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Record W2751387550 · doi:10.1111/acem.13303

Rate Control With Beta‐blockers Versus Calcium Channel Blockers in the Emergency Setting: Predictors of Medication Class Choice and Associated Hospitalization

2017· article· en· W2751387550 on OpenAlexafffundabout
Clare Atzema, Peter C. Austin

Bibliographic record

VenueAcademic Emergency Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersOntario Ministry of Health and Long-Term CareHeart and Stroke Foundation of Canada
KeywordsMedicinePropensity score matchingAtrial fibrillationLogistic regressionEmergency departmentInternal medicineHeart rateEmergency medicineCardiologyBlood pressure

Abstract

fetched live from OpenAlex

OBJECTIVES: Rate control is an important component of the management of patients with atrial fibrillation (AF). Previous studies of emergency department (ED) rate control have been limited by relatively small sample sizes. We examined the use of beta-blockers (BBs) versus nondihydropyridine calcium channel blockers (CCBs) in ED patients from 24 sites and the associated hospital admission rates. METHODS: In this preplanned substudy, we examined chart data on AF patients who visited one of 24 hospital EDs in Ontario, Canada, between April 2008 and March 2009. We describe the proportion of patients who received either a BB or a CCB, had a heart rate < 110 beats/min 2 hours later, and had any complications. We used hierarchical logistic regression modeling to determine the predictors of BB versus CCB use and to assess the between-hospital variation in use of BBs versus CCBs. Solely in patients who had no rhythm control attempts, we examined the difference in the probability of hospital admission after propensity score matching patients by medication class. RESULTS: Of the 1,639 patients who received either a BB (n = 429) or a CCB (n = 1,210), 70.9% of the patients who received a BB had successful rate control versus 66.1% for a CCB. Complications were rare (2.4%), and the large majority were hypotension (2.0%). In adjusted analyses, predictors of receiving a BB (compared to a CCB) included already being on a BB, being sent in from a doctor's office, or being seen at a teaching hospital. In contrast, patients with evidence of heart failure, prior use of a CCB, a higher presenting heart rate, or a successful pharmacologic cardioversion (vs. no attempt) or who were seen at the highest AF volume EDs were significantly less likely to receive a BB, compared to a CCB. Systematic between-hospital differences accounted for 8% of the variation in BB versus CCB use. Hospital characteristics accounted for the large majority of that variation: after accounting for patient characteristics the between-hospital variation decreased by a relative 2.8%. By further adjusting for hospital characteristics, it decreased by a relative 74.7%. Among propensity score-matched patients with no rhythm control attempts, more CCB patients were admitted (51.6%) compared to BB patients (40.0%; difference of 11.6%; 95% confidence interval = 7.9%-16.2%). CONCLUSIONS: In this study of 24 EDs, CCBs were used more frequently for rate control than BBs, and complications were rare and easily managed using both agents. Variation between hospitals in BB versus CCB use was predominantly due to hospital characteristics such as teaching status and AF volumes, rather than different case mix. Among patients who did not receive attempts at rhythm control, use of a BB for rate control was associated with a lower rate of hospitalization.

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)0.005
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0000.001
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 head0.065
GPT teacher head0.366
Teacher spread0.301 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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