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Abstract 4138345: Incidence and Predictors of Infective Endocarditis in Bacteremic Patients

2024· article· en· W4404341363 on OpenAlexaffabout
Ahmed AlTurki, Jacinthe Boulet, Mohammed Abalhassan, Yi Fan, Vivian G. Loo, T. Huynh

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsMontreal Heart InstituteMcGill University Health Centre
Fundersnot available
KeywordsMedicineInfective endocarditisIncidence (geometry)EndocarditisBacteremiaInternal medicineCardiologyIntensive care medicineMicrobiologyAntibiotics

Abstract

fetched live from OpenAlex

Background: Infective endocarditis (IE) is a serious disease, with a high mortality rate.There has been a shift in the composition of organisms causing infective endocarditis. We aimed to provide a contemporary incidence of IE among bacteremic patients stratified by bacterial pathogens. We also sought to evaluate the predictors of IE in bacteremic patients. Methods: We included patients at a large quaternary centre in Canada. All bacteremias during the period from 2014 to 2016 were identified and data was collected on the following variables – patient demographics, organism, comorbidities, pre-disposing factors, clinical findings, pertinent biochemistry, performance of echocardiography, and diagnosis of IE. Diagnosis of IE was made using the modified Duke’s criteria. Logistic regression was used to identify predictors of IE. Results: There were 1,708 patients with bacteremia included in this analysis. The mean age was 64.5±17.0 years old; 39.7% were females and 77.4% were community acquired bacteremia. In addition, 47.0% had hypertension, 29.9% had diabetes mellitus, 17.5% had chronic kidney disease and 25.2% were immunosuppressed. There were 102 (6.0%) patients with confirmed infective endocarditis (IE). The proportion of bacteremic patients who developed IE was 13.2% in 281 patients with methicillin-sensitive Staphylococcus aureus, 5.4% in 56 patients with methicillin-resistant S. aureus, 2.4% in 292 patients with S. epidermidis, 9.9% in 121 patients with Streptococcus viridans group, 16.5% in 91 patients with Enterococcus, 1.3% in 303 patients with Escherichia coli, 38.5% in 13 in patients with Streptococcus bovis, and 9.5% in 21 patients with Propionibacterium acnes. Predictors of IE were intravenous drug use (odds ratio [OR]=8.64, 95% confidence interval [CI] 3.50- 21.3; p<0.0001), prosthetic heart valve (OR= 2.10, 95% CI 1.01- 4.39; p=0.049), structural heart disease (OR= 12.2, 95% CI 3.35- 44.67; p= 0.0002) and community acquired bacteremia (OR= 2.06, 95% CI 1.11- 3.82; p=0.02). CONCLUSIONS: Methicillin-sensitive Staphylococcus aureus is the commonest cause overall of IE followed by viridans group streptococcus. Streptococcus bovis and Enterococcus bacteremia have the highest incidence of IE. Knowledge of the incidence rates of IE among various pathogens may guide diagnostic testing such as pursuing more advanced imaging procedures for patients with increased risk of IE.

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)0.000
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.255
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 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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