Incidence and outcomes of dementia after a diagnosis of heart failure: a population-based study
Bibliographic record
Abstract
Abstract Background Although dementia and heart failure (HF) may co-exist, the interrelationships of these two conditions have not been fully explored in the population. This study aimed to assess the incidence and risk factors of dementia in HF patients and evaluate mortality after dementia diagnosis. Methods We conducted a population-based retrospective cohort study among eligible individuals aged 40 to 80 years who were newly diagnosed with HF in Canada, between April 1, 2002, and March 31, 2023. Patients with HF were stratified as inpatients or outpatients based on the location of the first recorded diagnosis of HF, and were compared with age-, sex-, and vascular risk factor-matched controls. Patients were followed from the index date of HF diagnosis until the end of study period or until an outcome occurred. Outcomes included incident dementia (using a validated administrative data algorithm) and all-cause mortality. Cause-specific proportional hazard models were used to examine associations between HF and dementia, and between dementia and subsequent outcomes, adjusting for age, sex, comorbidities, and neighborhood socioeconomic status. Results During 10-year follow-up of the inpatient cohort 14,977 patients were diagnosed with incident dementia among 156,420 HF patients. In the outpatient cohort, 24,504 patients developed incident dementia among 255,444 HF patients. The cumulative incidence of dementia was higher in HF patients than in matched controls among those diagnosed initially as inpatients or outpatients (Figure). Among inpatients diagnosed with HF before the age of 60, females had a dementia incidence rate that was 4.1 times higher than their matched controls, while males had a rate 3 times higher (Table). The incidence rate ratio decreased with increasing age, falling to 2.14 in females and 1.97 in males among those diagnosed with HF between 61 and 70 years, and 1.45 in females and 1.41 in males among those over the age of 70, indicating a diminishing rate of dementia with increasing age. Similar trends were observed in the outpatient setting. Key predictors of incident dementia included increasing age, female sex, diabetes, cerebrovascular disease, and low socioeconomic status. Using time-varying covariates, incident dementia was associated with higher mortality in both inpatient (HR: 2.44, 95% CI: 2.39, 2.49) and outpatient HF cohorts (HR: 2.67, 95% CI: 2.63, 2.72). Conclusion Patients with HF have an increased risk of dementia, with higher absolute rates of dementia in older age groups, but higher relative increase in younger patients compared to matched controls without HF. Dementia also confers higher mortality risk, emphasizing the need for integrated dementia prevention and care strategies in HF.Figure.Cumulative incidence of dementia Table.Incidence of dementia 10yr f/u
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".