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Record W2764337986 · doi:10.1093/pch/pxx086.095

PROTOCOL FOR REDUCING TIME TO ANTIBIOTICS IN FEBRILE NEONATES PRESENTING TO THE EMERGENCY DEPARTMENT: A QUALITY IMPROVEMENT INITIATIVE

2017· article· en· W2764337986 on OpenAlexaboutno aff
Amélie Boutin

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEmergency departmentTriageEmergency medicineAntibioticsSepsisProtocol (science)ConcomitantPediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Febrile neonates are at high risk of morbidity and mortality from infectious causes. This risk further increases if antibiotics are not received in a timely manner. Current guidelines recommend early initiation (less than 1 hour) of antibiotics for patients with severe sepsis. Time-to-antibiotic administration (TAA) should also be targeted as a quality-of-care (QOC) measure for febrile neonates. A previous evaluation showed that most of these patients were not receiving antibiotics in the first hour at our emergency department (ED). OBJECTIVES: We evaluated whether a simple quality improvement protocol would improve the proportion of febrile neonates receiving antibiotics within 60 minutes of arrival to the ED. DESIGN/METHODS: This was a pre-post intervention study conducted in the ED of an academic pediatric tertiary care hospital with an annual volume of approximately 83,000 patients in 2014-2016. Participants were a random sample of all children younger than 28 days old visiting the ED for a febrile illness. The new protocol, which consisted for the nurses, after triage, to place the patients directly in the resuscitation room for immediate assessment by a physician, was implemented in February 2016. Previously, these children were triaged level 2 on the Canadian Triage and Acuity Scale (CTAS), flagged and placed in a regular examination room waiting for the physician assessment. With the new protocol, IV access, blood culture, urine analysis and culture were immediately obtained by the nurse in charge with the concomitant assessment by the attending physician. Forty charts prior to and 50 charts after protocol initiation were reviewed by an archivist using a standardized form between 2014-2015 and 2016, respectively. The primary outcome was TAA. This was defined as the time from initial ED registration to the beginning of antibiotics infusion. As a secondary outcome, all cases were reviewed individually to determine barriers to rapid antibiotic administration (day, evening, or night shifts, other treatments or investigations, number of attempts for intravenous access) and to elicit new quality improvement strategies. RESULTS: During the study periods a total of 178 (pre) and 135 (post) patients fulfilled the inclusion criteria. Among the random samples, 6/50 (12%) of patients received their antibiotics within 60 minutes in the post-intervention period compared to 0/40 (0%) in the pre-implementation period (difference 12%; 95 CI: 1-24%). Within 90 minutes, the proportion improved from 1/40 (2.5%) to 29/50 (58%) (difference 56%; 95 CI: 38-68%). Median TAA in febrile neonates decreased from 182 minutes (interquartile range, 147-219 minutes) in the pre-implementation period to 85 minutes (interquartile range, 73-115 minutes) in the post-implementation period. The main obstacle to the goal of 60 minutes for TAA was the difficulty to get IV access as well as antibiotic availability. CONCLUSION: In this study, a new protocol mandating the immediate transfer of febrile neonate from triage to the resuscitation room improved proportion of febrile neonates receiving antibiotics in less than 60 minutes in our ED. Our results suggest that simple interventions can reduce TAA in a selected group of patients presenting to the ED.

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)0.074
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.074
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0040.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.117
GPT teacher head0.440
Teacher spread0.323 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations1
Published2017
Admission routes1
Has abstractyes

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