Malnutrition is prevalent in patients with cardiorenal syndrome and negatively influences clinical outcome.
Bibliographic record
Abstract
Cardiorenal syndrome (CRS) describes the concurrent failure of cardiac and renal function, each influencing the other. Malnutrition and cachexia frequently develop in patients with heart failure or kidney failure. However, no information is currently available on the prevalence of malnutrition in CRS patients. We studied CRS patients admitted to an internal medicine ward during a 5-month period and evaluated their clinical characteristics and nutritional status. Malnutrition risk was assessed by using the validated screening tool NRS-2002 whilst body composition was assessed by bioimpedance analysis and muscle function was measured by handgrip (HG) strength. Cardiac mass was also recorded. Length of stay, hospital readmission and 6-month mortality were registered. During the study period, 22 CRS patients were studied. Twenty patients were diagnosed with either CRS type 1 or CRS type 5. In CRS patients, fat-free mass showed a trend toward representing a protective factor for 6-month mortality (OR=0.904; p=0.06). Also, fat-free mass correlated with HG strength and cardiac ejection fraction. Malnutrition risk was diagnosed in 45% of the patients, whereas 8 patients met the definition of cachexia. Even without statistical significance, CRS patients with malnutrition had lower BMI (Body Mass Index) (p=0.038) and fat-free mass (p= n.s.). However, CRS malnutrition was associated to higher 6-month mortality (p= 0.05), and appears to negatively influence the outcome in CRS (OR= 9; p= 0.06). Our results show that malnutrition is prevalent in CRS patients and influences the clinical outcome. The assessment of nutritional status, and particularly body composition, should be implemented in daily practice of patients with CRS.
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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 | 0.001 |
| 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 | 0.000 | 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".