Frailty, Cognitive Impairment, and Anticoagulation Among Older Adults with <scp>Nonvalvular</scp> Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Oral anticoagulation (OAC) is challenging in older patients with nonvalvular atrial fibrillation (NVAF) who are often frail and have cognitive impairment. We examined the characteristics of older NVAF patients associated with higher odds of physical and cognitive impairments. We also examined if these high-risk patients have different OAC prescribing patterns and their satisfaction with treatment because it may impact optimal management of their NVAF. METHODS: The patients in the Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF study cohort 2016-2018) had NVAF, were aged 65 and older, and eligible for the receipt of OAC. Measures included frailty (Fried Frailty scale), cognitive impairment (Montreal Cognitive Assessment Battery), OAC prescribing and type (direct oral anticoagulant [DOAC] or vitamin K antagonist [VKA]), depressive symptoms (Patient Health Questionnaire-9), bleeding, stroke risk, and treatment benefit (Anti-Clot Treatment Scale). RESULTS: Patients (n = 1,244) were 49% female, aged 76 (standard deviation = 7) years. A total of 14% were frail, and 42% had cognitive impairment. Frailty and cognitive impairment co-occurred in 9%. Odds of having both impairments versus none were higher with depression (odds ratio [OR] = 4.62; 95% confidence interval [CI] = 2.59-8.26), older age (OR = 1.56; 95% CI = 1.29-1.88), lower education (OR = 3.81; 95%CI = 2.13-6.81), race/ethnicity other than non-Hispanic White (OR = 7.94; 95% CI = 4.34-14.55), bleeding risk (OR = 1.43; 95% CI = 1.12-1.81), and stroke risk (OR = 1.35; 95% CI = 1.13-1.62). OAC prescribing was not associated with CI and frailty status. Among patients taking OACs (85%), those with both impairments were more likely to take DOAC than VKA (OR = 1.69; 95% CI = 1.01-2.80). Having both impairments (OR = 1.87; 95% CI = 1.08-3.27) or cognitive impairment (OR = 1.56; 95% CI = 1.09-2.24) was associated with higher odds of reporting lower treatment benefit. CONCLUSION: In a large cohort of older NVAF patients, half were frail or cognitively impaired, and 9% had both impairments. We highlight the characteristics of patients who may benefit from cognitive and physical function screenings to maximize treatment and enhance prognosis. Finally, the co-occurrence of impairment was associated with low perceived benefit of treatment that may impede optimal management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".