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PB2039: INFLAMMATORY MARKERS IN HEMOPHAGOCYTIC LYMPHOHISTIOCYTOSIS - A SINGLE CENTRE STUDY

2023· article· en· W4386086960 on OpenAlexaff
Mariam Goubran, Caroline Spaner, Amanda M. Li, Adi Zoref‐Lorenz, Michael B. Jordan, Luke Y. C. Chen, Audi Setiadi

Bibliographic record

VenueHemaSphere · 2023
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineHemophagocytic lymphohistiocytosisCytokine stormImmunologyMacrophage activation syndromePathologicalInterleukin 6CytokineDiseaseInternal medicineCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Topic: 12. Bone marrow failure syndromes incl. PNH - Clinical Background: Hemophagocytic lymphohistiocytosis (HLH) is a syndrome of pathological immune activation that can be difficult to distinguish from other cytokine storm syndromes such as Adult-Onset Still’s disease (AOSD) and COVID-19 cytokine storm (CCS). The HLH-2004 criteria and HScore are the best available diagnostic criteria but have important limitations. Many of the tests included in the current HLH-2004 criteria, such as flow cytometry for NK cell cytotoxicity and cytokine analysis, are only available in specialized centers, and while useful in pediatric HLH, are less so in adult HLH which is characterized by hyperinflammation. Inflammatory markers such as ferritin, are not specific for HLH and can be seen in several hyperinflammatory syndromes. We sought to examine the clinical utility of a simply, readily available inflammatory marker, C-reactive protein CRP) in combination with sIL2r levels in distinguishing HLH from two similar cytokine storm syndromes, AOSD and CCS. Aims: Our aim is to analyze patterns of elevation in CRP and sIL2r to help clinicians in the differential diagnosis of HLH, AOSD, and CCS. Methods: A retrospective chart review was conducted for 61 patients with secondary HLH, 10 patients with AOSD, and 13 patients with CCS. Demographic data as well as inflammatory biomarkers including CRP and sIL2r levels were collected if drawn within 72 hours of the acute episode that led to diagnosis, and prior to treatment. The Kruskal-Wallis test was used to compare CRP in the HLH, AOSD, and CCS groups, as well as to compare CRP in HLH subgroups by underlying trigger (infection, malignancy, autoimmune, and idiopathic). The Kruskal-Wallis test was also used to measure and compare sIL2r levels in the HLH group compared to AOSD, as well as by HLH subtype. Results: C-reactive protein is significantly lower in secondary HLH (Mdn = 76.4) compared to AOSD (Mdn = 114.5, p = 0.039) and CCS (Mdn = 121.0, p = 0.003). When analyzed by subtype, CRP levels in malignancy-associated HLH (MAHS) were not significantly different than AOSD or CCS, while CRP levels were significantly lower in the non-MAHS groups (Mdn = 58.9, p = 0.01) compared to AOSD and CCS (p <0.001), particularly when compared to infection-associated HLH (IAHS) (Mdn = 32.7, p = 0.009). Soluble IL2 receptor levels were significantly higher in the HLH group (Mdn = 7436.0, p = 0.03) compared to AOSD (Mdn = 2252.0). Summary/Conclusion: C-reactive protein is significantly lower in HLH compared to AOSD and CCS, specifically in non-MAHS, and may be a useful and easily accessible biomarker aiding in the differential diagnosis of HLH. Soluble IL2 receptor levels are a useful biomarker in distinguishing AOSD from HLH, as it is not as significantly elevated in AOSD. Keywords: Macrophage, Inflammation, IL-6, Cytokine

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.285
Teacher spread0.260 · 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 routes1
Has abstractyes

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