Research objective
We aimed to model transformation rates simultaneously over attained age and time since diagnosis in patients with MPN. Further analyses were carried out by exposure to cytoreductive therapies. Additional summary was also provided for genetic information.
Settings
Study design: cohort study
Data sources:
- the National Cancer Register (for MPN, other hematological malignancies)
- the National Inpatient Register (hospital admissions), the National Outpatient Register (specialist visits) (for MPN and AML/MDS diagnoses)
- the National Causes of Deaths Register (for AML/MDS diagnoses and other causes of deaths)
- the National Prescribed Drugs Register (for cytoreductive drugs)
- the Total Population Register (for demographic data)
- the MPN Quality Register (for genetic data)
Study period: three diagnosis-periods are covered in this study:
- 2001 - 2021: for describing transformation rates by subtypes
- 2006 - 2021: for summary by exposure to cytoreductive drugs
- 2008 - 2021: for summary by somatic mutations JAK2, CALR, MPL
Index date: at 3 months post MPN date date. Individuals were excluded if prior to the index date, they transformed to AML/MDS, developed other hematological malignancy, or they emigrated, or died.
Event of interest: transformation rates were analysed separately for AML and MDS. For the event AML, patients were not censored if they were diagnosed with MDS prior to AML, whereas for the MDS, patients were censored if they were diagnosed with AML prior to MDS.
End of follow-up: individuals were followed until the outcome of interest or censoring due to above, or due to emigration, due to death from other causes, or due to the end of follow-up (December 31, 2022); whichever occurred first.
Results
Cohort characteristics
Table 3.1: Characteristics of Swedish patients diagnosed during 2001-2021 with myeloproliferative neoplasms. PV=polycythemia vera, ET=essential thrombocythemia, PMF=primary myelofibrosis.
| PV (N=7156) | ET (N=6810) | PMF (N=1080) |
|---|
Sex |
|
|
|
Female | 3178 (44.4%) | 4200 (61.7%) | 452 (41.9%) |
Male | 3978 (55.6%) | 2610 (38.3%) | 628 (58.1%) |
Age at diagnosis |
|
|
|
Median (Q1, Q3) | 70.9 (60.8, 78.2) | 68.2 (55.5, 77.3) | 71.6 (62.7, 78.7) |
18-49 | 684 (9.6%) | 1192 (17.5%) | 96 (8.9%) |
50-59 | 1007 (14.1%) | 1011 (14.8%) | 114 (10.6%) |
60-69 | 1718 (24.0%) | 1504 (22.1%) | 280 (25.9%) |
70-79 | 2327 (32.5%) | 1884 (27.7%) | 370 (34.3%) |
80-90 | 1420 (19.8%) | 1219 (17.9%) | 220 (20.4%) |
Calendar period of diagnosis |
|
|
|
2001-2010 | 3171 (44.3%) | 2635 (38.7%) | 325 (30.1%) |
2011-2021 | 3985 (55.7%) | 4175 (61.3%) | 755 (69.9%) |
Total person-years and events
Table 3.2: For each subtype, number of individuals, person-years, number of events by outcome, respectively.
Event | | PV | ET | PMF |
|---|
AML | N | 7,156 | 6,810 | 1,080 |
Person-years | 51,234 | 50,546 | 5,132 |
Events | 190 | 191 | 135 |
Median (Q1, Q3) follow-up time (years) | 6.09 (3.00, 10.22) | 6.18 (3.10, 10.79) | 3.71 (1.75, 6.73) |
MDS | N | 7,156 | 6,810 | 1,080 |
Person-years | 50,842 | 50,131 | 4,980 |
Events | 115 | 166 | 83 |
Median (Q1, Q3) follow-up time (years) | 6.01 (2.97, 10.13) | 6.12 (3.06, 10.72) | 3.51 (1.57, 6.53) |
Table 3.3: For each subtype, number of individuals, person-years, number of events by sex and outcome, respectively.
| PV | ET | PMF |
|---|
Event | | Female | Male | Female | Male | Female | Male |
|---|
AML | N | 3,178 | 3,978 | 4,200 | 2,610 | 452 | 628 |
Person-years | 22,557 | 28,677 | 31,713 | 18,832 | 2,265 | 2,867 |
Events | 93 | 97 | 97 | 94 | 56 | 79 |
Median (Q1, Q3) follow-up time (years) | 6.04 (3.06, 9.96) | 6.12 (2.96, 10.41) | 6.35 (3.24, 10.95) | 6.05 (2.95, 10.42) | 4.00 (1.96, 6.88) | 3.48 (1.50, 6.60) |
MDS | N | 3,178 | 3,978 | 4,200 | 2,610 | 452 | 628 |
Person-years | 22,318 | 28,524 | 31,446 | 18,684 | 2,206 | 2,774 |
Events | 62 | 53 | 94 | 72 | 28 | 55 |
Median (Q1, Q3) follow-up time (years) | 5.94 (3.01, 9.86) | 6.08 (2.93, 10.36) | 6.27 (3.17, 10.90) | 6.01 (2.91, 10.38) | 3.88 (1.91, 6.72) | 3.33 (1.45, 6.24) |
Patients by somatic mutations
Table 3.4: Number of patients diagnosed with PV, ET, and PMF in 2008-2021 by somatic mutation status, respectively.
| JAK2 positive | JAK2 negative or not assessed or missing | CALR positive | CALR negative or not assessed or missing | MPL positive | MPL negative or not assessed or missing |
|---|
PV, N = 2325 |
|
|
|
|
|
|
N patients (% among PV) | 2213 (95.2) | 112 (4.8) |
|
|
|
|
Sex |
|
|
|
|
|
|
Female, n (%) | 1110 (50.2) | 58 (51.8) |
|
|
|
|
Male, n (%) | 1103 (49.8) | 54 (48.2) |
|
|
|
|
Calendar period of diagnosis |
|
|
|
|
|
|
2008-2015, n (%) | 1155 (52.2) | 62 (55.4) |
|
|
|
|
2016-2021, n (%) | 1058 (47.8) | 50 (44.6) |
|
|
|
|
ET, N = 2871 |
|
|
|
|
|
|
N patients (% among ET) | 1823 (63.5) | 1048 (36.5) | 249 (8.7) | 2622 (91.3) | 71 (2.5) | 2800 (97.5) |
Sex |
|
|
|
|
|
|
Female, n (%) | 1110 (60.9) | 563 (53.7) | 118 (47.4) | 1555 (59.3) | 41 (57.7) | 1632 (58.3) |
Male, n (%) | 713 (39.1) | 485 (46.3) | 131 (52.6) | 1067 (40.7) | 30 (42.3) | 1168 (41.7) |
Calendar period of diagnosis |
|
|
|
|
|
|
2008-2015, n (%) | 871 (47.8) | 587 (56.0) | 35 (14.1) | 1423 (54.3) | 7 (9.9) | 1451 (51.8) |
2016-2021, n (%) | 952 (52.2) | 461 (44.0) | 214 (85.9) | 1199 (45.7) | 64 (90.1) | 1349 (48.2) |
PMF, N = 810 |
|
|
|
|
|
|
N patients (% among PMF) | 440 (54.3) | 370 (45.7) | 96 (11.9) | 714 (88.1) | 25 (3.1) | 785 (96.9) |
Sex |
|
|
|
|
|
|
Female, n (%) | 185 (42.0) | 162 (43.8) | 41 (42.7) | 306 (42.9) | 15 (60.0) | 332 (42.3) |
Male, n (%) | 255 (58.0) | 208 (56.2) | 55 (57.3) | 408 (57.1) | 10 (40.0) | 453 (57.7) |
Calendar period of diagnosis |
|
|
|
|
|
|
2008-2015, n (%) | 204 (46.4) | 196 (53.0) | 10 (10.4) | 390 (54.6) | <5 | 397 (50.6) |
2016-2021, n (%) | 236 (53.6) | 174 (47.0) | 86 (89.6) | 324 (45.4) |
| 388 (49.4) |
Wald tests for parameters for t1 and t2
Main effects model
Fitted the following flexible parametric survival model on the log-hazard scale with two time-scales:
\[\begin{equation}
\log h = s(t_1; \gamma_1) + s(t_2; \gamma_2)
\tag{3.1}
\end{equation}\]
where \(t_1\) is time since the index date and \(t_2\) is attained age. Functions \(s\) represent restricted cubic splines with \(\gamma\) as corresponding parameters.
Stata code for the model:
/* stset for reference time-scale: time since index date */
stset eof_date, origin(start_date) failure(status==1) scale(365.25) id(lopnr)
/* fit fpm model, where second time-scale, attained age, is defined by using start(start_age) */
stmt, ///
time1(df(3) logtoff) ///
time2(df(3) logtoff start(start_age))
Based on the above model, used Wald test for t1 and for t2 presented in the Table 3.5.
Table 3.5: P-vaues from the Wald tests for parameters of t1 and t2 from the model in (3.1).
| AML | MDS |
|---|
| t1
(time since index date) | t2
(attained age) | t1
(time since index date) | t2
(attained age) |
|---|
PV | 0.01200 | 0.00167 | 0.06958 | 0.00089 |
ET | 0.00350 | 0.00000 | 0.51549 | 0.00000 |
PMF | 0.21794 | 0.33305 | 0.12399 | 0.02239 |
Model with interaction
Fitted the following flexible parametric survival model on the log-hazard scale with two time-scales:
\[\begin{equation}
\log h = s(t_1; \gamma_1) + s(t_2; \gamma_2) + s(t_1, \gamma_3)\cdot(t_2, \gamma_4)
\tag{3.2}
\end{equation}\]
where \(t_1\) is time since the index date and \(t_2\) is attained age. Functions \(s\) represent restricted cubic splines with \(\gamma\) as corresponding parameters. Optimal number of knots for the spline functions were chosen based on AIC and BIC of the models.
Stata code for the model:
/* stset for reference time-scale: time since index date */
stset eof_date, origin(start_date) failure(status==1) scale(365.25) id(lopnr)
/* fit fpm model, where second time-scale, attained age, is defined by using start(start_age) */
stmt , ///
time1(df(3) logtoff) ///
time2(df(3) logtoff start(start_age)) timeint(t1:t2 1:1)
Based on the above model, used Wald test for t1 and for t2 presented in the Table 3.6.
Table 3.6: P-vaues from the Wald tests for parameters of t1 and t2 from the model in (3.2).
| AML | MDS |
|---|
| t1
(time since index date) | t2
(attained age) | t1
(time since index date) | t2
(attained age) |
|---|
PV | 0.02702 | 0.00427 | 0.07651 | 0.00179 |
ET | 0.00790 | 0.00000 | 0.48399 | 0.00000 |
PMF | 0.07767 | 0.09285 | 0.11817 | 0.03222 |
Cumulative incidence of AML and MDS
Table
Table 5.1: Cumulative incidence function (CIF) with 95% confidence intervals (CI) at 1-, 5-, 10-, and 15-years since the index date by subtypes for transformation to AML and to MDS for different ages at the index date, respectively, (index date = MPN diagnosis date + 3m). For AML event, death from other causes was considered as a competing event. For MDS event, both transformation to AML or death from other causes were considered as competing events. PV = polycythemia vera, ET = essential thrombocythemia, PMF = primary myelofibrosis.
Event | Age at index date | Time since index date | PV, CIF (95% CI) | ET, CIF (95% CI) | PMF, CIF (95% CI) |
|---|
AML | 55 | 1y | 0.1% (0.1%, 0.2%) | 0.0% (0.0%, 0.1%) | 3.1% (1.7%, 4.5%) |
5y | 0.9% (0.5%, 1.3%) | 0.6% (0.3%, 0.8%) | 12.2% (8.3%, 16.0%) |
10y | 2.6% (1.9%, 3.3%) | 2.2% (1.6%, 2.9%) | 17.3% (12.1%, 22.6%) |
15y | 5.0% (3.7%, 6.2%) | 4.8% (3.5%, 6.2%) | 20.8% (14.3%, 27.3%) |
60 | 1y | 0.2% (0.1%, 0.3%) | 0.1% (0.0%, 0.2%) | 3.2% (1.8%, 4.5%) |
5y | 1.3% (0.9%, 1.7%) | 1.1% (0.7%, 1.5%) | 12.9% (9.1%, 16.7%) |
10y | 3.4% (2.6%, 4.2%) | 3.8% (2.8%, 4.7%) | 18.7% (13.8%, 23.7%) |
15y | 5.7% (4.5%, 6.9%) | 6.6% (5.2%, 8.1%) | 22.6% (16.9%, 28.4%) |
65 | 1y | 0.3% (0.2%, 0.4%) | 0.2% (0.1%, 0.3%) | 3.2% (1.9%, 4.4%) |
5y | 1.6% (1.2%, 2.1%) | 1.8% (1.3%, 2.3%) | 13.1% (9.9%, 16.3%) |
10y | 3.7% (2.9%, 4.5%) | 4.8% (3.8%, 5.9%) | 19.1% (15.0%, 23.2%) |
15y | 5.6% (4.6%, 6.7%) | 7.2% (5.8%, 8.5%) | 23.0% (18.0%, 27.9%) |
70 | 1y | 0.3% (0.2%, 0.5%) | 0.3% (0.2%, 0.4%) | 3.0% (2.0%, 4.1%) |
5y | 1.7% (1.3%, 2.1%) | 2.1% (1.6%, 2.7%) | 12.6% (9.9%, 15.3%) |
10y | 3.5% (2.8%, 4.1%) | 4.7% (3.9%, 5.6%) | 18.2% (14.3%, 22.0%) |
15y | 4.9% (3.9%, 5.8%) | 6.5% (5.2%, 7.7%) | 21.3% (16.6%, 26.0%) |
75 | 1y | 0.3% (0.2%, 0.5%) | 0.3% (0.2%, 0.4%) | 2.8% (1.7%, 3.9%) |
5y | 1.5% (1.2%, 1.9%) | 1.9% (1.5%, 2.4%) | 11.4% (8.5%, 14.3%) |
10y | 2.9% (2.3%, 3.6%) | 4.0% (3.2%, 4.9%) | 15.9% (12.1%, 19.7%) |
80 | 1y | 0.3% (0.2%, 0.4%) | 0.3% (0.1%, 0.4%) | 2.5% (1.4%, 3.5%) |
5y | 1.3% (1.0%, 1.7%) | 1.7% (1.2%, 2.2%) | 9.7% (7.0%, 12.3%) |
MDS | 55 | 1y | 0.2% (0.1%, 0.3%) | 0.2% (0.1%, 0.3%) | 0.9% (0.2%, 1.7%) |
5y | 0.7% (0.4%, 1.0%) | 0.9% (0.6%, 1.2%) | 4.0% (1.9%, 6.0%) |
10y | 1.4% (0.9%, 1.9%) | 1.6% (1.1%, 2.1%) | 7.6% (4.3%, 11.0%) |
15y | 2.2% (1.5%, 3.0%) | 2.4% (1.7%, 3.2%) | 10.7% (6.0%, 15.4%) |
60 | 1y | 0.2% (0.1%, 0.3%) | 0.2% (0.1%, 0.3%) | 1.3% (0.5%, 2.0%) |
5y | 0.8% (0.5%, 1.1%) | 1.0% (0.7%, 1.4%) | 4.9% (2.7%, 7.1%) |
10y | 1.5% (1.0%, 2.0%) | 2.0% (1.4%, 2.5%) | 8.7% (5.3%, 12.1%) |
15y | 2.4% (1.7%, 3.1%) | 3.1% (2.2%, 3.9%) | 11.2% (7.0%, 15.4%) |
65 | 1y | 0.2% (0.1%, 0.3%) | 0.3% (0.2%, 0.4%) | 1.7% (0.8%, 2.6%) |
5y | 0.9% (0.5%, 1.2%) | 1.3% (0.9%, 1.6%) | 6.1% (3.8%, 8.3%) |
10y | 1.6% (1.1%, 2.1%) | 2.4% (1.8%, 3.1%) | 9.8% (6.7%, 12.9%) |
15y | 2.5% (1.9%, 3.2%) | 3.8% (3.0%, 4.7%) | 11.6% (7.9%, 15.2%) |
70 | 1y | 0.2% (0.1%, 0.3%) | 0.4% (0.2%, 0.5%) | 2.2% (1.3%, 3.2%) |
5y | 0.9% (0.6%, 1.2%) | 1.6% (1.2%, 2.0%) | 7.4% (5.3%, 9.5%) |
10y | 1.7% (1.2%, 2.1%) | 3.1% (2.4%, 3.7%) | 10.7% (7.8%, 13.6%) |
15y | 2.6% (1.9%, 3.2%) | 4.6% (3.6%, 5.7%) | 11.7% (8.4%, 14.9%) |
75 | 1y | 0.2% (0.1%, 0.3%) | 0.5% (0.3%, 0.6%) | 2.9% (1.7%, 4.1%) |
5y | 0.9% (0.7%, 1.2%) | 2.0% (1.6%, 2.5%) | 8.5% (5.9%, 11.1%) |
10y | 1.7% (1.2%, 2.1%) | 3.7% (2.9%, 4.5%) | 10.8% (7.7%, 14.0%) |
80 | 1y | 0.3% (0.1%, 0.4%) | 0.6% (0.4%, 0.8%) | 3.4% (1.9%, 4.8%) |
5y | 1.0% (0.6%, 1.3%) | 2.4% (1.8%, 3.1%) | 8.6% (5.9%, 11.3%) |
Stata code
/* For event AML */
* FPM on log-hazard scale with competing events (AML and death from other causes) over time-scales:
* t1 = time since index date
* t2 = attained age
* for AML: log h=s(t1, df=3) + s(t2, df=3) + t1*t2
* for Death: log h=s(t2, df=4)
// ssc install merlin
merlin (time_y /// /* AML */
rcs(time_y, df(3) orthog event) ///
rcs(time_y, offset(start_age) df(3) orthog event) ///
rcs(time_y, df(1) orthog event)#rcs(time_y, offset(start_age) df(1) orthog event), ///
family(loghazard, failure(cause_aml)) ///
timevar(time_y)) ///
(time_y /// /* Death from other causes */
rcs(time_y, offset(start_age) df(4) orthog event), ///
family(loghazard, failure(cause_death)) ///
timevar(time_y))
/* estimate cumulative incidence function over two time-scales */
local n=1
local datalist ""
forvalues a0= 55 60 : 80 {
qui clear
qui set obs 401
qui gen start_age =`a0' // a0: age at index
qui range time1 0 20 401 // t1: time on study
qui gen time2 = time1 + start_age // t2: attained age
qui gen time_y = .
qui gen cause_aml = .
qui gen cause_death = .
predict cif_aml, cif outcome(1) causes(1 2) ci timevar(time1) at(start_age `a0')
predict cif_death, cif outcome(2) causes(1 2) ci timevar(time1) at(start_age `a0')
qui tempfile temppred`n'
qui save `temppred`n''
local datalist `datalist' `temppred`n''
local n=`n'+1
}
qui clear
qui set obs 0
qui append using `datalist'
keep time1 time2 start_age cif*
By cytoreductive treatment
We aimed to investigate further the patterns of transformation rates over two time-scales in MPN patients overall by exposure to cytoreductive treatments. Since the Prescribed Drugs Register was established in July 2005, we looked at patients with MPN diagnosed in 2006-2021.
The cytoreductive treatment constituted any of the following drugs that were collected after the MPN date and before the end of follow-up: interferon (ATC: L03AB11, L03AB10), hydroxyurea (ATC: L01XX05), ruxolitinib (ATC: L01EJ01), anagrelide (ATC: L01XX35), busulfan (ATC: L01AB01).
Person-years and event numbers by drugs
A time-varying treatment variable was created based on the following criteria:
- “0” = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan,
- “1” = 2 collections of Interferon,
- “2” = 2 collections of Hydroxyurea,
- “3” = 2 collections of each Interferon and Hydroxyurea,
- “4” = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).
Patients can go from treatment state 0 to 1, from 0 to 2, from 1 to 3, from 2 to 3, and reach state 4 from any state. The starting treatment state can be any of 0-4. Note: for the analysis, only treatment states that were at index date and beyond were included.
Due to very few events for specific drugs, it was not possible to fit a model by exposure to different drugs. Below is the summary of person-years and number of events by outcome for each sutbype.
AML
Table 6.1: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is AML. Treatment state is a time-varying covariate, so patients can go from 0 to 1, from 0 to 2, from 1 to 3, from 2 to 3, and reach state 4 from any state, where ‘0’ = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan, ‘1’ = 2 collections of Interferon, ‘2’ = 2 collections of Hydroxyurea, ‘3’ = 2 collections of each Interferon and Hydroxyurea, ‘4’ = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). Note: starting treatment state can be any of the above.
Subtype | Treatment state | Person-years | Number of events | N |
|---|
PV | 0 = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan | 15,557 | 15 | 4,618 |
1 = 2 collections of Interferon | 1,366 | <5 | 300 |
2 = 2 collections of Hydroxyurea | 15,386 | 72 | 2,994 |
3 = 2 collections of each Interferon and Hydroxyurea | 860 | 5 | 203 |
4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). | 1,364 | 25 | 386 |
ET | 0 = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan | 12,667 | 9 | 4,255 |
1 = 2 collections of Interferon | 1,519 | <5 | 322 |
2 = 2 collections of Hydroxyurea | 17,204 | 68 | 3,263 |
3 = 2 collections of each Interferon and Hydroxyurea | 773 | <5 | 180 |
4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). | 2,747 | 23 | 585 |
PMF | 0 = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan | 1,618 | 45 | 757 |
1 = 2 collections of Interferon | 253 | <5 | 65 |
2 = 2 collections of Hydroxyurea | 1,771 | 41 | 481 |
3 = 2 collections of each Interferon and Hydroxyurea | 125 | <5 | 34 |
4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). | 586 | 28 | 218 |
Total number of patients in the cohort: PV N=5566, ET N=5517, PMF N=960. |
MDS
Table 6.2: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is MDS. Treatment state is a time-varying covariate, so patients can go from 0 to 1, from 0 to 2, from 1 to 3, from 2 to 3, and reach state 4 from any state, where ‘0’ = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan, ‘1’ = 2 collections of Interferon, ‘2’ = 2 collections of Hydroxyurea, ‘3’ = 2 collections of each Interferon and Hydroxyurea, ‘4’ = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). Note: starting treatment state can be any of the above.
Subtype | Treatment state | Person-years | Number of events | N |
|---|
PV | 0 = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan | 15,511 | 12 | 4,618 |
1 = 2 collections of Interferon | 1,361 | <5 | 299 |
2 = 2 collections of Hydroxyurea | 15,289 | 43 | 2,991 |
3 = 2 collections of each Interferon and Hydroxyurea | 858 | <5 | 202 |
4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). | 1,330 | 9 | 380 |
ET | 0 = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan | 12,617 | 26 | 4,255 |
1 = 2 collections of Interferon | 1,516 | <5 | 322 |
2 = 2 collections of Hydroxyurea | 17,100 | 61 | 3,256 |
3 = 2 collections of each Interferon and Hydroxyurea | 772 | <5 | 180 |
4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). | 2,711 | 18 | 581 |
PMF | 0 = <2 collections of Interferon, Hydroxyurea, Jakavi or Anagrelide, and 0 collection of Busulfan | 1,542 | 37 | 757 |
1 = 2 collections of Interferon | 250 | <5 | 65 |
2 = 2 collections of Hydroxyurea | 1,736 | 24 | 476 |
3 = 2 collections of each Interferon and Hydroxyurea | 125 | <5 | 34 |
4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). | 561 | 11 | 210 |
Total number of patients in the cohort: PV N=5566, ET N=5517, PMF N=960. |
Person-years and event numbers by initiation
A time-varying treatment variable was created based on the following criteria:
- “0” = Not initiated cytoreductive treatment,
- “1” = Initiated cytoreductive treatment.
Patients can go from treatment state 0 to 1, or start at 1. Note: for the analysis, only treatment states that were at index date and beyond were included.
Due to very few events for state “Not initiated cytoreductive treatment”, it was not possible to fit a model. Below is the summary of person-years and number of events by outcome for each sutbype.
AML
Table 6.3: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is AML. Treatment state is a time-varying covariate, so patients can go from 0 to 1, or start at 1, where ‘0’ = Not initiated cytoreductive treatment, ‘1’ = Initiated cytoreductive treatment. Note: starting treatment state can be any of the above.
Subtype | Treatment state | Person-years | Number of events | N |
|---|
PV | 0 = Not initiated cytoreductive treatment | 14,581 | 13 | 3,327 |
1 = Initiated cytoreductive treatment | 19,951 | 105 | 3,515 |
ET | 0 = Not initiated cytoreductive treatment | 11,720 | 5 | 2,631 |
1 = Initiated cytoreductive treatment | 23,191 | 101 | 3,941 |
PMF | 0 = Not initiated cytoreductive treatment | 1,450 | 39 | 491 |
1 = Initiated cytoreductive treatment | 2,903 | 77 | 699 |
Total number of patients in the cohort: PV N=5566, ET N=5517, PMF N=960. |
MDS
Table 6.4: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is MDS. Treatment state is a time-varying covariate, so patients can go from 0 to 1, or start at 1, where ‘0’ = Not initiated cytoreductive treatment, ‘1’ = Initiated cytoreductive treatment. Note: starting treatment state can be any of the above.
Subtype | Treatment state | Person-years | Number of events | N |
|---|
PV | 0 = Not initiated cytoreductive treatment | 14,537 | 12 | 3,327 |
1 = Initiated cytoreductive treatment | 19,812 | 56 | 3,511 |
ET | 0 = Not initiated cytoreductive treatment | 11,671 | 20 | 2,631 |
1 = Initiated cytoreductive treatment | 23,045 | 89 | 3,937 |
PMF | 0 = Not initiated cytoreductive treatment | 1,378 | 28 | 491 |
1 = Initiated cytoreductive treatment | 2,835 | 46 | 694 |
Total number of patients in the cohort: PV N=5566, ET N=5517, PMF N=960. |
Statistical Software
The statistical software used for the analysis: Stata 18.0 and R version 4.4.3.
