FRAILTY MEETS NURSING COMPLEXITY: EXPLORING THEIR IMPACT ON HOSPITAL LENGTH OF STAY IN HEART FAILURE PATIENTS
Bibliographic record
Abstract
Abstract Background Frailty, a multidimensional condition, poses significant challenges in heart failure (HF) patients, frequently leading to increased nursing complexity and prolonged hospital length of stay (LOS). This study aims to investigate the association between frailty and nursing complexity, as well as their combined influence on hospital length of stay (LOS) in HF patients. Methods A retrospective observational study was conducted at an Italian university hospital, including all patients with HF admitted consecutively over one year. Frailty was measured using the Blaylock Risk Assessment Screening Score (BRASS), classifying patients into low (score ‹10), moderate (score 10–19), and high (score ≥20) risk categories. Nursing complexity was measured using the Nursing Dependency Index (NDI), defined as the number of nursing diagnoses per patient on hospital admission. Prolonged LOS was defined as stays exceeding the 75th percentile. Data were collected using the hospital discharge register and the Professional Assessment Instrument, a system designed to record nursing care. Pearson correlation analyses were conducted to explore relationships between variables. Latent Class Analysis (LCA) identified nursing complexity and frailty profiles, while logistic regression evaluated associations between LCA profiles and prolonged LOS. Results Among 608 patients (mean age 75.7 ± 13.06), the mean NDI score was 4.31 ± 3.44, and the mean BRASS score was 8.01 ± 6.00. The NDI increased significantly with frailty risk (low: 3.96 ± 3.34; moderate: 5.46 ± 3.71; high: 5.31 ± 3.00; F = 10.212, p ‹ 0.001). Correlations were observed between NDI and frailty (r = 0.213, p ‹ 0.001), NDI and LOS (r = 0.127, p ‹ 0.005), and frailty and LOS (r = 0.179, p ‹ 0.001). LCA identified two profiles: low complexity/low frailty (NDI: 3.62 ± 3.06, BRASS: 6.39 ± 4.50) and high complexity/high frailty (NDI: 6.92 ± 3.58, BRASS: 14.1 ± 6.96). Model fit indices indicated an acceptable fit (log–likelihood = –3193, AIC = 6585, BIC = 7021, and entropy = 0.726), demonstrating good class separation. Logistic regression showed that the high complexity/high frailty profile increased the odds of prolonged LOS by 87% (OR = 1.867, 95% CI: 1.225–2.846, p ‹ 0.005). Conclusion Higher nursing complexity and frailty are strongly associated with prolonged LOS in HF patients. Identifying distinct profiles of complexity and frailty can guide tailored interventions to improve outcomes and optimize resource use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".