And this is my somewhat structured peer review, based mostly on results from Cohort Diagnostics and pending an answer on the point above re potential inclusion of measurements:
Concepts included and observed:
- The concept set used for this phenotype is OK, although somewhat dependant on the observation of complications. These can be indeed the first clue that leads to a diagnosis, but there is always the possibility that these would be long-term sequelae, leading to index date inaccuracies
- As mentioned, I miss the inclusion of key measurements. I would NEVER diagnose SLE without supporting immunology/serologies. I understand some datasets might not have measurements data, but maybe it is worth studying what happens when we include vs exclude these in the phenotype in the datasets with good measurement capture
Orphan concepts (standard only reviewed):
The app was a bit jumpy and did not allow me to review these for all databases at once. In any case, I skimmed through each of one of these and could not find any ‘never miss’ orphan concept. Some could be symptoms of SLE (eg certain rashes) but of course these can be caused by many other conditions/exposures
Incidence Rates
Somehow this did not work. I tried a few things but kept getting an error message (Error: there is no package called ‘ggh4x’). Can you see if you also see this @jswerdel ? This is an important bit of information for me, because I want to make sure your phenotype matches my expectations based on previous descriptive epi literature, however scarce that might be.
Index event breakdown
This matched my expectation that most cases would be identified by Concept ID 257628. It also made me realise that we missed the inclusion of hydroxychloroquine or chloroquine among common treatments in the ‘Plan’ section of the Clinical description above.
Visit context
this again matched my expectation that most diagnoses would be made in outpatient clinics, even in data sources with good/granular information on visit types, e.g. Pharmetrics Plus
Cohort overlap
I guess this was never going to be useful for this one, but I still checked overlap with C225: [P] Drug-induced Lupus (180Pe, 365Era) because I’m cheeky like that. I did not see great overlap, but I did see a bit of that. Somewhat reassuring, but could dive in more if needed, because these are different conditions. Again, antibody measurements would help a great deal
Cohort characterization
This reassured me quite a bit as it showed the profile I expected, particularly in terms of socio-demographics but also in terms of the treatments observed etc
Compare cohort char
This was also useful. I used the ‘drug-induced lupus’ cohort as a “benchmark” of sorts for comparison, and indeed saw the differences I expected in terms of age-sex profile and treatments
SUMMARY
It is likely that this is a good working phenotype for incident (newly diagnosed) SLE, which I understand is the target here. Recommendations could include the following:
- Inclusion of ANA or dsDNA antibody measurements could increase PPV in databases where these are available
- Include hydroxychloroquine in the clinical description for completeness, as this is indeed a first line treatment for SLE
NOTE: I did not review Incidence Rates as there was some glitch in the app. Let me know if I should redo/re-review if it works for you or you manage to fix this.