296Clinical benefit of assessing cognitive function in frail patients with heart failure: a multicenter prospective cohort study
Bibliographic record
Abstract
Abstract Background Cognitive decline is highly prevalent in patients with heart failure (HF) and increases the risk of hospital readmission or mortality due to poor self-care ability. Although cognitive decline often coexists with physical frailty (PF) in HF, the clinical utility of combined assessment of both cognitive function and PF remains unclear. Purpose The aim of this study was to examine the prognostic value of assessing cognitive function and PF in patients with HF. Methods This prospective study was performed as a multicenter cohort study in Japan (FLAGSHIP). We enrolled 1611 patients admitted for acute HF or exacerbation of chronic HF and who were able to walk at discharge. Patients with severe dementia [Mini-Mental State Examination (MMSE) scores <18], severe psychological disorders or less than 6-month life expectancy were excluded. From data at discharge, we collected data on cognitive function, PF, age, gender, New York Heart Association class, left ventricular ejection fraction, brain natriuretic peptide, estimate glomerular filtration rate, hemoglobin, depression (5-item geriatric depression scale ≥2) and comorbidities, including atrial fibrillation, diabetes mellitus, stroke, and hyponatremia. PF was defined as ≥2 of the followings based on our previous publication: usual walking speed <0.8 m/s; grip strength <26 kg (men) or <17 kg (women); Performance Measure of Activity in Daily Living-8 ≥21; body mass index <20 kg/m2. Cognitive function was assessed by MMSE. We selected the optimal cutoff point of MMSE that predict a worse outcome by the receiver operating characteristic (ROC) curve analysis. Study outcome was a composite outcome of rehospitalization for worsening HF or all-cause mortality within 2 years after discharge. We used Cox proportional-hazard models to examine the association between the presence of cognitive decline and PF and 2-years prognosis, controlling for potential confound factors. Results A total of 507 events (31.5%) were observed (400 HF rehospitalization, 27 cardiac death, 80 non-cardiac death). The optimal cutoff point of MMSE was 28 (the area under the ROC curve: 0.58, p<0.01, sensitivity: 71.0%, specificity: 41.0%). There was a significant difference in event-free survival across the groups stratified by cognitive decline (MMSE <28) and PF (Figure). After adjusting for all variables, coexistence of both cognitive decline and PF was independently associated with 2-years prognosis (hazard ratio: 1.52, 95% confidence interval: 1.19–1.94). Conclusion Our data shows that even a slight decline in cognitive function leads to an increased risk of death or HF rehospitalization in frail patients with HF. Combined assessment both cognitive function and PF improves risk stratification for readmission and mortality in patients with HF. Acknowledgement/Funding This work was supported by a Grant-in-Aid for Scientific Research (A) from the Japan Society for the Promotion of Science [16H01862].
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".