cytokine-storm-analysis-agent

<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA

-->

---
name: 'cytokine-storm-analysis-agent'
description: 'AI-powered cytokine release syndrome (CRS) and cytokine storm analysis for prediction, monitoring, and management in immunotherapy and infectious disease.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---


# Cytokine Storm Analysis Agent

The **Cytokine Storm Analysis Agent** provides comprehensive AI-driven analysis of cytokine release syndrome (CRS) and hyperinflammatory states. It integrates cytokine profiling, clinical parameters, and immunological markers for early prediction, severity grading, and treatment guidance in CAR-T therapy, sepsis, and viral infections.

## When to Use This Skill

* When monitoring CAR-T patients for cytokine release syndrome risk.
* To predict CRS severity and timing post-immunotherapy.
* For analyzing cytokine panels in sepsis and viral infections (COVID-19).
* When guiding tocilizumab/siltuximab anti-IL-6 therapy decisions.
* To distinguish CRS from ICANS, HLH, and other inflammatory syndromes.

## Core Capabilities

1. **CRS Risk Prediction**: ML models predict CRS development and severity from baseline factors (tumor burden, disease type, CAR-T product).

2. **Real-Time Monitoring**: Track cytokine dynamics (IL-6, IFN-γ, IL-10, ferritin) with early warning alerts.

3. **Severity Grading**: Automated ASTCT CRS grading using clinical parameters and biomarkers.

4. **Differential Diagnosis**: Distinguish CRS from HLH/MAS, ICANS, infection, and tumor lysis syndrome.

5. **Treatment Guidance**: AI-driven recommendations for tocilizumab, corticosteroids, and supportive care.

6. **Outcome Prediction**: Model response to anti-cytokine therapy and overall outcomes.

## Cytokine Panel Analysis

| Cytokine | Role in CRS | Kinetics | Therapeutic Target |
|----------|-------------|----------|-------------------|
| IL-6 | Central mediator | Early peak | Tocilizumab, Siltuximab |
| IFN-γ | T-cell activation | Early | Emapalumab |
| IL-1β | Inflammasome | Early | Anakinra |
| IL-10 | Regulatory | Variable | - |
| TNF-α | Pro-inflammatory | Early | Infliximab (caution) |
| IL-2 | T-cell expansion | Early | - |
| GM-CSF | Myeloid activation | Sustained | Lenzilumab |

## ASTCT CRS Grading (Automated)

| Grade | Fever | Hypotension | Hypoxia |
|-------|-------|-------------|---------|
| 1 | ≥38°C | None | None |
| 2 | ≥38°C | Responsive to fluids | Low-flow O2 |
| 3 | ≥38°C | One vasopressor | High-flow O2 |
| 4 | ≥38°C | Multiple vasopressors | Ventilation |

## Workflow

1. **Input**: Cytokine levels, vital signs, laboratory values, treatment history.

2. **Risk Assessment**: Baseline CRS risk stratification pre-therapy.

3. **Monitoring**: Real-time cytokine tracking with trend analysis.

4. **Grading**: Automated CRS grade assignment per ASTCT criteria.

5. **Differential**: Rule out mimics (infection, HLH, ICANS).

6. **Treatment**: Generate management recommendations.

7. **Output**: CRS risk score, grade, differential diagnosis, treatment plan.

## Example Usage

**User**: "Monitor this CAR-T patient's cytokine levels and predict CRS severity."

**Agent Action**:
```bash
python3 Skills/Immunology_Vaccines/Cytokine_Storm_Analysis_Agent/crs_analyzer.py \
    --patient_data demographics.json \
    --cytokines cytokine_panel.csv \
    --vitals vital_signs.csv \
    --labs laboratory_values.csv \
    --cart_product tisagenlecleucel \
    --day_post_infusion 5 \
    --model crs_predictor_v3 \
    --output crs_report.json
```

## AI/ML Models

**CRS Risk Prediction**:
- Features: tumor burden (LDH), lymphodepletion intensity, CAR-T dose, disease type
- Model: Gradient boosting with SHAP interpretability
- Performance: AUC 0.82-0.88 for severe CRS

**Severity Trajectory**:
- Time-series modeling of cytokine dynamics
- LSTM networks for temporal patterns
- Early warning 24-48 hours before clinical deterioration

**Treatment Response**:
- Tocilizumab response prediction
- Corticosteroid escalation timing
- ICU admission risk

## Differential Diagnosis Decision Tree

```
Fever + Elevated Cytokines
          |
    CAR-T context?
    /           \
  Yes            No
   |              |
Hypotension?   Infection workup
   |              |
  CRS          Sepsis vs viral
   |
Neuro symptoms?
   |
  ICANS vs CRS
   |
Ferritin >10,000?
   |
  HLH/MAS evaluation
```

## Clinical Decision Support

**Tocilizumab Indication**:
- Grade 2+ CRS
- Rapidly rising cytokines
- High-risk baseline features

**Corticosteroid Indication**:
- Tocilizumab-refractory CRS
- ICANS any grade
- Grade 3+ CRS

## Prerequisites

* Python 3.10+
* scikit-learn, XGBoost for ML
* Time-series analysis libraries
* FHIR client for EHR integration

## Related Skills

* CART_Design_Optimizer_Agent - For CAR-T design
* TCell_Exhaustion_Analysis_Agent - For T-cell function
* Clinical_NLP - For extracting symptoms from notes

## Special Populations

1. **Pediatric**: Different baseline cytokine ranges
2. **Post-COVID**: Altered inflammatory responses
3. **Bridging Therapy**: Impact on CRS risk
4. **Concurrent Infection**: Confounding cytokine elevation

## Author

AI Group - Biomedical AI Platform


<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->