much more closely.
Using the nonparametric approach the contours of constant likelihood ratio were approximately parallel to the baseline (Figure 9), suggesting that a patient's risk is determined by the magnitude of the displacement of the NT measurement from the baseline. This is the approach used in The Fetal Medicine Foundation software for the calculation of risk1, 3. Figure 10 shows the corresponding contours of constant likelihood ratio using the NT MoM approach and making the necessary Gaussian assumptions. These diverge from the baseline and therefore the NT MoM approach is not appropriate for risk estimates associated with NT measurements.
There is one further modification, in fact a simplification to the nonparametric approach, which can be made. This follows from the conclusion that contours of constant risk (likelihood ratio) are parallel to the baseline. This being the case, it follows that the deltaNT values, which are the residual NT values about the baseline, can be accumulated to determine the patientspecific risk. This is the conclusion that supports the deltaNT approach. The nonparametric likelihood ratio profile for deltaNT values is shown in Figure 11. The profile plotted is reasonably smooth for likelihood ratio values up to 50. From a practical point of view the range over which the nonparametric approach is reliable is adequate for clinical purposes.
The detection rate of trisomy 21, for a fixed 5% falsepositive rate, using the NT MoM approach was 74% and with the deltaNT approach it was 76%. The difference between the two was not statistically significant. However, there were major differences between the two methods in the calculation of individual patientspecific risks for trisomy 21. Table 3 shows the estimated risks with the two methods in a 30yearold woman (a priori age risk of 1 in 626) and an observed fetal deltaNT of 1.0 mm (solid line in Figure 10). The NT MoM approach provides inaccurate risk assessments because at 11 weeks it overestimates the risk, whereas at 12 and 13 weeks it underestimates the risk.
Table 3. At a fixed deltaNT, risks of trisomy 21 at each gestational day calculated using the NT MoM approach and the deltaNT approach in a 30yearold woman (a priori agerelated risk of 1 in 626)Gestation (days)  NT (mm)  DeltaNT (mm)  NT MoM  Risk deltaNT  Risk NT MoM 


77  2.2  1.0  1.79  1 in 156  1 in 136 
84  2.6  1.0  1.73  1 in 156  1 in 191 
91  2.8  1.0  1.63  1 in 156  1 in 278 
Discussion
 Top of page
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
The findings of this study demonstrate that in screening for trisomy 21 by fetal NT, calculation of risks by the NT MoM and deltaNT methods provide similar overall detection rates. This was also the case in the only previous comparison of NT MoM and deltaNT in 3180 unaffected and 32 trisomy 21 pregnancies14. However, we found that the NT MoM approach provides inaccurate individual patientspecific risks for trisomy 21 and therefore the use of this method is inappropriate. None of the three basic assumptions that underpin the NT MoM approach was valid. Thus in the unaffected population, the distributions of NT MoM and log_{10}(NT MoM) were not Gaussian, the SDs did not remain constant with gestation, and the median MoM in the trisomy 21 pregnancies was not a constant proportion of the median for unaffected pregnancies. Although this is the largest dataset of trisomy 21 cases studied, the small number of cases at each gestational day was insufficient for us to confirm nonGaussian fit or variation of SD with gestational day. However it is unlikely that these factors in trisomy 21 cases should behave differently from the large unaffected population studied.
The data in previous publications using the NT MoM approach also demonstrate a deviation from a Gaussian distribution. Thus, in the study of Wald and Hackshaw there was a clear deviation from a Gaussian distribution at 1.2 and 0.7 MoM in unaffected pregnancies7. Crossley et al. also showed a probability plot with clear deviation from Gaussianarity at 1.5 and 0.5 MoM in unaffected pregnancies15. The variation in SD of log_{10}(NT MoM) with gestational age and the declining median NT MoM in trisomy 21 pregnancies clearly all add further evidence to the inappropriateness of the MoM approach, which ultimately leads to women being given inaccurate patientspecific risks. Such temporal variation of marker MoMs has also been observed with first and secondtrimester biochemical markers, but in general the distributions in the log_{10} domain are Gaussian16. Nevertheless, it was suggested that to produce accurate patientspecific risks the algorithms should no longer use the constant median separation model but they should be modified to a variable separation model in which each week of gestation has its own specific model parameters producing more accurate individual patientspecific risks17. In the cases of NT even this approach would be inaccurate because the log_{10}(NT MoM) distribution is not Gaussian.
Nonparametric density estimates are ideal for determining the ‘true’ patientspecific risk, because they are dataled and as such are not modeldependent. Examination of NT measurements using this approach has clearly demonstrated that the contours of constant likelihood ratio are parallel to the baseline for unaffected pregnancies. This provides conclusive evidence that accurate patientspecific risks can be determined by the magnitude of the displacement of their NT measurements from the baseline, which is the deltaNT approach.
What happens in population terms is of little relevance to an individual mother who wishes to be given the best estimate of risk for her—for too long screening for chromosomal anomalies has focused on population detection rates rather than accurate risks for individuals. It is clear from our analysis that when using NT in the first trimester accurate patientspecific risks cannot be provided by the NT MoM approach and that the deltaNT approach is the best method available for ensuring accurate risks when using NT alone or in conjunction with maternal serum biochemistry8, 9.