Frailty phenotype and cognitive functioning in middle-aged and older adults with congenital heart disease: results of an international multicentric study
Bibliographic record
Abstract
Abstract Background The life expectancy of patients with congenital heart disease (CHD) has increased rapidly, resulting in a growing and aging population. Recent studies showed that older CHD patients have a higher morbidity, healthcare utilization, and mortality. Furthermore, many will encounter a state of frailty. To maintain longevity and quality of life, understanding the evolving medical and psychosocial challenges of older patients is important to enable the mapping of specific healthcare needs. There are indications that, independent of age and comorbidity, the frailty phenotype can guide risk prediction in chronically ill patients. However, current knowledge on frailty phenotype and cognitive function of older CHD patients is limited. Purpose This study aimed (i) to describe the frailty and cognitive profile of older adults with mild to complex CHD, and (ii) to investigate the relationship with patient-related factors (e.g., age, complexity of CHD and physiological stage). Methods Using a cross-sectional, multicentric design, we included 814 patients aged ≥ 40 years (median age: 52y [45.0-61.0]; 52% women; 13% mild, 70% moderate, 17% complex CHD) from 17 centers in 11 countries. The frailty phenotype was determined using the Fried method, which classifies patients as robust, prefrail and frail based on five criteria: weakness; slow walking speed; unintentional weight loss; exhaustion; and low physical activity. Cognitive function was evaluated using the Montréal Cognitive Assessment (MoCA). Comorbidities were assessed using the Charlson Comorbidity Index (CCI). Results In this sample, 52% of patients were assessed as robust, 42% prefrail, and 6% frail. Based on the MoCA, 39% had cognitive dysfunction. A weak negative correlation was found between frailty and cognitive functioning (rho=-0.210, p<0.001). Multinomial regression showed that prefrail and frail status was associated with older age, female sex, higher physiological class, and comorbidities (Table 1). Logistic regression demonstrated that cognitive dysfunction was related to older age and comorbidities. Conclusion Approximately half of the sample of CHD patients were (pre)frail and almost two out of five experienced cognitive impairment. These age-related problems were not related to the anatomical complexity of the heart condition, indicating that these issues are not limited to severe CHD. Future research is needed to determine the impact of these findings on healthcare use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".