1/ #LBBB generates often a classical pattern on #EchoFirst. The pattern is very distinctive in Tissue Doppler of the septum.

The classical pattern arises from the time lapse of the activation and relaxation of the two walls, creating a pattern of interaction due to a sequence temporal imbalances of the tension between the two walls.
2/ As the septum is activated first, it contracts (shortening - septal flash) without activation of the lateral wall, which stretches. This generates slower pressure build up than a normal IVC, which then is prolonged.
3/ During ejection, the LV volume decreases, so both walls shorten. Tension, however, declines first in the septum, as this was activated first. This leads to a tension imbalance, so the lateral wall continues to shorten, the more relaxed septum stretches.
4/ The stretching of the septum builds up an elastic tension in the septum, which is released as recoil, when tension declines in the lateral wall, showing a post systolic shortening in the septum which is due to elasticity.
5/ This classical pattern is also very evident in the strain curves, and an integrated strain analysis will show this, in a semi quantitative way describing how work is wasted by looking at the opposing wall. https://t.co/8DS2F1S0BW
6/ This classical interaction pattern explains all changes seen in apical velocities (apical rocking), basal velocity curves, strain and strain rate.
7/ Applying estimated LV pressure do not add information, simply because the pressure curve is the same for both walls, so the different strain-pressure loops arises from plotting different strain curves against the same pressure curve, the differences lie in the strain.
This is the CLASSICAL pattern. This is dependent on a normally functioning lateral wall (except for the delay) https://t.co/fQ83cMGI3k
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@mugecevik is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
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The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
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The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
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The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
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The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
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