Actionable Antibiotic Resistance Insights Without Delay
digitalABST® uses machine learning to translate metagenomic bacterial data into clinically relevant antibiotic resistance insights. Designed for critical care environments, it provides rapid, data-driven support for antimicrobial stewardship and treatment planning.
digitalABST®
Machine Learning–Driven Resistance Insights
digitalABST® analyzes output from Bactfast® to predict antibiotic resistance patterns at the species level. By combining genomic data with advanced analytics, it supports faster, more informed antimicrobial decisions in high-acuity settings.
digitalABST® delivers detailed resistance profiling across identified bacterial species. Its machine learning models assess genomic markers associated with resistance, providing clinicians with a comprehensive view of potential antimicrobial effectiveness.
Bactfast® integrates seamlessly with other detection kits:
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The Challenge
The Limits of Conventional Susceptibility Testing
Delays in obtaining results can postpone targeted treatment, increasing the risk of complications in critical patients.
When infections go undetected by traditional methods, clinicians face diagnostic uncertainty that can compromise timely care.
Tests that miss less common or slow-growing organisms may result in incomplete diagnoses, hindering effective treatment decisions.
Tests that miss less common or slow-growing organisms may result in incomplete diagnoses, hindering effective treatment decisions.
The SOLUTION
How digitalABST® Supports Confident Treatment Decisions
digitalABST® uses the data output from Bactfast® without the need for additional sequencing. Machine modelled resistanceTM provides predictive antimicrobial resistance covering both horizontal and vertical mechanisms. The generated synthetic variations are linked to antibiotic molecules to ascertain sensitivity and resistance. Bacteria replicate at 200 – 1000 bp/s which can cause evolving resistance patterns. digitalABST® used machine modelling to provide predictive AMR based on the ascensions found in Bactfast®
Conventional ABST | Sequenced ABST | digitalABST |
|---|---|---|
Needs a positive culture to
get expressed sensitivity | Presence of sequenced
mutation does not
translate to expression | Machine modelled
predictive AMR covers
horizontal and vertical
mechanisms of resistance |
Competitive inhibition
causes variability in growth
and expression response. | Established mutations are
targeted and can miss
minor variations. Does not
consider horizontal
transfer mechanisms of
resistance. | Covers both horizontal and
vertical mechanisms. ML
models update
consistently. |
Culture dependent | Primer design / alignment
on specific WGS of
bacteria. | Metagenomic data along
with MMRTM evaluates all
possible resistance
variations for reported
accessions and link them
to therapeutic molecules |
Connect with Credence Genomics to learn more about our detection kits.
Data-Driven Antimicrobial Stewardship at the Point of Care
Dynamically Generated
Resistance Inherited & Acquired
No Additional Chemistry
Antibiotic Recommender
digitalABST®
digitalABST® uses the data output from Bactfast® without the need for additional sequencing. Machine Modelled Resistance™ provides predictive antimicrobial resistance, covering both horizontal and vertical mechanisms. The generated synthetic variations are linked to antibiotic molecules to determine sensitivity and resistance.
Bacteria replicate at 200–1,000 bp/s, which can lead to evolving resistance patterns. digitalABST® uses machine modelling to deliver predictive AMR based on the ascensions identified in Bactfast®.
Our Process
How Credence Genomics Detection Kits Work
Clinical samples are processed using validated wet-lab protocols designed to preserve microbial DNA while minimizing contamination. This ensures consistent, high-quality input across diverse sample types.
Next-generation sequencing captures the full microbial landscape within each sample. Proprietary bioinformatics and machine learning algorithms identify pathogens while separating commensal organisms and contaminants.
Results are delivered in a clear, structured format—providing species-level identification and clinically relevant insights that support informed treatment decisions and faster patient care for your needs.