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Record W4388211377 · doi:10.1007/s12630-023-02619-8

Prevention of postoperative atrial fibrillation in cardiac surgery: a quality improvement project

2023· article· en· W4388211377 on OpenAlexaff
Sinead Egan, Coilin Collins‐Smyth, Shruti Chitnis, Jamie Head, Allison Chiu, Gurdip Bhatti, Sean R. McLean

Bibliographic record

VenueCanadian Journal of Anesthesia/Journal canadien d anesthésie · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsVancouver Coastal HealthUniversity of British ColumbiaVancouver General Hospital
Fundersnot available
KeywordsMedicineAtrial fibrillationCardiac surgeryAmiodaroneIncidence (geometry)Cardiothoracic surgeryVascular surgeryInternal medicineCardiologyEmergency medicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Postoperative atrial fibrillation (POAF) has an incidence of 20-60% in cardiac surgery. The Society of Cardiovascular Anesthesiologists and the European Association of Cardiothoracic Anaesthesiology Practice Advisory have recommended postoperative beta blockers and amiodarone for the prevention of POAF. By employing quality improvement (QI) strategies, we sought to increase the use of these agents and to reduce the incidence of POAF among our patients undergoing cardiac surgery. METHODS: This single-centre QI initiative followed the traditional Plan, Do, Study, Act (PDSA) cycle scientific methodology. A POAF risk score was developed to categorize all patients undergoing cardiac surgery as either normal or elevated risk. Risk stratification was incorporated into a preprinted prescribing guide, which recommended postoperative beta blockade for all patients and a postoperative amiodarone protocol for patients with elevated risk starting on postoperative day one (POD1). A longitudinal audit of all patients undergoing cardiac surgery was conducted over 11 months to track the use of prophylactic medications and the incidence of POAF. RESULTS: Five hundred and sixty patients undergoing surgery were included in the QI initiative from 1 December 2020 to 1 November 2021. The baseline rate of POAF across all surgical subtypes was 39% (198/560). The use of prophylactic amiodarone in high-risk patients increased from 13% (1/8) at the start of the project to 41% (48/116) at the end of the audit period. The percentage of patients receiving a beta blocker on POD1 did fluctuate, but remained essentially unchanged throughout the audit (34.8% in December 2020 vs 46.7% in October 2021). After 11 months, the overall incidence of POAF was 29% (24.9% relative reduction). Notable reductions in the incidence of POAF were observed in more complex surgical subtypes by the end of the audit, including multiple valve replacement (89% vs 56%), aortic repair (50% vs 33%), and mitral valve surgery (45% vs 33%). CONCLUSIONS: This single-centre QI intervention increased the use of prophylactic amiodarone by 28% for patients at elevated risk of POAF, with no change in the early postoperative initiation of beta blockers (46.7% of patients by POD1). There was a notable reduction in the incidence of POAF in patients at elevated risk undergoing surgery.

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)0.075
metaresearch head score (Gemma)0.059
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 score0.075
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.320
Teacher spread0.266 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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