Risk factors of pulmonary arterial hypertension in patients with systemic lupus erythematosus: A meta-analysis
Bibliographic record
Abstract
Objective To determine the risk factors of pulmonary arterial hypertension (PAH) related to systemic lupus erythematosus (SLE) through systematic reviews and meta-analyses. Methods We undertook electronic search strategies using Medline via PubMed, Embase, Web of Science, and Cochrane Library up to April 11, 2023. Study selection and data extraction were performed by 2 authors independently. We made risk of bias judgments based on the Newcastle-Ottawa Scale (NOS). Pooled risk ratios (RRs) and 95% confidence intervals (CIs) were calculated to estimate the overall effect sizes of potential risk factors for PAH in SLE patients. Univariate and multivariate meta-regression models were used to assess the independent effects of each risk factor on PAH. Sensitivity analyses were also conducted to explore potential sources of heterogeneity. Results A total of 19 articles were included in this meta-analysis, and the results showed that gender (female) [RR = 1.04, 95% CI (1.02, 1.06), p = .0001], interstitial lung disease [RR = 4.36, 95% CI (2.42, 7.85), p = .0001], alopecia [RR = 1.39, 95% CI (1.06, 1.83), p = .017], Raynaud’s phenomenon [RR = 1.83, 95% CI (1.41, 2.37), p = .0001], systemic hypertension [RR = 1.30, 95% CI (1.07, 1.58), p = .007], serositis [RR = 2.29, 95% CI (1.89, 2.77), p = .0001], pericardial effusion [RR = 3.33, 95% CI (2.20, 5.05), p = .0001], anti-RNP [RR = 1.86, 95% CI (1.19, 2.91), p = .006], anti-SSA [RR = 1.28, 95% CI (1.01, 1.62), p = .041], anti-SSB [RR = 1.38, 95% CI (1.19, 1.60), p = .0001], anti-U1RNP [RR = 1.58, 95% CI (1.07, 2.34), p = .023], thrombocytopenia [RR = 1.38, 95% CI (1.14, 1.68), p = .001], and current smokers [RR = 2.20, 95% CI (1.19, 4.06), p = .012] were all risk factors for PAH related to SLE. Conclusion PAH is a serious complication of SLE. Since prognosis of SLE patients after the occurrence of PAH is poor, routine examination should be conducted for SLE patients with PAH risk factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| 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".