cytokine-storm-analysis-agent
SkillAI & modelsThe Cytokine Storm Analysis Agent provides comprehensive AI-driven analysis of cytokine release syndrome (CRS) and hyperinflammatory states.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Then ask your AI: use the cytokine-storm-analysis-agent skill
About this skill
The largest open-source medical AI skills library for OpenClaw🦞.
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/cytokine-storm-analysis-agent/SKILL.md and read by ahel’s review.
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
-
CRS Risk Prediction: ML models predict CRS development and severity from baseline factors (tumor burden, disease type, CAR-T product).
-
Real-Time Monitoring: Track cytokine dynamics (IL-6, IFN-γ, IL-10, ferritin) with early warning alerts.
-
Severity Grading: Automated ASTCT CRS grading using clinical parameters and biomarkers.
-
Differential Diagnosis: Distinguish CRS from HLH/MAS, ICANS, infection, and tumor lysis syndrome.
-
Treatment Guidance: AI-driven recommendations for tocilizumab, corticosteroids, and supportive care.
-
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
-
Input: Cytokine levels, vital signs, laboratory values, treatment history.
-
Risk Assessment: Baseline CRS risk stratification pre-therapy.
-
Monitoring: Real-time cytokine tracking with trend analysis.
-
Grading: Automated CRS grade assignment per ASTCT criteria.
-
Differential: Rule out mimics (infection, HLH, ICANS).
-
Treatment: Generate management recommendations.
-
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:
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
- Pediatric: Different baseline cytokine ranges
- Post-COVID: Altered inflammatory responses
- Bridging Therapy: Impact on CRS risk
- Concurrent Infection: Confounding cytokine elevation
Author
AI Group - Biomedical AI Platform
Signals
- GitHub stars
- 3k
- Forks
- 412
- Last commit
- Jul 2026
Advanced
- Item type
- skill
- Key
cytokine-storm-analysis-agent- Source
- github.com/freedomintelligence/openclaw-medical-skills
github.com/freedomintelligence/openclaw-medical-skills
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