Acute Pulmonary Exacerbation Phenotypes in Patients with Cystic Fibrosis
Bibliographic record
Abstract
Abstract Rationale The etiology of cystic fibrosis (CF) pulmonary exacerbations (PEx) is likely multifactorial with viral, bacterial, and non-infectious pathways contributing. Objectives To determine whether viral infection status and CRP (C-reactive protein) can classify subphenotypes of PEx that differ in outcomes and biomarker profiles. Methods Patients were recruited at time of admission for a PEx. Nasal swabs and sputum samples were collected and processed using the respiratory panel of the FilmArray multiplex polymerase chain reaction (PCR). Serum and plasma biomarkers were measured. PEx were classified using serum CRP and viral PCR: “pauci-inflammatory” if CRP < 5 mg/L, “non-viral with systemic inflammation” if CRP ⩾ 5 mg/L and no viral infection detected by PCR and “viral with systemic inflammation” if CRP ⩾ 5 mg/L and viral infection detected by PCR. Results Discovery cohort (n = 59) subphenotype frequencies were 1) pauci-inflammatory (37%); 2) non-viral with systemic inflammation (41%); and 3) viral with systemic inflammation (22%). Immunoglobulin G, immunoglobulin M, interleukin-10, interleukin-13, serum calprotectin, and CRP levels differed across phenotypes. Reduction from baseline in forced expiratory volume in 1 second as percent predicted (FEV1pp) at onset of exacerbation differed between non-viral with systemic inflammation and viral with systemic inflammation (−6.73 ± 1.78 vs. −13.5 ± 2.32%; P = 0.025). Non-viral with systemic inflammation PEx had a trend toward longer duration of intravenous antibiotics versus pauci-inflammation (18.1 ± 1.17 vs. 14.8 ± 1.19 days, P = 0.057). There were no differences in percent with lung function recovery to <10% of baseline FEV1pp. Similar results were seen in local and external validation cohorts comparing a pauci-inflammatory to viral/non-viral inflammatory exacerbation phenotypes. Conclusions Subphenotypes of CF PEx exist with differences in biomarker profile, clinical presentation, and outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".