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Record W3093484152 · doi:10.4212/cjhp.v73i4.3025

Clinical Blood Isolates from Hemodialysis Patients: Distribution of Organisms and Antimicrobial Resistance, 2007–2014

2020· article· en· W3093484152 on OpenAlexaffvenueabout
Courtney K. Lawrence, Chris Sathianathan, Mauro Verrelli, Philippe Lagacé‐Wiens, Robert E. Ariano, Grace Badejo, Michelle Boyce, Jane Davis, Sheryl Zelenitsky

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsOttawa HospitalSt. Boniface HospitalUniversity of Manitoba
Fundersnot available
KeywordsHemodialysisMedicineCiprofloxacinAntibiotic resistancePopulationCeftriaxoneInternal medicineStaphylococcus aureusAntimicrobialAntibioticsDrug resistanceMicrobiologyVancomycinEmpiric therapyBiologyBacteria

Abstract

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Background: Given the morbidity and mortality associated with bloodstream infections in hemodialysis patients, understanding the microbiology is essential to optimizing treatment in this high-risk population.Objectives: To conduct a retrospective surveillance study of clinical blood isolates from adult hemodialysis patients, and to predict the microbiological coverage of empiric therapies for bloodstream infections in this population.Methods: Clinical blood isolate data were collected from the 4 main outpatient hemodialysis units in Winnipeg, Manitoba, from 2007 to 2014. The distribution of organisms and antimicrobial susceptibilities were characterized. When appropriate, changes over time were tested using time series analysis. Study data were used to predict and compare the microbiological coverage of various empiric therapies for bloodstream infections in hemodialysis patients.Results: The estimated annual number of patients receiving chronic hemodialysis increased steadily over the study period (p < 0.001), whereas the number of blood isolates increased initially, then decreased significantly, from 180 in 2011 to 93 in 2014 (p = 0.04). Gram-positive bacteria represented 72.6% (743/1024) of isolates, including Staphylococcus aureus (36.9%, 378/1024) and coagulase-negative staphylococci (23.1%, 237/1024). Only 26.1% (267/1024) of the isolates were gram-negative bacteria, the majority Enterobacteriaceae. The overall rate of methicillin resistance in S. aureus was 17.5%, and although annual rates were variable, there was a significant increase over time (p = 0.04). Antibiotic resistance in gram-negative bacteria was relatively low, except in Escherichia coli, where 13.5% and 16.2% of isolates were resistant to ceftriaxone and ciprofloxacin, respectively. Empiric therapy with vancomycin plus an agent for gram-negative coverage was predicted to cover 98.8% to 99.7% of blood isolates from hemodialysis patients, whereas cefazolin plus an agent for gram-negative coverage would cover only 67.5% to 68.4%.Conclusions: In an era of increasing antimicrobial resistance, data such as these and ongoing surveillance are essential components of antimicrobial stewardship in the hemodialysis population.Keywords: hemodialysis, microbiology, surveillance, resistance, antimicrobial stewardshipRÉSUMÉ Contexte : Étant donné la morbidité et la mortalité associées aux infections du sang parmi les patients en hémodialyse, la compréhension de la microbiologie est essentielle à l’optimisation du traitement de cette population exposée à un risque élevé.Objectifs : Mener une étude de surveillance rétrospective des isolats de sang cliniques des patients adultes en hémodialyse et prédire la couverture microbiologique des thérapies empiriques contre les infections du sang dans cette population.Méthodes : Les données relatives aux isolats de sang cliniques ont été recueillies dans les quatre unités ambulatoires principales d’hémodialyse à Winnipeg (Manitoba), entre 2007 et 2014. La caractérisation a porté sur la distribution des organismes et les susceptibilités aux antimicrobiens. L’évolution dans le temps a été testée au besoin à l’aide d’une analyse chronologique. Les données de l’étude ont permis de prédire et de comparer la couverture microbiologique de diverses thérapies empiriques contre les infections du sang pour les patients en hémodialyse.Résultats : On estime que le nombre annuel de patients recevant une hémodialyse chronique a augmenté régulièrement au cours de la période de l’étude (p < 0,001); le nombre d’isolats de sang a tout d’abord augmenté, puis il a grandement diminué : de 180 en 2011, il est passé à 93 en 2014 (p = 0,04). Les bactéries à Gram positif représentaient 72,6 % (743/1024) des isolats, y compris les Staphylococcus aureus (36,9 %, 378/1024) et les staphylocoques à coagulase négative (23,1 %, 237/1024). Seulement 26,1 % (267/1024) des isolats étaient des bactéries à Gram négatif, la majorité desquelles étant des Enterobacteriaceae. Le taux général de résistance à la méticilline de S. aureus était de 17,5 %, et bien que les taux annuels étaient variables, une augmentation importante a été observée avec le temps (p = 0,04). La résistance aux antibiotiques des bactéries à Gram négatif était relativement faible, sauf Escherichia coli, où respectivement 13,5 % et 16,2 % des isolats étaient résistants à la ceftriaxone et à la ciprofloxacine. On prévoyait que la thérapie empirique à la vancomycine associée à un agent pour la couverture à Gram positif couvrirait de 98,8 % à 99,7 % des isolats de sang des patients en hémodialyse, tandis que la céfazoline associée à un agent de la couverture à Gram négatif ne couvrirait que 67,5 % à 68,4 %.Conclusions : À une époque qui se caractérise par une augmentation de la résistance aux antimicrobiens, des données comme celles-ci et celles portant sur la surveillance continue sont des composantes essentielles de la bonne gestion de l’utilisation des antimicrobiens pour les patients adultes en hémodialyse.Mots-clés : hémodialyse, microbiologie, surveillance, résistance, gestion de l’utilisation des antimicrobiens

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 score0.141
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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
Published2020
Admission routes3
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