Inflammatory Biomarkers Improve Clinical Prediction of Mortality in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
RATIONALE: Accurate prediction of mortality helps select patients for interventions aimed at improving outcome. OBJECTIVES: Because chronic obstructive pulmonary disease is characterized by low-grade systemic inflammation, we hypothesized that addition of inflammatory biomarkers to established predictive factors will improve accuracy. METHODS: A total of 1,843 patients enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints study were followed for 3 years. Kaplan-Meier curves, log-rank analysis, and Cox proportional hazards analyses determined the predictive value for mortality of clinical variables, while C statistics assessed the added discriminative power offered by addition of biomarkers. MEASUREMENTS AND MAIN RESULTS: At recruitment we measured anthropometrics, spirometry, 6-minute walk distance, dyspnea, BODE index, history of hospitalization, comorbidities, and computed tomography scan emphysema. White blood cell and neutrophil counts, serum or plasma levels of fibrinogen, chemokine ligand 18, surfactant protein D, C-reactive protein, Clara cell secretory protein-16, IL-6 and -8, and tumor necrosis factor-α were determined at recruitment and subsequent visits. A total of 168 of the 1,843 patients (9.1%) died. Nonsurvivors were older and had more severe airflow limitation, increased dyspnea, higher BODE score, more emphysema, and higher rates of comorbidities and history of hospitalizations. The best predictive model for mortality using clinical variables included age, BODE, and hospitalization history (C statistic of 0.686; P < 0.001). One single biomarker (IL-6) significantly improved the C statistic to 0.708, but this was further improved to 0.726 (P = 0.003) by the addition of all biomarkers. CONCLUSIONS: The addition of a panel of selected biomarkers improves the ability of established clinical variables to predict mortality in chronic obstructive pulmonary disease. Clinical trial registered with www.clinicaltrials.gov (NCT00292552).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".