Non-invasive monitoring strategies for atrial fibrillation detection in adult cardiac surgery patients after hospital discharge: A scoping review
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a common complication after cardiothoracic surgery, affecting up to 50 % of patients. It can develop after discharge, leading to frequent hospital readmissions. There is a growing need for effective monitoring strategies to detect AF in the post-discharge period. OBJECTIVES: To synthesis the available literature on various mobile monitoring devices used to detect AF in adult cardiac surgery patients post-discharge from the hospital. METHODS: Following Arksey and O'Malley's framework and the PRISMA-ScR guidelines. A comprehensive search of six databases (PubMed; MEDLINE; CINAHL; Scopus; ProQuest; and Web of Science) was performed, including studies published between 2009 and 2024. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: A total of 1256 de-duplicated studies were screened, and 102 studies underwent full-text review. Five studies were included: four prospective cohort studies, and one randomised clinical trial. Samples sizes ranged from 23 to 730 adults undergoing cardiac surgery, with follow-up between four weeks to three months post-discharge. Handheld and wearable ECG-based devices were the most used tools for AF detection, demonstrating high sensitivity and specificity. Their use effectively reduced unplanned hospital visits and improved clinical outcomes. Patient adherence to monitoring protocols was generally high, though variability in engagement was noted. CONCLUSIONS: Handheld and wearable ECG- based devices, are effective for post-discharge AF detection in cardiac surgery patients. Integrating these tools into routine post-discharge care can improve patient outcomes. Future research should focus on long-term effectiveness and strategies to optimise patient engagement and implementation in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".