# 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.

## Screening

We screened in sources based on their abstracts that met these criteria:
- **IVIG Therapy**: Does this study involve patients treated with intravenous immunoglobulin (IVIG) therapy?
- **Biomarker-Response Relationship**: Does this study evaluate or assess the relationship between biomarkers and IVIG treatment response?
- **Biomarker Measurement**: Does this study measure or assess clinical biomarkers (laboratory, imaging, or clinical parameters)?
- **Response Criteria**: Does this study have clearly defined response criteria or outcome measures for IVIG therapy?
- **Study Type**: Is this an original research study (randomized controlled trial, cohort study, case-control study, cross-sectional study) or a systematic review/meta-analysis?
- **Human Subjects**: Does this study involve human subjects (not exclusively animal or in vitro studies)?
- **Baseline Biomarker Assessment**: Are biomarkers measured at baseline (before or at initiation of IVIG treatment) rather than only after treatment completion?
- **Sample Size**: Does this study include 10 or more patients (not a small case report or case series with fewer than 10 patients)?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

## Data extraction

We asked a large language model to extract each data column below from each paper:
- **Medical Condition**: Extract the specific medical condition or indication for which IVIG therapy was administered (e.g., Guillain-Barré syndrome, Kawasaki disease, etc.).
- **Biomarker Details**: Extract all clinical biomarkers tested for predicting IVIG response.
- **IVIG Response Definition**: Extract how IVIG treatment response was defined and measured.
- **Biomarker Performance**: Extract the predictive performance of each biomarker for IVIG response.
- **IVIG Protocol**: Extract details of the IVIG treatment protocol used.
- **Study Population**: Extract characteristics of the study population relevant to biomarker performance.
- **Clinical Outcomes**: Extract the actual clinical outcomes observed in responders vs non-responders to IVIG.

## 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 (206 responders, 23 non-responders) | 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 (279 responders, 41 resistant) | 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 (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 |
| 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 |

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.

Non-responders to IVIG consistently demonstrated worse clinical outcomes. Coronary artery abnormalities occurred at substantially higher rates in non-responders: 71% versus 5% in Sano et al., 38.5% versus 1.4% in Fukunishi et al.. Response rates to initial IVIG treatment ranged from 84-87.5% across studies.

### Autoimmune Neurological Disorders

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

- In Guillain-Barré syndrome, Maddur et al. investigated regulatory T cell (Treg) frequency as a post-treatment biomarker.
- For chronic inflammatory demyelinating polyneuropathy (CIDP), Mausberg et al. identified natural killer (NK) cell markers measured 24 hours post-IVIG initiation as predictive.
- In myasthenia gravis, Irgens et al. used the QMG Score for Disease Severity as both outcome measure and potential severity stratification tool.

### Other Autoimmune Conditions

In systemic lupus erythematosus, Levy et al. examined autoantibodies and complement levels as potential predictors. For dermatomyositis, Charles-Schoeman et al. examined clinical disease activity scores and myositis-associated autoantibodies.

## Synthesis

The heterogeneity in predictive biomarkers across conditions reflects fundamental differences in disease pathophysiology, IVIG mechanisms of action, and optimal measurement timing rather than inconsistent or contradictory findings.

**Disease-Specific Biomarker Patterns**
For Kawasaki disease, acute inflammatory markers (CRP, liver enzymes, bilirubin) consistently predicted resistance when measured pre-treatment. Neurological autoimmune conditions demonstrated entirely different biomarker profiles.

**Timing of Biomarker Measurement**
The optimal measurement timing varied systematically by condition and biomarker type.

**Composite Prediction Scores**
Additionally, two Kawasaki studies developed composite prediction scores.

**Clinical Outcome Implications**
The clinical consequences of IVIG non-response differed markedly by condition, with higher risks of coronary artery abnormalities in Kawasaki disease non-responders.

**Generalizability Across Populations**
Disease severity emerged as a crucial modifier of IVIG response across conditions. None of the studies examined whether different IVIG doses might alter biomarker predictive value.

**Methodological Considerations**
Most Kawasaki studies used multivariate analysis to identify independent predictors, but none reported correlation matrices between candidate biomarkers.

## References

- M. Maddur, Emmanuel Stephen-Victor, et al. (2017). Regulatory T cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to intravenous immunoglobulin therapy. Journal of Neuroinflammation
- T. Sano, S. Kurotobi, et al. (2006). Prediction of non-responsiveness to standard high-dose gamma-globulin therapy in patients with acute Kawasaki disease before starting initial treatment. European Journal of Pediatrics
- Anne K. Mausberg, M. Heininger, et al. (2020). NK cell markers predict the efficacy of IV immunoglobulins in CIDP. Neurology: Neuroimmunology & Neuroinflammation
- Y. Levy, Y. Sherer, et al. (1999). A study of 20 SLE patients with intravenous immunoglobulin clinical and serologic response. Lupus
- K. Egami, H. Muta, et al. (2006). Prediction of resistance to intravenous immunoglobulin treatment in patients with Kawasaki disease. Jornal de Pediatria
- J. Abe, Ryota Ebata, et al. (2008). Elevated granulocyte colony-stimulating factor levels predict treatment failure in patients with Kawasaki disease. Journal of Allergy and Clinical Immunology
- A. Irgens, T. Dammen, et al. (2012). Thought Field Therapy (TFT) as a treatment for anxiety symptoms: a randomized controlled trial. Explore
- Mariko Fukunishi, Makiko Kikkawa, et al. (2000). Prediction of non-responsiveness to intravenous high-dose gamma-globulin therapy in patients with Kawasaki disease at onset. Jornal de Pediatria
- C. Charles-Schoeman, J. Schessl, et al. (2025). Predictors of response to intravenous immunoglobulin in patients with dermatomyositis: the ProDERM study. Rheumatology
- J. Hwang, Kyung-Yil Lee, et al. (2010). Assessment of intravenous immunoglobulin non-responders in Kawasaki disease. Archives of Disease in Childhood