Abstract 14717: Identifying Predictors of Red Cell Distribution Width at Admission and Changes in RDW During Heart Failure Hospitalization
Bibliographic record
Abstract
Introduction: The causes of risk-associated elevations in red cell distribution width (RDW) are unknown, but RDW strongly predicts adverse outcomes in heart failure (HF) patients. Initial RDW level and the change in RDW (ΔRDW) were previously shown to predict hospital length of stay, 30-day readmission, and 30-day mortality. Hypothesis: Risk factors and diagnoses predict initial RDW and in-hospital ΔRDW of HF patients. Methods: Patients (N=6,414) were studied if they had an inpatient stay for the primary diagnosis of HF during 2004-2013, had RDW measured within 24 hours after admission, and had a second RDW tested within 24 hours prior to discharge. ΔRDW was calculated as the difference between the two RDW measurements. All patients were aged 65 years or older and were discharged alive and not to hospice. Results: In linear regression, predictors of initial RDW included age, being ever diagnosed with 15 comorbidities (Table), and diagnosis in the prior month with 8 comorbidities. Those 24 factors explained 10.5% of the variation in RDW (model F-statistic= 33.3, p<0.001). In contrast, predictors of ΔRDW were sex, length of stay, initial red blood cell count, mean corpuscular volume, RDW, mean corpuscular hemoglobin concentration, sodium, calcium, potassium, medications administered in-hospital (e.g., ACE-I, inotropes, and vasopressors), and diagnosis ever or within the last month of various comorbidities (Table). Those factors explained 13.9% of the variation in ΔRDW (model F-statistic= 35.9, p<0.001). Conclusions: Predictors of initial RDW and ΔRDW in HF patients clustered among diagnoses of cardiovascular, pulmonary, renal, and psychological diseases, and (as expected) hematological/bleeding conditions. These findings may aid in determining the cause of RDW elevations and in personalizing medical care using common, inexpensive, electronic laboratory data. With most variance in RDW and ΔRDW still unexplained, discovery of other predictors is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.001 |
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".