git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/mpn-progression-monitor-agent" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-mpn-progression-monitor-agent && rm -rf "$T"
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/mpn-progression-monitor-agent" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-mpn-progression-monitor-agent && rm -rf "$T"
skills/mpn-progression-monitor-agent/SKILL.mdname: 'mpn-progression-monitor-agent' description: 'AI-powered myeloproliferative neoplasm monitoring for disease progression prediction, treatment response tracking, and transformation risk assessment in PV, ET, and myelofibrosis.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
MPN Progression Monitor Agent
The MPN Progression Monitor Agent provides comprehensive monitoring of myeloproliferative neoplasms (PV, ET, MF) for disease progression, treatment response, and transformation risk. It integrates molecular profiling, clinical parameters, and AI-based risk models to guide management of chronic phase disease and predict blast transformation.
When to Use This Skill
- When monitoring JAK2/CALR/MPL mutation burden over time.
- For predicting fibrosis progression in PV/ET.
- To assess risk of blast transformation.
- When tracking treatment response to JAK inhibitors.
- For calculating dynamic risk scores (DIPSS, MIPSS70).
Core Capabilities
-
Mutation Monitoring: Track driver and high-risk mutation VAF.
-
Progression Prediction: Model fibrosis and transformation risk.
-
Risk Scoring: Calculate DIPSS, MIPSS70+, MTSS dynamically.
-
Treatment Response: Assess molecular and clinical response.
-
Clone Evolution: Track clonal dynamics and new mutations.
-
Transplant Timing: Optimize allo-HSCT timing decisions.
MPN Classification
| MPN Type | Driver Mutations | Progression Risk |
|---|---|---|
| PV | JAK2 V617F (95%), JAK2 exon 12 | Fibrosis 10-15%, AML 2-5% |
| ET | JAK2 (55%), CALR (25%), MPL (5%) | Fibrosis 5-10%, AML 1-2% |
| Pre-PMF | Same as PMF | Variable |
| PMF | JAK2 (60%), CALR (25%), MPL (5%) | AML 10-20% |
High-Risk Mutations
| Mutation | Impact | MF Association |
|---|---|---|
| ASXL1 | Adverse | Strong |
| SRSF2 | Adverse | Strong (PMF) |
| EZH2 | Adverse | Moderate |
| IDH1/2 | Adverse | Transformation |
| RUNX1 | Very Adverse | Transformation |
| TP53 | Very Adverse | Transformation |
| U2AF1 | Adverse | Moderate |
Risk Scores
| Score | Components | Application |
|---|---|---|
| IPSS | Age, Hb, WBC, blasts, symptoms | PMF at diagnosis |
| DIPSS | Same, dynamic | PMF follow-up |
| DIPSS+ | + karyotype, transfusion, platelets | PMF refined |
| MIPSS70 | Molecular markers | Transplant-age PMF |
| MIPSS70+ v2.0 | + U2AF1, karyotype | Most comprehensive |
| MTSS | Transplant-specific | Allo-HSCT outcomes |
Workflow
-
Input: Serial molecular testing, CBC, clinical parameters.
-
Baseline Assessment: Calculate initial risk score.
-
Mutation Tracking: Monitor VAF trends over time.
-
Risk Recalculation: Update scores at each timepoint.
-
Progression Detection: Identify molecular/clinical progression.
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Treatment Assessment: Evaluate response to therapy.
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Output: Dynamic risk assessment, progression alerts, recommendations.
Example Usage
User: "Monitor this myelofibrosis patient's disease trajectory and update risk scores with new molecular data."
Agent Action:
python3 Skills/Hematology/MPN_Progression_Monitor_Agent/mpn_monitor.py \ --patient_id MF_001 \ --molecular_data serial_mutations.csv \ --cbc_data serial_cbc.csv \ --clinical_data symptoms.json \ --mpn_type pmf \ --baseline_date 2024-01-15 \ --calculate_scores dipss,mipss70 \ --output mpn_monitoring/
Input Requirements
| Data Type | Parameters | Frequency |
|---|---|---|
| Molecular | JAK2/CALR/MPL VAF, NGS panel | q3-6 months |
| CBC | Hb, WBC, platelets, blasts | Monthly |
| Clinical | Symptoms, spleen size | q3 months |
| Bone Marrow | Fibrosis grade, cytogenetics | q6-12 months |
Output Components
| Output | Description | Format |
|---|---|---|
| Risk Scores | DIPSS, MIPSS70 over time | .csv |
| VAF Trends | Mutation burden plots | .png |
| Progression Alert | Warning if criteria met | .json |
| Response Assessment | IWG-MRT criteria | .json |
| Transplant Timing | Recommendation if indicated | .json |
| Clone Evolution | New mutations, clonal shifts | .csv |
Progression Criteria
| Progression Type | Criteria | Action |
|---|---|---|
| Clinical | New symptoms, splenomegaly | Intensify therapy |
| Hematologic | Cytopenias, increased blasts | BMB, cytogenetics |
| Molecular | New high-risk mutations | Risk restaging |
| Fibrotic | Increased fibrosis grade | Consider transplant |
| Blast Phase | ≥20% blasts | Urgent intervention |
Response Criteria (IWG-MRT)
| Response | Definition | Implications |
|---|---|---|
| Complete Remission | No disease manifestations | Excellent outcome |
| Partial Remission | >50% improvement | Good response |
| Clinical Improvement | Symptom/spleen improvement | Benefit |
| Stable Disease | No change | Observe |
| Progressive Disease | Progression criteria | Change therapy |
AI/ML Components
Progression Prediction:
- Survival analysis with molecular features
- Random survival forests
- Deep learning time-to-event
Clone Tracking:
- VAF trajectory modeling
- New clone detection
- Evolutionary tree inference
Transplant Decision:
- Survival benefit modeling
- NRM prediction
- Optimal timing algorithms
Treatment Response Monitoring
| Therapy | Response Markers | Timeline |
|---|---|---|
| Ruxolitinib | Spleen, symptoms, JAK2 VAF | 12-24 weeks |
| Fedratinib | Similar to ruxolitinib | 24 weeks |
| Momelotinib | + anemia improvement | 24 weeks |
| Interferon | Molecular response, JAK2 VAF | 12+ months |
Prerequisites
- Python 3.10+
- lifelines, scikit-survival
- Variant annotation tools
- Risk score calculators
- Visualization libraries
Related Skills
- CHIP_Clonal_Hematopoiesis_Agent - Pre-MPN states
- MDS_Classification_Agent - Overlap syndromes
- Bone_Marrow_AI_Agent - Morphology analysis
- Coagulation_Thrombosis_Agent - Thrombosis risk
Thrombosis Risk in MPN
| Factor | Risk Increase | Management |
|---|---|---|
| Age >60 | 2-3x | Cytoreduction |
| Prior thrombosis | 3-5x | Anticoagulation |
| JAK2 V617F | 2x | Higher for homozygous |
| High WBC | 1.5-2x | Control counts |
| CV risk factors | Additive | Aggressive management |
Special Considerations
- Triple-Negative MPN: Different prognosis, consider other diagnoses
- Cytogenetic Evolution: High-risk signal, BMB follow-up
- New Mutations: May indicate disease evolution
- Treatment Resistance: Consider second-line or transplant
- Quality of Life: Balance treatment intensity
Transplant Indications
| Indication | Criteria | Timing |
|---|---|---|
| High-Risk PMF | MIPSS70+ high/very high | Consider early |
| Blast Phase | ≥20% blasts | Urgent if fit |
| Refractory Disease | Failed JAKi | Evaluate |
| Transfusion Dependence | RBC/platelet dependent | Factor in decision |
Author
AI Group - Biomedical AI Platform
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->