Comorbidity and Inflammatory Markers May Contribute to Predict Mortality of High-Risk Patients With Chronic Obstructive Pulmonary Disease Exacerbation
Bibliographic record
Abstract
BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (COPD) causes not only an accelerated disease progression, but also an increased mortality rate. The purpose of this study was to analyze the factors associated with clinical features, comorbidities and mortality in patients at high risk for acute COPD exacerbation who had been hospitalized at least once in a year. METHODS: The study enrolled 606 patients who had been diagnosed with and were being treated for COPD at university affiliated hospital. Among them, there were 61 patients at high risk for acute exacerbation of COPD who had been hospitalized at least once in a year. A retrospective analysis was conducted to examine the factors affecting mortality. The analysis divided the patients into non-survivor and survivor groups, and reviewed their medical records for clinical aspects, comorbidities, pulmonary function tests and blood tests. RESULTS: In the high-risk group, the number of comorbidities at diagnosis (P = 0.020) and the Charlson comorbidity index value (P = 0.018) were higher in the non-survivor group than in the survivor group. During hospitalization, the non-survivor group had a significantly higher neutrophil (%) and a significantly lower lymphocyte (%) in complete blood count. Under stable conditions, the high-sensitivity C-reactive protein (hsCRP) concentration in blood plasma and neutrophil (%) were significantly higher (P = 0.025 and P = 0.036), while the lymphocyte (%) was significantly lower (P = 0.005) in the non-survivor group. A pulmonary function test revealed no statistically significant differences between the two groups. CONCLUSION: The number of comorbidities, neutrophil (%), lymphocyte (%) in complete blood cell (CBC) and hsCRP in blood plasma concentration among the groups at high risk for COPD exacerbation are associated with increased mortality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".