Predictive performance of risk scores for the outcome of acute exacerbation of COPD - A retrospective study
Bibliographic record
Abstract
Introduction: Different scoring systems are available for risk stratification in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study aimed to validate established risk scores for the prediction of in-hospital death in AECOPD on a Swiss cohort and to evaluate predictive accuracy of a combination of their parameters. Methods: BAP65 Score, National Early Warning Score (NEWS), and modified versions of the Ottawa COPD Risk Scale (OTTAWAm) and DECAF Score (DECAFm) were calculated with admission data of patients hospitalized for AECOPD in a Swiss hospital in 2022/2023. Predictive power for in-hospital death was compared with receiver operating characteristic (ROC) curves and the respective area under the curve (AUC). A new score, AECOPD-COMBI, that combines parameters used in the four scores, was validated on the same cohort. Results: 314 patients (mean age 73y (range 48-94), 47% female) were included. In-hospital mortality was 2.2%. Patients who died had significantly higher BAP65 (mean difference 0.9, p<0.001) and DECAFm (mean difference 0.9, p=0.004) scores. Predictive accuracy is summarized in Fig.1. erj;66/suppl_69/PA4679/F1 F1 F1 Conclusion: Established scores, especially BAP65, performed well in the prediction of in-hospital death on this recent Swiss cohort. The new combined model, AECOPD-COMBI, shows potential to improve prediction but requires further refinement and validation on larger cohorts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".