Read on Twitter
1/ Alright, this heady stuff even for me ( @ProfDFrancis is a bit of a groundbreaker in the cardiac community, his work on biventric. pacemakers is next-level, really cool). He approaches info from an engineering and mathematical viewpoint. https://twitter.com/rigmatthew/status/1294473061136314374
2/ As I understand his viewpoint, what he’s saying is that this study took the cardiac MRI measurements of a “normal” Ejection Fraction (basically, the amount of blood the heart pumps out to the body) and set it as fairly narrow range (mean 56% w/ standard deviation, 54-58%.
3/ The problem is that’s essentially saying that roughly 2/3 of the human population is walking around w/ a “normal” EjectionFraction (EF) of right at around 56%. In reality, “normal range” of the median 2/3 of population varies greatly from maybe a low of 49% -70% (ballpark).
4/ That’s a crazy narrow and restrictive range. It’d be akin to saying the average skin tone of 2/3 of the population, if averaged, would be a sort of light brown color. Well, 2/3 of the population isn’t that one single color. It’s a vastly wide range of tones.
5/ This study, however, predicated findings on the statistical assumption that a normal EF% (mean w/ standard dev.) was 54-58% when in the real world it probably closer to 48-70%.
6/ This means that any finding outside of that 54-58% range falls into being abnormal. They set “normal” as a far too small/narrow range and this essentially balloons the number of abnormal findings.
7/ So, looking at least at just this study, it would initially imply that Covid19 myocarditis is a much bigger issue based upon cardiac MRI screenings of “normal” EF% when, the reality, may be much different.
8/ Now, is Covid myocarditis “no big deal”? Definitely not, it’s a huge deal w/ significant morbidity & mortality implications. But, if you’re making decisions based upon studies, this particular one looks to be empirically flawed based upon its initial assumption of “normal.”
