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Record W4389232055 · doi:10.1182/blood-2023-178867

Inflammatory Markers in Hemophagocytic Lymphohistiocytosis - a Single Centre Study

2023· article· en· W4389232055 on OpenAlexaffabout
Caroline Spaner, Mariam Goubran, Sophie Stukas, Kam Shojania, Amanda M. Li, Mypinder S. Sekhon, Cheryl L. Wellington, Adi Zoref‐Lorenz, André Mattman, Erica A. Peterson, Michael B. Jordan, Luke Y. C. Chen, Audi Setiadi

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsSt. Paul's HospitalVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsHemophagocytic lymphohistiocytosisCytokine stormMedicineFerritinMacrophage activation syndromeImmunologyCytokineInflammationInternal medicinePediatricsDiseaseCoronavirus disease 2019 (COVID-19)

Abstract

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BACKGROUND Hemophagocytic lymphohistiocytosis (HLH) has significant clinical and biochemical overlap with other cytokine storm syndromes including Adult-Onset Still's disease (AOSD) and COVID-19 cytokine storm (CCS). Diagnosis of HLH is based largely on the HLH-2004 criteria which carry certain limitations. Many of the tests included in these criteria such as flow cytometry for NK cell cytotoxicity and soluble CD25 levels are only available in specialized centres. Moreover, they can take days to weeks to result, leading to delays in diagnosis. Biomarkers that are unique to HLH and more readily available are needed to ensure a timelier diagnosis of this life-threatening disease. C-reactive protein (CRP) is a sensitive marker of inflammation that is driven by IL-6 activity, a cytokine that is significantly elevated in AOSD and CCS but comparatively less so in HLH. Our study sought to determine whether lower levels of CRP, in conjunction with other patterns of inflammatory markers, could reliably distinguish HLH from AOSD and CCS. METHODS This was a single-centre, retrospective study examining adult ( n=45) and pediatric ( n=12) patients with HLH, and adult patients with AOSD ( n=11) and CCS ( n=13). Patients were enrolled from Vancouver General Hospital between January 1 st, 2000, to June 28 th, 2023. CRP and ferritin levels were collected for each patient if available, and values were included in analysis only if drawn prior to HLH-specific treatment. Ferritin levels were included to add to our understanding of different patterns of inflammation seen in similar cytokine storm syndromes. The Kruskal-Wallis test was used to compare CRP and ferritin values in total HLH cases compared to AOSD and CCS. Diagnostic performance of CRP was analyzed by performing a receiver operating characteristic (ROC) curve for CRP in HLH patients using the Youden index to identify the optimal cut-off point for CRP values. RESULTS In total, eighty-one patients were included in the study. Median CRP was significantly lower in HLH compared to AOSD (68.9 mg/L versus 168.0 mg/L, p < 0.001) and CCS (68.9 mg/L versus 121.0 mg/L, p = 0.024) (Figure 1). Median serum ferritin levels were significantly lower in CCS compared to HLH (1,386 ng/mL versus 16,722, p < 0.001) and AOSD (1,3860 ng/mL versus 12,480 ng/mL, p = 0.016). There was no significant difference in ferritin levels between HLH and AOSD. The ROC curve was performed for CRP values in total HLH cases which demonstrated an Area Under the Curve (AUC) of 0.799, indicating a fair diagnostic performance in differentiating HLH, with a sensitivity of 66.1% and specificity of 87.5% for CRP values less than 94.5 mg/L, as calculated by Youden's index. CONCLUSION CRP is significantly lower in HLH compared to AOSD and CCS, suggesting that mild to moderate elevations in CRP reflect the comparatively lower IL-6 activity underlying HLH. When used in conjunction with other inflammatory markers, especially marked hyperferritinemia, a comparatively lower CRP value helps capture the unique cytokine pattern underlying HLH, and is a useful, easily accessible biomarker than can aid in the diagnosis of HLH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.272
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes2
Has abstractyes

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