Elicit: Predictive Biomarkers for IVIG Response

Predictive Biomarkers for IVIG Response

Clinical biomarkers predicting response to IVIG therapy

Predictive biomarkers for IVIG response are disease-specific: pre-treatment inflammatory markers (C-reactive protein, liver enzymes, bilirubin) predict resistance in Kawasaki disease, while post-treatment cellular immune markers (NK cells, regulatory T cells) predict response in chronic neurological autoimmune disorders.

Abstract

Clinical biomarkers predicting IVIG response differ substantially by disease condition and reflect distinct pathophysiological mechanisms. In Kawasaki disease, pre-treatment inflammatory markers consistently predicted resistance: C-reactive protein (cut-offs 7-10 mg/dL), liver transaminases (AST ≥200 IU/L, ALT ≥80 IU/L), and total bilirubin (≥0.9 mg) demonstrated significant associations with non-response in multivariate analyses. Composite prediction scores combining multiple markers achieved 76-78% sensitivity and specificity. Post-treatment inflammatory markers measured 24 hours after infusion showed superior performance, with total leucocyte count >13.1×10⁹/L achieving 91% sensitivity and 89% specificity. In contrast, autoimmune neurological disorders demonstrated no predictive value for inflammatory markers. Instead, post-treatment cellular immune biomarkers proved informative: NK cell markers (CD56bright, KLRB1, KLRK1) measured 24 hours after IVIG initiation significantly discriminated CIDP responders from non-responders (p=0.007-0.027), while regulatory T cell frequency correlated with clinical recovery in Guillain-Barré syndrome. In dermatomyositis, higher baseline cutaneous disease activity predicted better response, whereas higher pulmonary activity predicted lower response. These divergent biomarker profiles reflect condition-specific IVIG mechanisms: acute inflammation suppression in Kawasaki disease versus immune cell modulation in chronic neurological disorders. Clinical consequences of non-response varied substantially, with Kawasaki disease non-responders experiencing coronary artery abnormalities at rates of 38-71% versus 1-5% in responders.

Methods

We analyzed 10 sources from an initial pool of 200, using 8 screening criteria. Each paper was reviewed for 7 key aspects that mattered most to the research question.

Results

Characteristics of Included Studies

Ten studies investigating clinical biomarkers for predicting IVIG response were identified, spanning multiple autoimmune and inflammatory conditions. Five studies (50%) had full text available for detailed review.

Study Full text retrieved? Medical Condition Sample Size IVIG Dose Response Definition
Y. Levy et al., 1999 No Systemic lupus erythematosus 20 2 g/kg monthly over 5 days Binary; clinical improvement with decreased SLAM score
A. Irgens et al., 2012 No Myasthenia gravis with worsening weakness 51 2 g/kg divided over 2 days Continuous; change in QMG Score at day 14
C. Charles-Schoeman et al., 2025 No Dermatomyositis 95 2.0 g/kg every 4 weeks Continuous; total improvement score (TIS)
J. Hwang et al., 2010 No Kawasaki disease 229 Not specified Binary; responder vs non-responder
M. Maddur et al., 2017 Yes Guillain-Barré syndrome 10 0.4 g/kg for 3-5 consecutive days Continuous; MRC and modified Rankin scores
T. Sano et al., 2006 No Acute Kawasaki disease 112 2 g/kg within 2 days of onset Binary; responsive vs non-responsive
Anne K. Mausberg et al., 2020 Yes Chronic inflammatory demyelinating polyneuropathy 29 Not specified, treatment up to 6 months Binary; INCAT score decline ≥1 point over 6 months
K. Egami et al., 2006 No Kawasaki disease 320 2 g/kg within 9 days of illness Binary; resistance vs responder groups
J. Abe et al., 2008 No Kawasaki disease Not specified High-dose (not specified) Binary; responsive vs nonresponsive
Mariko Fukunishi et al., 2000 No Kawasaki disease 82 High-dose (not specified) Binary; defervescence within 5 days

Predictive Biomarkers by Disease Category

Kawasaki Disease

Five studies examined biomarkers predicting IVIG resistance in Kawasaki disease, demonstrating substantial heterogeneity in both biomarkers identified and their measurement timing. Pre-treatment inflammatory markers emerged as consistent predictors across multiple studies.

Study Pre-treatment Biomarkers Cut-off Values Performance Metrics Post-treatment Biomarkers
T. Sano et al., 2006 CRP, total bilirubin, AST, ALT, LDH, neutrophil count CRP ≥7.0 mg, TB ≥0.9 mg, AST ≥200 IU/L Multivariate p-values: CRP (p=0.009), TB (p<0.001), AST (p=0.002) Not assessed
K. Egami et al., 2006 Age, illness days, platelet count, ALT, CRP Age <6 months, <4 illness days, platelet ≤30×10¹⁰/L, CRP ≥8 mg/dL, ALT ≥80 IU/L Prediction score sensitivity 78%, specificity 76% Not assessed
Mariko Fukunishi et al., 2000 CRP, total bilirubin, LDH, gamma-glutamyltranspeptidase, hemoglobin CRP >10 mg/dL, LDH >590 IU/L, hemoglobin <10 g/dL Multivariate p-values: CRP (p=.006), LDH (p=.035), TB (p=.046) Not assessed
J. Hwang et al., 2010 Neutrophil differential, CRP Pre-treatment: neutrophil >51% Not specified Total leucocyte >13.1×10⁹/L (sens 91%, spec 89%), neutrophil >51% (sens 91%, spec 76%), total protein <72 g/L (sens 64%, spec 78%)
J. Abe et al., 2008 Polycythemia rubra vera 1, G-CSF measured by mRNA and protein levels Not specified Higher in nonresponders Not assessed

Autoimmune Neurological Disorders

Three studies examined biomarkers in distinct neurological conditions, revealing markedly different predictive approaches.

  1. Guillain-Barré syndrome: Maddur et al. investigated regulatory T cell (Treg) frequency as a post-treatment biomarker.
  2. Chronic inflammatory demyelinating polyneuropathy (CIDP): Mausberg et al. identified natural killer (NK) cell markers measured 24 hours post-IVIG initiation as predictive.
  3. Myasthenia gravis: Irgens et al. used the QMG Score for Disease Severity as both outcome measure.

Other Autoimmune Conditions

In systemic lupus erythematosus, Levy et al. examined autoantibodies and complement levels as potential predictors. In dermatomyositis, a study identified higher cutaneous scores as predicting better improvement.

Synthesis

The heterogeneity in predictive biomarkers across conditions reflects fundamental differences in disease pathophysiology, IVIG mechanisms of action, and optimal measurement timing.