# 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.

- **Medical Condition**:
  - Extract the specific medical condition or indication for which IVIG therapy was administered (e.g., Guillain-Barré syndrome, Kawasaki disease, myasthenia gravis, primary immunodeficiency, autoimmune neurological disorders). Include disease subtype or severity classification if specified.

- **Biomarker Details**:
  - Extract all clinical biomarkers tested for predicting IVIG response.

- **IVIG Response Definition**:
  - Extract how IVIG treatment response was defined and measured, including primary response criteria similar to fever resolution, clinical improvement scores, and functional outcomes.

- **Biomarker Performance**:
  - Extract the predictive performance of each biomarker for IVIG response, including statistical associations with response, sensitivity, specificity, and correlation coefficients.

- **IVIG Protocol**:
  - Extract details of the IVIG treatment protocol used, including IVIG dose, administration schedule, and duration.

- **Study Population**:
  - Extract characteristics of the study population relevant to biomarker performance: demographics, sample size, disease severity, and setting.

- **Clinical Outcomes**:
  - Extract observed clinical outcomes in responders vs non-responders, including response rates and specific measures.

## 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 | Medical Condition | Sample Size | IVIG Dose | Response Definition |
|-------|-------------------|-------------|-----------|--------------------|
| Y. Levy et al., 1999 | Systemic lupus erythematosus | 20 | 2 g/kg monthly over 5 days | Binary; clinical improvement with decreased SLAM score |
| A. Irgens et al., 2012 | 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 | Dermatomyositis | 95 | 2.0 g/kg every 4 weeks | Continuous; total improvement score (TIS) |
| J. Hwang et al., 2010 | Kawasaki disease | 229 (206 responders, 23 non-responders) | Not specified | Binary; responder vs non-responder |
| M. Maddur et al., 2017 | Guillain-Barré syndrome | 10 | 0.4 g/kg for 3-5 consecutive days | Continuous; MRC and modified Rankin scores |
| T. Sano et al., 2006 | Acute Kawasaki disease | 112 | 2 g/kg within 2 days of onset | Binary; responsive vs non-responsive |
| A. K. Mausberg et al., 2020 | 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 | Kawasaki disease | 320 (279 responders, 41 resistant) | 2 g/kg within 9 days of illness | Binary; resistance vs responder groups |
| J. Abe et al., 2008 | Kawasaki disease | Not specified | High-dose (not specified) | Binary; responsive vs nonresponsive |
| M. Fukunishi et al., 2000 | Kawasaki disease | 82 (69 responsive, 13 non-responsive) | High-dose (not specified) | Binary; defervescence within 5 days |

The studies represented diverse autoimmune conditions, with Kawasaki disease being the most extensively studied (5/10 studies). Sample sizes ranged from 10 to 320 patients. Most studies used standard high-dose IVIG protocols of 2 g/kg, though administration schedules varied. Response definitions were predominantly binary (responder vs non-responder), though some studies used continuous outcome measures.

### 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 |
| M. 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%) |
| J. Abe et al., 2008 | Polycythemia rubra vera 1, G-CSF | Not specified | Higher in nonresponders | Not assessed |

C-reactive protein emerged as the most consistently identified pre-treatment biomarker, appearing in four of five Kawasaki studies. However, optimal CRP cut-off values varied considerably across studies: ≥7.0 mg in Sano et al., ≥8 mg/dL in Egami et al., and >10 mg/dL in Fukunishi et al.. Liver enzymes (AST, ALT) and total bilirubin also demonstrated predictive value in multiple studies.

### Autoimmune Neurological Disorders

Three studies examined biomarkers in distinct neurological conditions, revealing markedly different predictive approaches. In Guillain-Barré syndrome, Maddur et al. correlated regulatory T cell (Treg) frequency with clinical recovery, suggesting Treg frequency represents a potential immunological biomarker. In CIDP, Mausberg et al. identified NK cell markers measured post-IVIG initiation as predictive.

### Other Autoimmune Conditions

In systemic lupus erythematosus, Levy et al. examined autoantibodies and complement levels. In dermatomyositis, Charles-Schoeman et al. identified clinical disease activity scores as predictors. These studies highlight the diversity of biomarkers across conditions and their relation to disease mechanisms.

## Synthesis

The heterogeneity in predictive biomarkers reflects differences in disease pathophysiology, IVIG mechanisms of action, and optimal measurement timing. Composite prediction scores developed may aid in identifying at-risk patients for non-response to IVIG therapy. The clinical consequences of non-response varied markedly across conditions, underscoring the need for timely identification of biomarkers and tailored treatment strategies.