Thank you @Marcela and @david_vizcaya . I 
our community so much that we don’t feel ‘limited’ by 28 days in Phenotype Phebruary, we create a Day 29!!! That’s the spirit!
And thank you for initiating an important discussion on ‘Acute Kidney Injury’. This is an outcome that we’ve encountered over and over again, and each time, it kind of feels like we attempt to reinvent the wheel, so it would be VERY helpful if this discussion leads to a community consensus approach that we can develop and thoroughly evaluate across a large network of data partners.
Re: change in creatinine values, I’ll note that during CHARYBDIS we wrote a custom script outside of ATLAS to capture these cases. @aostropolets did a lot of work in this space back then. And we found that few databases were actually capturing SCr values with the frequency necessary to observe the acute changes. So, while it is currently a known limitation in ATLAS, I suspect its primarily a limitation of most data that will hold us back from using the creatinine change values. But, for data partners with complete capture of SCr, there is a crude work-around one can use in ATLAS: instead of looking for relative changes (e.g. increase in SCr >=0.3 mg/dl in 48 hours), one can create entry events based on absolute values (e.g. SCr <1.35 mg/dL (upper bound of normal range) AND mesurement value SCr > 1.65 mg/dL (upper bound + 0.3) the next day). This would not be a ‘sensitive’ definition, in that it’s possible for someone to have a lower baseline value and still qualify for AKI, but it would be a ‘specific’ definition in that anyone with two values on two days meeting these thresholds would certainly qualify. And, at least according to this article by Waiker et al, if I’d reading it correctly, the absolute increase is often ~2 mg/dL, so this would mean we’d capture most of the cases this way (at least those without pre-existing kidney disease who start with a normal value). And if we wanted to get even cuter, then we could create multiple entry events to model the ‘Increase in SCr >=1.5 time baseline’, such as ‘(baseline SCr < 1.35 AND SCr > 2.025 (1.351.5) in 7 days post index) OR (baseline SCr < 2 AND SCr > 3 (21.5) in 7 days post index) OR (baseline SCr < 3 AND SCr > 4.5 (31.5) in 7 days post index) OR (baseline SCr < 5 AND SCr > 7.5 (51.5) in 7 days post index)’. We could profile the data of baseline values to make sure we covered the observed scenarios, to see how much we ‘miss’ by this approximate approach but I’d be willing to bet we could get very close with only a couple iterations.