The role of C-reactive protein and ferritin in the diagnosis of HLH, adult-onset still’s disease, and COVID-19 cytokine storm
Bibliographic record
Abstract
Cytokine storm syndromes such as hemophagocytic lymphohistiocytosis (HLH), Adult-onset Still's disease (AOSD), and COVID-19 cytokine storm (CCS) are characterized by markedly elevated inflammatory cytokines. However clinical measurement of serum cytokines is not widely available. This study examined the clinical utility of C-reactive protein (CRP) and ferritin, two inexpensive and widely available inflammatory markers, for distinguishing HLH from AOSD and CCS. This single centre retrospective study included 44 secondary HLH patients, 14 AOSD patients, and 13 CCS patients. Baseline CRP and ferritin measured within 72 h of diagnosis and before administration of corticosteroids or other anti-inflammatory therapies were analyzed. The median CRP in HLH patients was lower than AOSD (71 mg/L vs. 172 mg/L, p < 0.001) and CCS (71 mg/L vs. 121 mg/L, p = 0.0095) patients. Serum ferritin levels were lower in CCS compared to HLH (1,386 µg/L vs. 29,019 µg/L, p < 0.001) and AOSD (11,359 µg/L vs. 29,019 µg/L, p = 0.035). A CRP < 130 mg/L when combined with an HScore > 136 improves the specificity of HScore alone for HLH from 85.2 to 96.3%. Adding CRP < 130 mg/L to ferritin > 15,254 µg/L increases specificity for HLH from 88.9 to 100%. This study demonstrates that median CRP is lower in HLH than in AOSD and CCS, and median ferritin is lower in CCS than in HLH or AOSD. This study demonstrates the clinical utility of these widely available inflammatory markers for distinguishing between different cytokine storm syndromes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".