Comorbidity-related risk factors for acute respiratory distress syndrome in sepsis patients: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Acute respiratory distress syndrome (ARDS) presents a significant challenge in the management of sepsis, with various comorbidities potentially influencing its development. Understanding the impact of these comorbidities is crucial for improving patient outcomes. OBJECTIVES: This meta-analysis was conducted to investigate the relationship between various comorbidities and the development of ARDS in patients with sepsis, with the aim of improving understanding and management of this condition. MATERIAL AND METHODS: The study included adult sepsis patients from 8 studies, totaling 16,964 participants. Risk of bias was assessed using the Newcastle-Ottawa scale (NOS), and the data analysis was performed and reported as pooled odds ratios (ORs) computed using a random-effects model. Heterogeneity and publication bias were assessed using the I2 statistic and Doi plots with the Luis Furuya-Kanamori (LFK) index, respectively. RESULTS: Chronic obstructive pulmonary disease was significantly associated with an increased risk of ARDS (OR: 1.43, 95% confidence interval (95% CI): 1.02-2.01). Other comorbidities showed no significant associations: diabetes mellitus (DM) (OR: 0.88, 95% CI: 0.69-1.11), hypertension (HTN) (OR: 0.86, 95% CI: 0.56 to 1.34), coronary artery disease (CAD) (OR: 0.95, 95% CI: 0.86-1.06), congestive heart failure (CHF) (OR: 1.08, 95% CI: 0.61 to 1.90), chronic kidney disease (CKD) (OR: 0.89, 95% CI: 0.65-1.22), chronic liver disease (CLD) (OR: 1.13, 95% CI: 0.61-2.09), and cancer (OR: 0.90, 95% CI: 0.59-1.35). Additional analyses indicated moderate-to-high heterogeneity and some evidence of publication bias. CONCLUSION: Chronic obstructive pulmonary disease is a notable risk factor for ARDS in sepsis patients, suggesting the need for enhanced surveillance and management in this group. Further research is necessary to understand the mechanisms and explore other potential ARDS risk factors in sepsis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".