Abstract
We have previously reported on the significant, but heterogeneous baseline MPN symptom burden among an international sample of MPN patients (including essential thrombocythemia (ET), polycythemia vera (PV), and myelofibrosis (MF)) utilizing the MPN Symptom Assessment Form (MPN-SAF) and the derivative Total Symptom Score (MPN-SAF TSS). Recent clinical trials have sought to determine optimal MPN symptom response criteria, such as absolute 10 point improvement in MPN SAF TSS for ET/PV (ELN Criteria, Barosi et. al. Blood 2013) and 50% reduction in MPN-SAF TSS for MF (IWG-MRT, Tefferi et. al. Blood 2013). We sought to determine the role of improvement in MPN-SAF TSS quartiles as potential thresholds to assess symptomatic response to therapy.
Utilizing prospectively gathered MPN-SAF TSS (Emanuel et. al. JCO 2012) in patients we assessed potential thresholds of response by evaluating quartile thresholds for severity of symptom burden. The MPN-SAF TSS was scored as the average of 10 symptoms (individual symptoms scores of 0-10, with a total score of 0 (best) to 100 (worst)). MPN-SAF TSS quartiles were identified by the percentage of scores between 0-24% (quartile 1 (Q1)), 25-49% (quartile 2 (Q2)), 50-74% (quartile 3 (Q3)), 75-100% (quartile 4 (Q4)).
MPN-SAF TSS Quartiles: MPN-SAF TSS quartiles were identified among 1858 MPN patients (ET N=775, PV N=654, and MF N=423). Overall MPN-SAF TSS scores of 0 - 7 were designated as Q1, 8 - 17 as Q2, 18 - 31 as Q3, and ≥ 32 was as Q4. MPN-SAF TSS scores were significantly different between clusters (p<0.001).
Associations Between Quartiles and Demographic/ Disease Factors: As quartiles increased, the proportion of PV and ET patients diminished and MF increased (Table 1, p<0.001). Cytopenias and transfusion dependence increased in prevalence in the higher quartiles (p<0.001). A history of prior thrombosis was also significantly more prevalent in the quartiles with highest symptom burden (p<0.001). The prevalence of women was significantly higher among the more symptomatic quartiles females 48.9% Q1, 49.4% Q2, 58.4% Q3, and 60.1% Q4; p<0.001).
. | Q1:<8 (N=466) . | Q2: 8-<18 (N=450) . | Q3: 18-<32 (N=465) . | Q4: >/=32 (N=477) . | Total (N=1858) . | p value . |
---|---|---|---|---|---|---|
MPN-SAF TSS | <0.0011 | |||||
Mean (SD) | 3.7 (2.42) | 12.7 (2.82) | 23.7 (3.86) | 44.9 (11.11) | 21.5 (16.69) | |
Gender | <0.0012 | |||||
F | 222 (48.9%) | 222 (49.4%) | 267 (58.4%) | 285 (60.1%) | 996 (54.3%) | |
MPN | <0.0012 | |||||
ET | 233 (50.1%) | 205 (45.9%) | 183 (39.5%) | 154 (32.3%) | 775 (41.8%) | |
PV | 161 (34.6%) | 152 (34%) | 169 (36.5%) | 172 (36.1%) | 654 (35.3%) | |
MF | 71 (15.3%) | 90 (20.1%) | 111 (24%) | 151 (31.7%) | 423 (22.8%) | |
IPSET Risk (ET only) | 0.182 | |||||
Low | 72 (34.4%) | 60 (32.4%) | 52 (34.4%) | 42 (36.5%) | 226 (34.2%) | |
Int | 105 (50.2%) | 91 (49.2%) | 78 (51.7%) | 44 (38.3%) | 318 (48.2%) | |
High | 32 (15.3%) | 34 (18.4%) | 21 (13.9%) | 29 (25.2%) | 116 (17.6%) | |
PV Risk (PV only) | 0.302 | |||||
Low | 46 (34.3%) | 36 (30.3%) | 40 (30.3%) | 42 (37.5%) | 164 (33%) | |
Int-1 | 46 (34.3%) | 43 (36.1%) | 37 (28%) | 29 (25.9%) | 155 (31.2%) | |
Int-2 | 40 (29.9%) | 36 (30.3%) | 45 (34.1%) | 36 (32.1%) | 157 (31.6%) | |
High | 2 (1.5%) | 4 (3.4%) | 10 (7.6%) | 5 (4.5%) | 21 (4.2%) | |
DIPSS Risk (MF only) | <0.0012 | |||||
Low | 16 (40%) | 9 (16.4%) | 14 (20.6%) | 4 (5.6%) | 43 (18.3%) | |
Int-1 | 19 (47.5%) | 35 (63.6%) | 38 (55.9%) | 36 (50%) | 128 (54.5%) | |
Int-2 | 5 (12.5%) | 10 (18.2%) | 14 (20.6%) | 28 (38.9%) | 57 (24.3%) | |
High | 0 (0%) | 1 (1.8%) | 2 (2.9%) | 4 (5.6%) | 7 (3%) |
. | Q1:<8 (N=466) . | Q2: 8-<18 (N=450) . | Q3: 18-<32 (N=465) . | Q4: >/=32 (N=477) . | Total (N=1858) . | p value . |
---|---|---|---|---|---|---|
MPN-SAF TSS | <0.0011 | |||||
Mean (SD) | 3.7 (2.42) | 12.7 (2.82) | 23.7 (3.86) | 44.9 (11.11) | 21.5 (16.69) | |
Gender | <0.0012 | |||||
F | 222 (48.9%) | 222 (49.4%) | 267 (58.4%) | 285 (60.1%) | 996 (54.3%) | |
MPN | <0.0012 | |||||
ET | 233 (50.1%) | 205 (45.9%) | 183 (39.5%) | 154 (32.3%) | 775 (41.8%) | |
PV | 161 (34.6%) | 152 (34%) | 169 (36.5%) | 172 (36.1%) | 654 (35.3%) | |
MF | 71 (15.3%) | 90 (20.1%) | 111 (24%) | 151 (31.7%) | 423 (22.8%) | |
IPSET Risk (ET only) | 0.182 | |||||
Low | 72 (34.4%) | 60 (32.4%) | 52 (34.4%) | 42 (36.5%) | 226 (34.2%) | |
Int | 105 (50.2%) | 91 (49.2%) | 78 (51.7%) | 44 (38.3%) | 318 (48.2%) | |
High | 32 (15.3%) | 34 (18.4%) | 21 (13.9%) | 29 (25.2%) | 116 (17.6%) | |
PV Risk (PV only) | 0.302 | |||||
Low | 46 (34.3%) | 36 (30.3%) | 40 (30.3%) | 42 (37.5%) | 164 (33%) | |
Int-1 | 46 (34.3%) | 43 (36.1%) | 37 (28%) | 29 (25.9%) | 155 (31.2%) | |
Int-2 | 40 (29.9%) | 36 (30.3%) | 45 (34.1%) | 36 (32.1%) | 157 (31.6%) | |
High | 2 (1.5%) | 4 (3.4%) | 10 (7.6%) | 5 (4.5%) | 21 (4.2%) | |
DIPSS Risk (MF only) | <0.0012 | |||||
Low | 16 (40%) | 9 (16.4%) | 14 (20.6%) | 4 (5.6%) | 43 (18.3%) | |
Int-1 | 19 (47.5%) | 35 (63.6%) | 38 (55.9%) | 36 (50%) | 128 (54.5%) | |
Int-2 | 5 (12.5%) | 10 (18.2%) | 14 (20.6%) | 28 (38.9%) | 57 (24.3%) | |
High | 0 (0%) | 1 (1.8%) | 2 (2.9%) | 4 (5.6%) | 7 (3%) |
P-value calculated via ANOVA F-Test
P-value calculated via Chi-Square Test
Associations Between Individual Symptoms and MPN-SAF TSS Quartiles: All individual symptoms measured in the MPN-SAF TSS were significantly worse in quartiles as they increased (p<0.0001).
Evaluation of Prognostic Scoring and MPN-SAF TSS Quartiles: Comparison of each patients individual risk score (IPSET, PV, DIPSS for MF) and worsening symptom quartile showed the highest correlation with MF patients (DIPSS) (Table 1). However, ET and PV risk scores were not surrogates for symptom burden by quartile.
Distribution of MPN patient symptomatic burden by MPN-SAF TSS quartiles provides an easy-to-calculate method to cluster and analyze MPN patients of similar burden. Although MF patients are most prevalent in the most severe quartile of MPN symptomatology it is notable that Q4 has many patients with PV and ET. Future prospective efforts are ongoing to assess the potential of using changes in quartile (i.e. improving from Q3 to Q1) as potential symptomatic response thresholds.
Etienne:novartis: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Bristol Myers Squibb: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Pfizer: Membership on an entity’s Board of Directors or advisory committees; Ariad: Membership on an entity’s Board of Directors or advisory committees. Roy:Novartis, BMS: Honoraria. Harrison:Gilead: Honoraria, Membership on an entity’s Board of Directors or advisory committees; S Bio: Honoraria, Membership on an entity’s Board of Directors or advisory committees; Shire: Speakers Bureau; Celgene: Honoraria; YM Bioscience: Honoraria, Membership on an entity’s Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding, Speakers Bureau. Vannucchi:Novartis: Honoraria, Membership on an entity’s Board of Directors or advisory committees. Birgegard:Vifor Pharma: Honoraria.
Author notes
Asterisk with author names denotes non-ASH members.
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