Mild cognitive impairment impacts health outcomes of patients with atrial fibrillation undergoing a disease management intervention
Bibliographic record
Abstract
Objective: Mild cognitive impairment (MCI) is prevalent in atrial fibrillation (AF) and has the potential to contribute to poor outcomes. We investigated the influence of MCI on survival and rehospitalisation in patients with chronic forms of AF undergoing a home-based, AF-specific disease management intervention (home-based intervention (HBI)) or standard management (SM). Methods: The Montreal Cognitive Assessment tool was administered at baseline (a score of <26/30 indicated MCI) in patients with AF randomised to HBI versus SM. Post hoc analyses of mortality and rehospitalisations during a minimum 24-month follow-up were conducted in the overall cohort and in each study group separately. Results: Of 260 patients (mean age 72±11, 47% female), 65% demonstrated MCI on screening (34% in SM; 31% in HBI). Overall, the number of days spent alive and out-of-hospital during follow-up (P=0.012) and all-cause rehospitalisation were influenced by MCI during follow-up (OR 3.16 (95% CI 1.46 to 6.84)) but MCI did not influence any outcomes in the SM group. However, survival was negatively influenced by MCI in the HBI group (P=0.036); those with MCI in this group were 5.6 times more likely to die during follow-up (OR 5.57 (95% CI 1.10 to 28.1)). Those with MCI in the HBI group also spent less days alive and out-of-hospital than those with no MCI (P=0.022). MCI was also identified as a significant independent correlate of shortest duration of event-free survival (OR 3.48 (95% CI 1.06 to 11.4)), all-cause rehospitalisation (OR 3.30 (95% CI 1.25 to 8.69)) and cardiovascular disease (CVD)-related rehospitalisation (OR 2.35 (95% CI 1.12 to 4.91)) in this group. Conclusions: The effectiveness of home-based, disease management for patients with chronic forms of AF is negatively affected by comorbid MCI. The benefit of adjunctive support for patients with MCI on CVD-related health outcomes requires further investigation.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".