<p>Greater eosinophil counts at first COPD hospitalization are associated with more readmissions and fewer deaths</p>
Bibliographic record
Abstract
PURPOSE: The impacts of high blood eosinophil count (HBEC) at admission for COPD exacerbation on posthospitalization outcomes are still unclear. Previous studies have focused on its associations with first readmission rates; yet, its impacts on longitudinal outcomes such as subsequent readmissions still have to be explored. The main objective of this study is to investigate outcomes associated with HBEC following a first hospitalization for COPD exacerbation. PATIENTS AND METHODS: This is an observational cohort study design. We retrospectively analyzed data of patients with a first hospitalization within 5 years for COPD exacerbation between April 2006 and March 2013. Patients were stratified into the HBEC group if the blood eosinophil count at admission was ≥200 cells/µL and/or ≥2% of the total white blood cells. With information on exact dates of subsequent hospitalizations and death, we modeled readmissions and death as states in a multi-state Markov model and estimated transition probabilities to the next states. Sensitivity analyses were performed by varying thresholds for the definition of HBEC (≥300 cells/µL and/or ≥3%). RESULTS: A total of 479 patients were included, of which 173 had HBEC. The transition probabilities for a first readmission was 74% (95% CI, 66%-83%) for patients with HBEC vs 70% (95% CI, 63%-77%) for patients with normal blood eosinophil count (NBEC). The transition probabilities for a second readmission were 91% (95% CI, 84%-100%) for HBEC patients in contrast with 83% (95% CI, 74%-92%) for NBEC patients. Meanwhile, transition probability for death was lower in patients with HBEC. The differences enlarged in sensitivity analyses with higher cutoff. CONCLUSION: Greater blood eosinophil cell counts during a first hospitalization for COPD predict increased susceptibility to up to two readmissions. These patients may however have a lower risk of death.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".