Poor Correlation Between DIPSS and Passamonti Prognostic Risk Scores for Post Polycythemia Vera and Essential Thrombocythemia Myelofibrosis

Background: Post polycythemia vera (PV) and essential thrombocytosis (ET) myelofibrosis (MF), like primary MF (PMF), are heralded by splenomegaly, cytopenias, heavy symptom burden, and decreased overall survival. Risk prognostication tools, including the dynamic international prognostic scoring system (DIPSS) and the Passamonti risk score for post-PV MF (Passamonti et al. Blood 2010, 2008), are helpful to estimate survival and guide therapeutic decision-making. For Post-PV/ET MF, it is unknown if DIPSS correlates with the Passamonti risk score. Here we evaluate the correlation between the two available risk prognostication tools in post-PV/ET MF.

Methods:

Retrospective chart review conducted for patients with post-PV/ET MF seen between 2000-2012. Descriptive statistics were used to describe patient demographic and clinical variables in post PV/ET patients.

DIPSS Score: calculated at time of last follow up: 1 point for age >65, 2 points for hemoglobin <10g/dL, 1 point for leukocytes >25 x 10⁹, 1 point for circulating blasts 1% and 1 point for constitutional symptoms. Risk group was assigned: low-risk (0 adverse points), intermediate-1 risk (1 adverse point), intermediate-2 risk (2-3 adverse points) and high-risk (4-6 adverse points).

Passamonti Risk Scores: calculated at time of last follow up: with 1 point assigned for hemoglobin level less than 100 g/L (10 g/dL), platelet count less than 100 x10 (9)/L, and/or leukocyte count more than 30x10(9)/L.

Cross-tabulation of DIPSS score versus Passamonti risk score at time of last follow-up was conducted and the percent agreement was described. Spearman correlation coefficient between DIPSS score at follow-up with Passamonti score was conducted.

Results:

Patients:

Sixty-one (58%) post-ET and 44 (42%) post-PV patients were identified, total N=105. Male to Female ratio was 1:1. Median age was 65 (range 25-87). JAK2 V617Fpresent in 68 (65.4%). Median hemoglobin was 10.9 g/dL (range 5.3-17.0), median platelet count 287 x 10⁹ (range 20-1864), and median WBC was 11.1x 10⁹ (range 1.1-165).

Risk prognostication:

When DIPSS risk scores were applied 11 patients were low, 48 patients were Intermediate-1, 31 patients were Intermediate-2, and 10 patients were high. When Passamonti risk scores were applied 57 patients received score of 0, 31 patients scored 1, 9 patients scored 2, and 3 patients scored 3.

DIPSS and Passamonti risk score agreement:

The overall agreement between DIPSS and Passamonti risk scores was low, with 24% of cases in agreement. Spearman correlation: Rho=0.451; p<0.001. See Table #1 with agreement seen in highlighted cells.

Conclusion:

Post PV/ET MF represents a unique clinical entity and risk prognostication schemes developed for PMF, such as DIPSS, may not represent accurate prognosis estimation in this subgroup of MF. When DIPSS and Passamonti risk scores were applied to same post PV/ET group, agreement was poor at 24%. Additionally, DIPSS was more likely to assign patients to high-risk category than Passamonti. Further studies are needed to evaluate the DIPSS and Passamonti prognostic risk score application to the post PV/ET MF population.