The impact of COVID-19 infection on idiopathic pulmonary fibrosis mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
COVID-19 has a negative impact on the survival of respiratory patients, especially those with interstitial lung disease. This review aims to better understand the effect of COVID-19 on patients with idiopathic pulmonary fibrosis (IPF). A systematic search of MEDLINE, PubMed, Embase, and Scopus performed from December 2019 up to July 2024 identified relevant studies. Eligibility criteria included English language, sample size ≥10 patients, COVID-19 infection, and outcome measures. Two independent reviewers assessed studies using the Newcastle-Ottawa Scale for bias and extracted data. Meta-analysis employed a random-effects model, and the Grading of Recommendations Assessment, Development and Evaluation assessed evidence quality. Outcomes considered were hospitalization, intensive care unit admission, and mortality. Of the 1541 initially identified articles, 6 high-quality studies were included. Meta-analysis revealed a 34% mortality rate [95% confidence interval (CI): 21-48%], 36% hospitalization rate (95% CI: 10-75%), and 31% intensive care unit admission rate (95% CI: 7-71%) among IPF patients with COVID-19. The certainty of evidence was low or very low due to publication bias and heterogeneity. This study underscores the elevated risk of hospitalization and death in IPF patients with COVID-19, emphasizing the vulnerability of this population. Prompt and tailored care is crucial to mitigate the impact of COVID-19 on IPF patients, necessitating proactive measures, vaccination, and comprehensive management.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".