5 Thrombosis Risk Prediction in PV

5.1 CYTO-PV study

Hct Less than 45% reduces thrombotic events.
What hematocrit control does not do?
- Suppress the JAK2-V617F clone.
- Prevent bone marrow fibrosis progression.
- Reduce transformation risk to post-PV myelofibrosis or blast phase
- Reduce JAK2 VAF with hydroxyurea in most patients.

5.2 Thrombosis Risk Assessment in Polycythemia Vera (TRAP) (Gangat et al. 2024)

5.3 Multiple factor-based prognostic score system of thrombosis (MFPS-PV)(Gu et al. 2023)

5.4 The risk of venous and arterial thrombosis in polycythemia vera patients with secondary malignancies.

5.5 ASXL1 Is a Risk Factor for Thrombosis in Polycythemia Vera (Zhang et al. 2022)

5.5.1 Code to generate a comparison table of thrombosis prediction models in Polycythemia Vera

Code

# Comparison of Thrombosis Prediction Models for Polycythemia Vera

library(knitr)

# Create comparison table
model_comparison <- data.frame(
  Model = c("Conventional", "ANA Model", "TRAP Model", "MFPS-PV"),
  
  Year = c("Standard", "2024", "2024", "2023"),
  
  Risk_Factors = c(
    "Age ≥60, Previous thrombosis",
    "Age ≥60, Neutrophils >8×10⁹/L, Arterial thrombosis history",
    "Hemoglobin thresholds, Leukocytes ≤11×10⁹/L, Diabetes",
    "Age ≥60, CVF, High-risk mutations (DNMT3A/ASXL1/BCOR), Previous thrombosis"
  ),
  
  Number_of_Factors = c(2, 3, 3, 4),
  
  Includes_Mutations = c("No", "No", "No", "Yes"),
  
  Risk_Groups = c("Low/High", "Low/High", "Low/Moderate/High", "Low/Intermediate/High"),
  
  C_Statistic = c(0.80, 0.786, 0.79, 0.87),
  
  Source = c("Traditional", "ASH Abstract #634", "ASH Abstract TRAP", "Gu et al. RPTH 2023")
)

# Display table
kable(model_comparison, 
      caption = "Comparison of Thrombosis Risk Prediction Models in Polycythemia Vera",
      align = c("l", "c", "l", "c", "c", "c", "c", "l"))

Comparison of Thrombosis Risk Prediction Models in Polycythemia Vera
Model	Year	Risk_Factors	Number_of_Factors	Includes_Mutations	Risk_Groups	C_Statistic	Source
Conventional	Standard	Age ≥60, Previous thrombosis	2	No	Low/High	0.800	Traditional
ANA Model	2024	Age ≥60, Neutrophils >8×10⁹/L, Arterial thrombosis history	3	No	Low/High	0.786	ASH Abstract #634
TRAP Model	2024	Hemoglobin thresholds, Leukocytes ≤11×10⁹/L, Diabetes	3	No	Low/Moderate/High	0.790	ASH Abstract TRAP
MFPS-PV	2023	Age ≥60, CVF, High-risk mutations (DNMT3A/ASXL1/BCOR), Previous thrombosis	4	Yes	Low/Intermediate/High	0.870	Gu et al. RPTH 2023

Code

# Simple scoring summary
cat("\n\nScoring Systems:\n")



Scoring Systems:

Code

cat("================\n\n")

================

Code

cat("ANA Model:\n")

ANA Model:

Code

cat("- Age ≥60: 2.48 points\n")

- Age ≥60: 2.48 points

Code

cat("- Neutrophils >8: 4.43 points\n")

- Neutrophils >8: 4.43 points

Code

cat("- Arterial history: 2.93 points\n")

- Arterial history: 2.93 points

Code

cat("- Risk: Low (<4.43) vs High (≥4.43)\n\n")

- Risk: Low (<4.43) vs High (≥4.43)

Code

cat("MFPS-PV Model:\n")

MFPS-PV Model:

Code

cat("- Age ≥60: 1 point\n")

- Age ≥60: 1 point

Code

cat("- CVF: 1.5 points\n")

- CVF: 1.5 points

Code

cat("- High-risk mutations: 1.5 points\n")

- High-risk mutations: 1.5 points

Code

cat("- Previous thrombosis: 2 points\n")

- Previous thrombosis: 2 points

Code

cat("- Risk: Low (≤1), Intermediate (1.5-2.5), High (≥3)\n\n")

- Risk: Low (≤1), Intermediate (1.5-2.5), High (≥3)

Code

cat("Key Finding: MFPS-PV is the first model to incorporate genetic mutations\n")

Key Finding: MFPS-PV is the first model to incorporate genetic mutations

Code

cat("and shows the best discrimination (C-statistic = 0.87)\n")

and shows the best discrimination (C-statistic = 0.87)

```