Scleroderma Renal Crisis: Risk Factors for an Increasingly Rare Organ Complication
Bibliographic record
Abstract
OBJECTIVE: Scleroderma renal crisis (SRC) is a severe life-threatening manifestation in patients with systemic sclerosis (SSc). However, the knowledge about risk factors for SRC is limited. We determined here the frequency of SRC and identified risk factors for the prediction of SRC. METHODS: Based on regular followup data from the German Network for Systemic Scleroderma, we used univariate and multivariate generalized estimating equations to analyze the association between clinical variables, SSc subsets, therapy [i.e., angiotensin-converting enzyme inhibitors (ACEi), corticosteroids], and the occurrence of SRC. RESULTS: Data of 2873 patients with 10,425 visits were available for analysis with a mean number of registry visits of 3.6 ± 2.8 and a mean time of followup of 3.6 ± 3.8 years. In total, 70 patients developed SRC (70/2873, 2.4%). Of these patients, 57.1% (40/70) were diagnosed with diffuse cutaneous SSc, 31.4% (22/70) with limited cutaneous SSc, and 11.4% (8/70) with SSc-overlap syndromes. Predictive independent factors with the highest probability for SRC were positive anti-RNA polymerase antibodies (RNAP), a history of proteinuria prior to SRC onset, diminished DLCO, and a history of hypertension. Interestingly, positive antitopoisomerase autoantibodies did not predict a higher risk for SRC. Further, patients with SRC were significantly more frequently treated with ACEi and corticosteroids without being independently associated with SRC. CONCLUSION: In this cohort, SRC has become a rare complication. By far the highest risk for SRC was associated with the detection of anti-RNAP and proteinuria.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".