Thursday, April 4, 2013

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.


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:

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

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

Monday, April 1, 2013

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:

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.