P1538Low energy intake predicts readmission of elderly heart failure patients independently of nutritional status
Bibliographic record
Abstract
Abstract Background Malnutrition is frequently present and closely associated with poor clinical outcomes in elderly heart failure (HF) patients. Our previous study showed that low energy intake (EI) is associated with worse functional status in elderly HF inpatients after cardiac rehabilitation, but significance of EI in prediction of hospital readmission has not been elucidated fully. Purpose We examined whether low EI is a predictor of readmission for cardiac events in elderly HF patients. Methods We retrospectively retrieved data for 298 HF patients aged ≥65 years (median age of 77 years, interquartile range [IQR]: 71 - 82, female: 53%) who admitted to our institute for diagnosis and treatment of HF. Medical records were reviewed with regard to demography, medical history, comorbidities, medications, laboratory data, echocardiograms, functional status, nutritional status and total energy intake. Nutritional status was assessed using the Mini Nutritional Assessment Short Form (MNA-SF) and total EI per day were calculated at discharge by a registered dietitian and a trained physical therapist. The primary endpoint was readmission due to cardiovascular events including worsening HF, arrhythmia, angina pectoris and myocardial infarction during a 1-year follow-up period. Results The median period of follow-up was 235 days (IQR: 78–365 days). The 1-year readmission rate for cardiovascular events was 54.4%. The cutoff values of MNA-SF score and EI, calculated by ROC curve analysis to predict the primary endpoint, were 7 points (area under the curve [AUC]: 0.59, sensitivity: 0.65, specificity: 0.50) and 31.8 kcal/kg/day (AUC: 0.59, sensitivity: 0.83, specificity: 0.35), respectively. Patients with low MNF-SF score (≤7) or low EI (≤31.8 kcal/kg/day) had significantly higher readmission rate during a 1-year follow-up period than did the patients with high MNF-SF score or EI (MNA-SF: 60.7% vs. 45.6%, p<0.01, EI: 60.4% vs. 36.8%, p<0.01), respectively. When patients were classified into four groups using cutoff values of MNA-SF score and EI, 1-year readmission rate was significantly higher in patients with low EI than in those with high EI regardless of MNF-SF scores. In multivariate Cox proportional hazard analyses adjusted for known prognostic factors in addition to age and gender, hazard ratios (HR) were significantly higher in patients with high MNA-SF score and low EI (adjusted HR: 2.81, 95% confidential interval [CI]: 1.15 - 9.32, p=0.02) and low MNA-SF score (≤7) and low EI (adjusted HR: 4.16, 95% CI: 1.72 - 13.72, p<0.01) than those with high MNA-SF score and high EI. Kaplan-Meier curves of readmission rates Conclusions Low energy intake is a nutritional status-independent predictor of 1-year readmission rate in elderly HF patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".