MétaCan
Menu
Back to cohort
Record W3118292205 · doi:10.1093/ofid/ofaa439.1755

1575. Predictors of Negative Clinical Outcomes among Patients treated with Meropenem-Vaborbactam for Serious Gram-Negative Bacterial Infections: Impact of Delayed Appropriate Antibiotic Selection

2020· article· en· W3118292205 on OpenAlexaff
Sara Alosaimy, Abdalhamid M Lagnf, Sarah C J Jorgensen, Travis J Carlson, Jinhee Jo, Kevin W. Garey, David W. Allen, Lilian M. Abbo, Kailynn DeRonde, Ana Vega, Veena Venugopalan, Steven Saw, Vasilios Athans, Kimberly C. Claeys, Wesley D. Kufel, Matthew Miller, Michael P. Veve, Chritsine Yost, Lee Amaya, Jessica K. Ortwine, Taylor Morrisette, Susan L. Davis, Michael J. Rybak

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsSinai Health System
Fundersnot available
KeywordsMedicineInternal medicineLogistic regressionAntibioticsMeropenemUrinary systemCohortRetrospective cohort studyProcalcitoninStepwise regressionGastroenterologySepsisAntibiotic resistance

Abstract

fetched live from OpenAlex

Abstract Background Numerous number of studies have found a positive correlation between delayed appropriate antibiotic therapy and negative clinical outcomes (NCO) in Gram-negative bacterial infections (GNBI). The combination of meropenem with vaborbactam (MVB) received Food and Drug Administration approval for the treatment of complicated urinary tract infections and acute pyelonephritis caused by susceptible organisms in August 2017. We sought to determine the impact of delayed appropriate therapy with MVB on NCO among patients with GNBI. Methods Multi-center, retrospective cohort study from October 2017 to March 2020. We included adult patients treated with MVB for >72 hours. We excluded patients who received alternative appropriate antibiotics for GNB prior to MVB and patients with unknown dates for index culture. NCO were defined as 30-day mortality and/or microbiological recurrence. All outcomes were measured from MVB start date. Classification and regression tree analysis (CART) was used to identify the time breakpoint (BP) that delineates the risk of NCO. Multivariable logistic regression analysis (MLR) was used to examine the independent association between the CART-derived-BP and NCO. Variables were retained in the model if P< 0.2 and removed in a backward stepwise approach. Results A total of 86 patients were included from 13 institutions in the United States: median(IQR) age 55 (37-67) years, 67% male, and 48% Caucasian. Median(IQR) APACHE II and Charlson Comorbidity index scores were 18(11-26) and 4(2-6), respectively. Common sources of infection were respiratory (37%) and intra-abdominal (21%). The most common pathogens were carbapenem-resistant Enterobacterales (83%). CART-derived BP between early and delayed treatment was 48 hours, where NCO was increased (36% vs.7%; P=0.04). Delayed MVB initiation was independently associated with NCO in the MLR (aOR=7.4, P=0.02). Results of Regression Analysis of Variables Associated With Negative Clinical Outcomes and Delayed Appropriate Therapy with Meropenem-vaborbactam Conclusion Our results suggest that delaying appropriate antibiotic therapy with MVB for >48 hours significantly increases the risk of NCO in patients with GNBI. Clinicians must ensure timely administration of MVB to assure best outcomes in patients with GNBI. Disclosures Kevin W. Garey, PharMD, MS, FASHP, Merck & Co. (Grant/Research Support, Scientific Research Study Investigator) Michael J. Rybak, PharmD, MPH, PhD, Paratek (Grant/Research Support)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOpen Forum Infectious DiseasesSame topicAntibiotic Use and ResistanceFrench-language works237,207