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Standard, Population & Customised fetal size charts 11 – Intergrowth 21

September 16, 2019

Standard charts

In 2008 the Intergrowth-21 group, funded by Bill Gates, produced a series of growth standard charts for fetuses.  Click here for their website, here for the main report and here for their estimated fetal weight standards, which were published separately. According to some historians Intergrowth-21 was originally a spin off from the World Health Organisation (WHO) fetal growth standard group. However the main reports from Intergrowth-21 preceded publication of those from WHO.

Intergrowth-21 collected scan measures from 4,600 healthy fetuses and healthy mothers in eight geographically distinct urban areas, Pelotas Brazil, Turin Italy, Muscat Oman, Oxford UK,
Seattle USA, Shunyi County Beijing China; Central Nagpur India, and Parklands Nairobi Kenya, where environmental, nutritional and social constraints on fetal growth were judged to be minimal. It was called the Fetal Growth Longitudinal Study (FGLS). They chose cities located below 1600 metres, with low levels of pollution. The women had no clinically relevant medical problems, started antenatal care before 14 weeks, had a height ≥153 cm, a body-mass index (BMI) between 18 and 30 kg/m², a haemoglobin concentration ≥110 g/L, and were not on any special diet. This resulted in a group of educated, affluent, clinically-healthy women, with adequate nutritional status, who by definition were at low risk of fetal growth restriction and preterm birth. The Intergrowth-21 group used all the latest scan methods as well as modern techniques to avoid bias (click here).

The authors found little variation by ethnicity. Specifically there were no statistically significant differences between each geographical area and the pooled data from the other seven. The charts also aligned closely with newborn charts from similar healthy populations.

This lack of important size difference between healthy fetuses from different ethnic groups implies that the differences we see every day between such groups are largely a result of environmental and nutritional factors. Once these are removed the differences disappear.  It is strong evidence against customisation by ethnicity.

But it is also the reason why some enthusiasts for customisation push back so strongly against it. They dispute the statistical methods, or point to other standard charts, notably those from WHO which we will discuss tomorrow, showing small differences between geographic groups, as evidence for customisation. But the converse argument does not apply. Finding ethnic differences even in standard charts, is not strong evidence for customisation. There are small differences, e.g between Seattle and Shunyi County, in Intergrowth-21, and between countries in WHO, but the most likely explanation is that neither group of researchers succeeded completely in removing all study participants who had environmental constraints acting on their pregnancy.

The Intergrowth-21 authors concluded that their charts are the single best growth standard chart for use worldwide, and I agree. We use them in Nottingham.

Next (click here) the WHO standard charts.

Jim Thornton

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Standard, Population & Customised fetal size charts 10 – estimated fetal weight

September 15, 2019

Another technical digression

There are no customised charts for direct fetal scan measures. They only exist for fetal weight and fundal height. This suits parents and non-experts who, unfamiliar with fetal biometry, may prefer weight to say, the abdominal circumference centile.  But it’s not straightforward for the obstetrician. Fetal weight is tricky to estimate, tricky to chart and tricky to interpret.

Once the baby is born we can weigh it. But until then we have to use formulae based on a combination of head, abdomen and femur measurements. The most popular were developed by Frank Hadlock in the 1980s, in Texas. He studied 276 fetuses each scanned within a week of birth (click here), which is rather few, especially as nearly half were less than 24 weeks. Nor would the details of his scan methods pass muster today (click here), although that is hardly his fault. Scan technology, and ways to reduce bias, have developed considerably since.

Turning weights, whether real or estimated, into a chart is also more complicated than just the choice of “population” or “standard” we discussed a few posts ago (click here). Charts based on babies actually born preterm, systematically underestimate centiles because babies born pre-term tend to be growth restricted. Normal weight babies at say 30 weeks, mostly don’t deliver at 30 weeks, so they can only be measured not weighed.

The figure below (taken from Stirnemann click here) shows the problem. The dotted lines are 3rd, 50th and 97th weight centiles based on babies actually born. The solid lines the same for all babies, including those who don’t deliver preterm. Not much difference at term (black square), but at 28 weeks a baby on the 50th centile of babies born at that gestation (black circle), lies below the 3rd for the whole population.

So how did Stirnemann and his Intergrowth-21 colleagues develop their chart? Their trick was this.

First they scanned a lot of fetuses, including those from unhealthy pregnancies, but using the up-to-date methods for avoiding bias. Some came from the Fetal Growth Longitudinal Study (FGLS) which we will describe later. The rest from an unselected cohort of women including smokers, and those with problem pregnancies, the INTERBIO‐21st Fetal Study. They then measured the birth weights of those babies who happened to be born within a short interval of the scan. There were 2,404 of these. Again they took special care with the birth weight measurements, drying the baby carefully, cutting the cord to a standard length, using a standard cord clamp and a trained person to work specially calibrated random-zero electronic scales to avoid digit preference.  The babies born preterm included many that were not healthy, but that didn’t matter at this stage. They simply used the measurements, with a small correction for the interval between the measurement and the actual birth, to create new formulae for estimating weight from head, abdomen and femur measures. i.e. the same as Hadlock, but with a larger sample, and modern scanning, weighing and bias reducing techniques.

The second stage of the procedure was to apply those formulae to the scan measures from healthy babies in the Fetal Growth Longitudinal Study, i.e. the main Intergrowth-21 population. Those are the weights and centiles that comprise the Intergrowth-21 fetal weight standard charts. Here’s the final result. Left the chart in Stirnemann’s paper and right the nicely printed version for regular use.

   

Frank Hadlock knew, and the creators of customised charts like GROW also know, that fetuses born preterm are systematically lighter than those destined to be born at term. Both made similar adjustments (click here for Hadlock’s, and here for GROW’s methods). I’m not suggesting that either confuse the weights of babies actually born preterm with those that go on the deliver at term.  However the GROW customised charts from the Perinatal Institute are based on weights estimated using Hadlock’s formulae, which themselves were created from a relatively small number of women in Texas in the 1980s, using the methods and scan machines available then.

Another issue with using ultrasound-based weight estimates to manage pregnancy is that they may not be the best predictors of babies who are likely to die, be brain damaged or unable to withstand the stress of labour. Expert interpretation of the individual measures from which the weight was estimated is often better. For example, if a fetus with a small abdominal circumference has relatively long legs or a big head, it will be heavier, but more malnourished.

Finally, since customised direct fetal measure charts don’t exist, obstetricians who use customised weight charts have to either ignore the individual components on which the weight was estimated, or use a non-customised chart to interpret those. At the very least a source of avoidable confusion.

Antenatal assessment of fetal compromise is complicated. That’s why we have fetal medicine specialists. Combining multiple size measures into a single estimate of weight may please parents, but is a potentially misleading simplification.

This is the last technical digression. Now we are ready to look in detail at the main modern charts, Intergrowth-21, WHO and the main customised chart, GROW. Tomorrow Intergrowth-21 (click here)

Jim Thornton

Standard, Population & Customised fetal size charts 9 – gender and parity

September 14, 2019

Customisation by fetal gender

After birth, male and female babies differ in size, and gender specific charts are widely used. This is reasonable since gender specific differences in mortality or morbidity are unrelated to fetal size.  On the contrary, male babies tend to be larger but have higher rates of mortality and morbidity, so gender specific charts are likely to better identify pathology.

There are also fetal gender differences in size (click here), so in theory gender specific charts might also be appropriate in utero.  However, in practice the gender differences are rather small, and because of practical difficulties – the ultrasonographer might be uncertain, or the parents might not want to know – gender specific fetal charts tend to be limited to specialist uses (click here).

Customisation by maternal parity

Birth weight and size increase with parity, at least over the first few pregnancies, but this is not independent of pathology. First babies are smaller than subsequent ones but have a higher mortality, partly because pre-eclampsia is common in first pregnancies, and partly because first labours tend to be longer. To a lesser degree, at least in the developed world, some of the increase in birth weight with increasing parity is related to increasing maternal weight. Again this is not benign. For these reasons customisation on maternal parity is unwise.

Tomorrow another technical digression. This time on estimated fetal weight (click here).

Jim Thornton

 

Standard, Population & Customised fetal size charts 8 – height

September 13, 2019

Customisation on maternal height

If there is any justification for customising fetal size charts, this is likely to be it. Height is objective, easy to measure, does not vary by gestational age, and is not influenced much by current nutrition, smoking or drug abuse. And taller mothers do tend to have larger babies.

If maternal height was largely due to benign genetic variation, with healthy tall mothers having larger healthy babies, and healthy shorter mothers having healthy smaller ones – remember the Shire horses and Shetland ponies- it would improve detection of pathology. But it is not.

Height is also closely related to parental and early life nutrition, and to health in adult life. Over the last 100 hundred years average heights have increased much faster than they plausibly could have by natural selection (review here). The cause is almost all better nutrition, reduced infections and other environmental improvements. Over this period perinatal mortality has fallen steeply. Even within modern populations short maternal height is clearly associated with stillbirth (table 1 here).

We don’t need to assume that short height causes stillbirths to make customisation inappropriate. If height is associated with some third factor which causes the adverse outcomes it would still be an inappropriate measure on which to customise.

Next gender and parity (click here)

Jim Thornton

 

Standard, Population & Customised fetal size charts 7 – maternal weight

September 12, 2019

Underweight women have smaller babies and overweight ones larger. Compared with ethnicity (click here and here) customising by weight should be straightforward. But it’s still unlikely to improve detection of adverse outcomes.

Outside a relatively narrow band, BMI 18-25, weight variation is not healthy. Undernourished women, smokers and users of other substances, have smaller babies, but also more dead and brain damaged ones. Those who are underweight because their mothers had been undernourished during their own pregnancy, probably also have higher rates of adverse outcomes.

Similarly the overweight population includes women who are over eating, and women with diabetes or pre-diabetes, whose babies are large in an unhealthy way. They also have more stillbirths, birth injuries and other adverse outcomes.

This is why, at the extremes, customising on maternal weight is harmful. It leads us to tell an overweight diabetic mother with a BMI of 35 that her macrosomic baby was “normal for her weight”, or an underweight woman with a malabsorbtion syndrome, that her small baby was “normal for her”!

Customising on weight within the relatively narrow band of “healthy” weight variation might make sense, but in practice variation in fetal size within this normal band is small.  Enthusiasts for customisation such as the Perinatal Institute (click here) recognise this and their GROW customisation software limits the adjustment on the basis of weight to the central BMI zone. However, presumably because they wish to “explain” more birth weight variation, they customise up to BMIs of 30, the borderline between “overweight” and “moderately obese”. This is not a healthy weight.

Next customisation by height (click here)

Jim Thornton

Standard, Population & Customised fetal size charts 6 – practical problems with race or ethnicity

September 11, 2019

Last Saturday (click here) I suggested that the relation between race or ethnicity and poor pregnancy outcomes made customisation of fetal size charts by either, a poor way to improve detection of adverse outcomes. This post considers more practical issues.

Classification of human beings by race, an inherent physical or biological quality attributable to some groups rather than others, arose as a scientific idea in the 17th century, reached its full flowering in the 19th, degenerated into a justification for mass murder in the mid twentieth century and is now completely discredited. Although the word “race” has a lay meaning, it no longer has a scientific one. Even if we wanted to, we couldn’t customise charts by race.

Classification by ethnic group, shared ancestry, language, homeland and other cultural features, is possible but also problematic. Definitions of what constitutes an ethnic group vary over time and by who is doing the classification, and the history of claiming ethnic differences in other areas such as IQ, criminality, sporting prowess etc. have all been based on poor science, often driven by racist ideas.

Ethnicity is also problematic because so many human beings are of mixed ethnicity. This paper (click here) nicely shows the problems. This not only makes it difficult for the clinician faced with a mixed ethnicity parent, to know which chart to use. It also causes problems for scientists drawing up ethnically-based customised charts.  How can they ensure that the people on whom their charts were derived, were of “pure” ethnicity?

In practice most ethnically customised charts are based on “self-reported ethnicity”; typically a “tick box” completed by a clerk when the patient registers. No-one knows whether the clerk based their decision on self-report, skin colour, facial features or something else.

To summarise ethnicity is poorly defined, and many of our patients are of mixed ethnicity, so even if ethnicity wasn’t also related to poor outcomes, which it is, it would be an unsuitable factor on which to either create customised charts or to decide which charts to use.

Next weight (click here)

Jim Thornton

Standard, Population & Customised fetal size charts 5 – race ethnicity

September 6, 2019

At first sight customisation by maternal (or paternal) race or ethnic group appears sensible. Han Chinese women, for example, tend to be smaller than say Swedish women, and have smaller babies. Surely we should plot their baby’s growth on different charts.

But how can we be sure that the ethnic “differences” we observe are not on, or correlated with, the pathway to the pathology we are trying to identify?

Consider the two ethnic groups above, who appear to differ in size, and have had relatively little historical intermingling, Northern Europeans, and the Han Chinese.  Northern European women and their babies are taller, and larger than Han Chinese.  But perinatal mortality is also higher in the Han Chinese.

Both differences are probably at least partly caused by under nutrition among the Han Chinese ancestors who would have lived through two of the worst famines in recent human history, Mao Tse Tung’s Great Leap Forward, and Cultural Revolution. Both will certainly be affected by present day differences in nutrition, and smoking and drinking habits.  If so, the observation that more than n% of Chinese babies fall below the nth centile of Western European charts, is a sign that such babies are genuinely failing to reach their full growth potential, rather than that the charts are wrong.

It doesn’t matter if race or ethnicity causes the adverse pregnancy outcomes, or is just correlated, the fact that there is a correlation should make us think twice about customising on either. Although even a strong correlation in itself doesn’t prove it wrong. If race was less strongly correlated with adverse outcomes than size, customisation on race, at least in theory, might still work.

Tomorrow (click here) we’ll see some practical reasons why successful customisatiom by race or ethnicity is in reality a hopeless quest.

Jim Thornton

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