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Record W2893002648 · doi:10.1093/ofid/ofy210.998

1165. Comparing Patient Risk Factors, Sequence Type, and Resistance Loci Identification Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections

2018· article· en· W2893002648 on OpenAlexaffabout
Derek R. MacFadden, Roberto G. Melano, Nathalie Tijet, William P. Hanage, Nick Daneman

Bibliographic record

VenueOpen Forum Infectious Diseases · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineAntibiotic resistanceLogistic regressionAntibioticsCephalosporinReceiver operating characteristicAntibiotic therapyInternal medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

Abstract Background To improve the adequacy of empiric antibiotic therapy, an important predictor of clinical outcome, rapid diagnostic tests of antibiotic resistance are increasingly being developed that identify the presence or absence of antibiotic resistance genes/Loci. Few approaches have utilized other sources of predictive information, which could be identified in shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage (e.g., sequence type). Methods Using a dataset of 414 Escherichia coli isolated from separate episodes of bacteremia at a single academic institution in Toronto, Canada between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic, sequence type, and gene identification) for classifying antibiotic resistance to three commonly used classes of broad-spectrum antibiotic therapy (third-generation cephalosporins, fluoroquinolones, and aminoglycosides). We used logistic regression models with binary predictor variables to generate model receiver operating characteristic curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) area under the curves (AUCs). Results Using two simple epidemiologic risk factors (prior antibiotic exposure and recent prior Gram-negative susceptibility), modest predictive discrimination was achieved (AUCs 0.65–0.74). Sequence type demonstrated strong discrimination (AUCs 0.84–0.94) across all three antibiotic classes. Epidemiologic risk factors significantly improved sequence-type prediction for cephalosporins and aminoglycosides (P < 0.05). Gene identification approaches provided the highest degree of discrimination (AUCs 0.73–0.99), with no statistically significant benefit of adding epidemiologic predictors. Conclusion Rapid identification of sequence type, or other lineage-based classification, could produce excellent discrimination of antibiotic resistance, and may be improved by incorporating readily available epidemiologic predictors. Disclosures All authors: No reported disclosures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.278
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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