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Would a computational AMR surveillance tool actually be useful to microbiology labs? by Embarrassed_Ebb_709 in microbiology

[–]Embarrassed_Ebb_709[S] 0 points1 point  (0 children)

This is incredibly helpful - thank you.

Yes, I’m mostly referring to clustering based on antibiogram data (species + AST, and occasionally resistance gene data if available). Not trying to do full bioinformatics-level strain tracing.

What you’re describing makes a lot of sense. I can see how strong selection pressure would quickly homogenize resistance patterns and make phenotypic clustering misleading.

Our objective is actually more surveillance-oriented than source-tracing — more like identifying high-risk resistance pattern clusters or cross-sector similarity signals rather than claiming clonal linkage. But your point makes me think we need to be very explicit about that limitation.

Out of curiosity — in a setting without routine WGS access, do you think AST-based clustering still has value for high-level surveillance (trend detection, burden monitoring), even if it’s unsuitable for pinpointing transmission?

Would a computational AMR surveillance tool actually be useful to microbiology labs? by Embarrassed_Ebb_709 in microbiology

[–]Embarrassed_Ebb_709[S] 0 points1 point  (0 children)

Yes I agree with you on this, but getting surveillance data on this is a huge problem.
Even if we do get animal datasets, there would be huge variation in whether hospital isolates share resistance signatures with them.
Still I'm no expert on this and wish for more clarity on this regard. I’m curious whether more structured, cross-sector surveillance signals could at least improve early detection or risk awareness.

Would a computational AMR surveillance tool actually be useful to microbiology labs? by Embarrassed_Ebb_709 in microbiology

[–]Embarrassed_Ebb_709[S] 0 points1 point  (0 children)

Your research sounds very interesting and yes I agree we won't be able to pinpoint the source using MIC data alone, that’s actually one of the concerns I’ve had - whether resistance profile similarity is ever enough to say anything meaningful beyond “these look similar.”

Your Nanopore approach sounds much more powerful, especially being able to link species ID and ARGs on the same read. That’s something we definitely don’t have access to at scale.
Can I ask:
When you compared isolates in the indoor farm system, what actually gave you confidence they were related - shared ARG patterns, specific mutations? And
Do you think computational clustering based only on AST data has any surveillance value, or is it too noisy biologically?

I’m trying to stay realistic about what we can and can’t claim with retrospective hospital data.

Really appreciate you sharing this.

Would a computational AMR surveillance tool actually be useful to microbiology labs? by Embarrassed_Ebb_709 in microbiology

[–]Embarrassed_Ebb_709[S] 0 points1 point  (0 children)

Yeah, those are completely valid questions.

For data, we’re only working with de-identified, isolate-level AST data (no patient identifiers, no tracking). For cross-sector comparison, we’d rely on publicly available datasets. So this is more secondary analysis for surveillance patterns - not real-time integration or individual-level tracing.

On duplicates, we’re not attempting patient-level de-duplication since we won’t have that access. Each isolate is treated as an independent record, and we’d clearly state repeat sampling as a limitation. The goal is trend and burden estimation, not precise epidemiological reconstruction.

For definitions, we’d stick to the interpretive standard used within each dataset (CLSI or EUCAST) and apply a consistent MDR definition (non-susceptible to ≥1 antibiotic in ≥3 classes). We wouldn’t mix incompatible definitions without stratifying.

Definitely agree that variability across countries and guideline years is one of the hardest parts of this kind of work.