Prevalence of atrial fibrillation and outcomes in older long-term care residents: a systematic review
Bibliographic record
Abstract
BACKGROUND: anticoagulation is integral to stroke prevention for atrial fibrillation (AF), but there is evidence of under-treatment in older people in long-term care (LTC). OBJECTIVE: to synthesise evidence on the prevalence and outcomes (stroke, mortality or bleeding) of AF in LTC and the factors associated with the prescription of anticoagulation. METHODS: studies were identified from Medline, CINAHL, PsycINFO, Scopus and Web of Science from inception to 31 October 2019. Two reviewers independently applied the selection criteria and assessed the quality of studies using the Newcastle Ottawa Scale. RESULTS: twenty-nine studies were included. Prevalence of AF was reported in 21 studies, ranging from 7 to 38%. Two studies reported on outcomes based on the prescription of anticoagulation or not; one reported a reduction in the ischaemic stroke event rate associated with anticoagulant (AC) prescription (2.84 per 100 person years, 95% confidence interval [CI]: 1.98-7.25 versus 3.95, 95% CI: 2.85-10.08) and a non-significant increase in intracranial haemorrhage rate (0.71 per 100 person years, 95% CI: 0.29-2.15 versus 0.65, 95% CI: 0.29-1.93). The second study reported a 76% lower chance of ischaemic stroke with AC prescription and a low incidence of bleeding (n = 4 events). Older age, dementia/cognitive impairment and falls/falls risk were independently associated with the non-prescription of anticoagulation. Conversely, previous stroke/transient ischaemic attack and thromboembolism were independently associated with an increased prescription of anticoagulation. CONCLUSION: estimates of AF prevalence and factors associated with AC prescription varied extensively. Limited data on outcomes prevent the drawing of definitive conclusions. We recommend panel data collection and systems for linkage to create longitudinal cohorts to provide more robust evidence.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".