Anticoagulation therapy and outcomes in patients with atrial fibrillation and serious mental illness: A systematic review and meta-analysis
Bibliographic record
Abstract
A systematic review was conducted to investigate prevalence, management and outcomes of atrial fibrillation (AF) in people with Serious Mental Illnesses (SMI) versus the general population. MEDLINE, EMBASE, and PsycINFO were searched for primary research written in English and published between 2004 and 2022. A total of 1459 studies were identified in the initial search of which 16 met the inclusion criteria. Studies (n = 4) reporting on ischaemic stroke and major bleeding events were included in the meta-analysis. Two independent reviewers extracted data and assessed risk of bias using the Newcastle-Ottawa Scale. Discrepancies were resolved by consulting a third reviewer. Low rates of AF were reported among people with SMI suggesting under-recognition or recording gaps. People with SMI and AF were less likely to receive oral anticoagulation therapy compared to the general population. When receiving warfarin, those with bipolar disorder experienced poor anticoagulation control as measured by time in INR therapeutic range. Pooled analysis of risk estimates showed that in patients with identified AF, SMI was not significantly associated with an increased risk of stroke (HR: 1.09; 95%CI: 0.85 to 1.40; I2 = 60%, p = 0.04) or major bleeding (HR: 1.11; 95%CI: 0.95 to 1.28; I2 = 57%, p = 0.03) when adjusted for underlying stroke and bleeding risks using the CHA2DS2VASc and HASBLED scales respectively. More research is needed to examine the prevalence, management and outcomes of AF in this population, and to evaluate the effect of the introduction of the novel anti-coagulants on these metrics over time.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".