## Coarctation Prediction in Neonates With PDA

Introducing a JavaScript interpretation of the recently published "coarctation prediction model" from Vanderbilt University Medical Center.

I particularly enjoy little challenges such as this one, issued by the authors of a recent JASE article:

... can be easily integrated into daily work flow by entering the CPM equation into a digital picture archiving and communication system.

(Actually, I find it much easier to put things like this on the web, rather than in our PACS—time for which is often dubbed “non-productive” by administrators. Sigh.)

The article is:

A Clinical Prediction Model to Estimate the Risk for Coarctation of the Aorta in the Presence of a Patent Ductus Arteriosus
Soslow JH, Kavanaugh-McHugh A, Wang L, Saurers DL, Kaushik N, Killen SA, Parra DA.
J Am Soc Echocardiogr. 2013 Sep 23. pii: S0894-7317(13)00658-5

The authors developed a model (a “logistic regression model”) to estimate the probability of developing a coarctation of the aorta, in neonates with a patent ductus arteriosus. If you were trained as I was, the admonishing rule was always that “you can’t rule out a coarctation in the presence of  a large PDA”. And now, it looks like maybe we can.

Using 5 simple linear measurements obtained (easily?) from the suprasternal notch view, several ratios are calculated and plugged into their formula. The resulting calculation is the likelihood (probability) of developing a coarctation. According to the authors, patients below the cutoff of 15%, “no longer require observation” and for patients above the 60% cutoff, we should “continue inpatient observation”.

I find it interesting that a model such as this performs as well as it does without using a z-score. Maybe that’s because z-scores are really only good for telling us if a particular measurement is normal— and not so good at telling how abnormal the measure is. Or, maybe it is because the existing z-scores are not particularly well suited for neonates. Or, maybe still, it’s because the arch anatomy changes dramatically when the ductus closes such that previously normal measurements become abnormal.

Perhaps it is a little of all of the above.

Anyhow, the inputs and calculations are fairly straightforward and easily adapted to a web calculator:

#### http://parameterz.github.io/vanderbilt-cpm

I thought this was worthy of it’s own project and so, it is now on GitHub (which is another little experiment). The live page is served directly from version control.
Bam.

I like it like that.

## Lean body mass as a scaling variable in pediatric cardiology

lean body mass prediction equations based on height, weight, age, and gender are now available, providing insights into the practice of scaling of cardiovascular parameters to body size.
Recent critical reviews of reference values for pediatric cardiology are frankly sobering: we don’t have many studies with sufficient numbers of patients to draw reasonable conclusions, and the statistical methods are, at times, specious. I considered writing my own critical review of these critical reviews, but that just felt, well, a bit too meta. Please read this critical review of the many methodological limitations,  and this critical review of the statistical methods used in developing the current reference values. These are harsh but honest assessments of the current state of the art. Considering the criticism leveled against the community, it’s enough to make one wonder if anyone out there is even trying to generate worthwhile reference data.
Clearly, some folks are.
Canadians, mostly, by the looks of things.

## background

Since the very first time a pediatric cardiologist realized that babies weren’t just tiny adults, and that their hearts are small but proportional to their bodies, we have been in a struggle to figure out how best to adjust for body size. The usual suspects have all been tried, with varying success: age, height, weight, body surface area, various allometric exponents of the same... Some groups advocate that weight alone is the best scaling variable; others argue convincingly that height is better; other groups go back-and-forth between the two (see the works from Mass General: 2009 and 1992 ).
Combining both height and weight, BSA has proven to be quite good as a scaling variable, with a defensible theoretic and empiric case for it’s use (see Sluysman and Colan, JAP 2005). But why should cardiac size scale with height, or weight, or BSA? What is the underlying physical principle that says heart size should be proportional to height? Could it be because tall people breathe thinner atmosphere? Because heavy people have to deal with more friction?
Lean body mass (or “Fat Free Mass”-- our brain, bones, and primarily, muscles) are what demand oxygen. If we were insects, we would just get our oxygen by moving the air right through our little exoskeletons. But most of us are not insects, and we get our oxygen instead via red blood cells, transported through our vast network of tubes, pumped from the heart. The heart has developed to pump oxygenated blood to the tissues that need it.
Thank you very much, Captain Obvious” you say.
But there it is: the heart scales with lean body mass.
Yet, we don’t measure lean body mass. Nobody in the echo lab measures LBM. In fact, it’s a rare person in the echo lab that even knows how to measure it.
Traditionally, measurement of LBM required specialized equipment and training. Personally, I have never seen a pair of skinfold calipers in the cardiology department, and a dunk tank is almost entirely out of the question. I keep thinking a bioelectric impedance tool would somehow magically grow into this role, but I have yet to see that happen. Technically, I think you could measure it in the MRI scanner, but DEXA is probably the gold standard, and I am pretty sure we don’t have one of those machines on campus.
Height and weight (and BSA) may be poor surrogates for LBM, but at least they are easy to measure.

## recent work

In the May-June issue of Annals of Human Biology, Foster et al. tackle the idea of developing a series of equations that could predict lean body mass from easily measured anthropometric measures. They measured lean body mass on over 800 children (using DEXA) and then performed some statistical kung-fu to develop their equations... and then validated those equations on another 300 or so patients. Using their prediction equations will get you within 5% of measured lean body mass, a feat that is about on par with some other available measurements (?bioelectric impedance??)
LBM estimated using this simple and inexpensive method may be useful in a variety of settings including ... characterization of the appropriateness of physiologic parameters that scale well to LBM such as resting energy expenditure, heart size, kidney function and drug dosing.
In an article in the current issue of JASE, the same group now apply these new lean body mass prediction equations to the age-old question of how best to determine left ventricular hypertrophy, a matter which they had previously covered and if I might add, raised the bar once already. They go on to demonstrate that— compared to values adjusted for lean body mass— height-adjusted LVM frequently over-estimates the incidence of LVH, and conversely, that BSA-adjusted values frequently under-estimate LVH (though less so than with height) They conclude the present investigation:
LBM is likely the best scaling variable and may be estimated reasonably accurately in children aged 5 to 21 years.
Only by scaling LV mass to LBM will we be able to determine the impact of obesity on heart size.

## future directions

I think the ability to estimate LBM is going to be important to the field of reference values for echocardiography. It is, in effect, the holy grail of scaling variables for pediatric cardiology, and cardiology in general.
I believe that this is going to be so important to future research, I want to help you calculate lean body mass on your own data:

### http://dev.parameterz.com/lbm/

In an effort to be completely transparent about the calculations, I have also provided a step-by-step worked example lean body mass calculation:

### http://dev.parameterz.com/lbm/walkthrough/

And finally, a way to batch-process your data, adding lean body mass by the droves (upload your own csv data)

## Pretty Useless Surface Charts

a quick update about newly available aortic root reference values.

I read the fairly recent AJC article Normal limits in relation to age, body size and gender of two-dimensional echocardiographic aortic root dimensions in persons ≥15 years of age and struggled to make use of their “surface charts”- maybe you did too. Here is an example “surface” representing “aortic diameters” (I presume they mean the sinus of Valsalva) for females:

At first, I thought it was intended to serve as a nomogram, i.e., that this device is what we are supposed to use to check our measured values against... but clearly that is impossible.

Mostly, this is just a simple demonstration of how the authors are communicating to us that aortic diameters increase with both body size and age. You should definitely not try to interpolate reference values from this- you’ll go cross-eyed, almost for sure.

I double-dog dare you to come up with a value for someone in the middle of those axes- say a 45 year old, 160 cm person.

Instead, try this:

### aoroot.parameterz.com/devereux2012/

I put together a quick tool that will help you find the correct reference values for the aortic root (aortic valve and sinus of Valsalva) that are gender-specific age- and size-adjusted. For instance, here are the calculated values for the previously mentioned and difficult-to-interpolate 160 cm, 45 year old female:

I did not incorporate these new calculations into the existing ones for a couple of reasons, not the least of which is time. The other concerns are that these new values are mainly for adults (all subjects were >=15yrs), and that these calculations are multi-factorial (age, size, and gender) and would have required a major re-work of the interface, for which I do not have the time. I hope to do some rough comparisons sometime in the future, but for now I hope they are useful for those that deal with adult patients, both young and old.

## On the Cusp?

upcoming studies addressing the inadequacies of existing reference values for (adult) echocardiography

## NORRE Study

...due to the lack of consistency in current echocardiographic ‘reference values’, their use for clinical decision-making remains questionable.

So reads the ‘background’ section of the upcoming NORRE study (NOrmal Reference Ranges for Echocardiography), enrolling subjects aged 25 – 75yrs in 22 labs (mostly in Europe).

## EchoNoRMAL Study

The effectiveness of any diagnostic test is dependent upon the test’s ability to accurately detect abnormalities. An assumption of reliability and validity underlies all medical tests and echocardiography is no exception. The deﬁnition of ‘abnormal’ relies on the deﬁnition of ‘normal’ and needs to acknowledge normal physiological variation that may arise from factors such as body size, gender, and ethnicity.

So reads the introduction of the EchoNoRMAL study, an effort by ambitious scientists in New Zealand. Also concerning adults, aged 18 and up.

Looks like the adult echo community is winning.

## The Why and How of... What the?

Print is dead.

When the editorial process of a peer-reviewed journal deteriorates as it does in the January JASE issue, then I think it is safe to say print is dead.

What was destined to be The editorial concerning z-scores for echocardiography will now only make sense if we read the coming errata, which diffuses the point entirely. I considered writing a letter to the editor, but it is not obvious to me that they would bother to read the submitted material or, are at all familiar with the subject.

Not sure what I am driving at? Read the editorial yourself and see if you can make sense of how-- all of a sudden-- we are now supposed to be adjusting cardiac size for body mass index
(Hint: we’re not. It’s a colossal misprint).