Identifying Tyrosine Kinase Inhibitor Nonadherence in Chronic Myeloid Leukemia: Subanalysis of TAKE-IT Pilot Study

Original Study

Identifying Tyrosine Kinase Inhibitor Nonadherence in Chronic Myeloid Leukemia: Subanalysis of TAKE-IT Pilot Study Avi Leader,1,2 Anat Gafter-Gvili,1,2,3 Noam Benyamini,4 Juliet Dreyer,1 Bronya Calvarysky,5 Alina Amitai,6 Osnat Yarchovsky-Dolberg,2,7 Giora Sharf,8 Eric Tousset,9 Opher Caspi,2,10 Martin Ellis,2,10 Itai Levi,11 Pia Raanani,1,2 Sabina De Geest12,13 Abstract Identifying nonadherence (NA) in chronic myeloid leukemia (CML) remains a challenge. Tyrosine kinase inhibitor adherence was measured by electronic monitoring and Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) self-report in 55 CML patients over 4 months. The BAASIS had 67% sensitivity and 71% specificity for diagnosing NA. The BAASIS and the risk factors for NA found in this study provide a basis for identifying nonadherent CML patients. Background: There are inconsistencies in reports on correlates for nonadherence (NA) to tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML). The diagnostic accuracy of subjective adherence measures using electronic monitoring (EM) as the reference standard is yet to be determined. This study aimed to evaluate correlates of TKI NA using EM and test the diagnostic accuracy of subjective adherence measures. Patients and Methods: CML patients receiving a TKI for any duration were enrolled at 4 hematology institutes, and adherence was measured for 4 months. EM adherence was the reference adherence measure, expressed as the percentage of days with the drug taken as prescribed. Subjective adherence was measured using the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) self-report and clinician-reported visual analog scale (VAS) at 2 time points. Baseline theoryederived correlates of NA were identified using single and multiple regression analysis. The diagnostic accuracy of BAASIS and clinician-reported VAS was tested against an exploratory EM NA cutoff of < 95%. Results: The median EM adherence (n ¼ 55) was 97.5% (range, 48-100%), while the 25th percentile was 92.1%. Lack of membership in a CML patient support group, living alone, and third-line treatment were associated with EM NA on multiple regression analysis. The BAASIS self-report (n ¼ 94) had a sensitivity of 67% and a specificity of 71% for diagnosing NA, while clinician-reported VAS (n ¼ 89) had a sensitivity of 78% and specificity of 42%. Conclusion: A quarter of patients had potentially clinically meaningful NA. These NA correlates and the BAASIS provide a basis for identifying nonadherent patients who can be targeted by interventions. Clinical Lymphoma, Myeloma & Leukemia, Vol. -, No. -, --- ª 2018 Elsevier Inc. All rights reserved. Keywords: Adherence, Electronic monitoring, Risk factors, Self-report

P.R. and S.D.G. authors contributed equally to this article, and both should be considered senior author. 1 Institute of Hematology, Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petah Tikva, Israel 2 Sackler School of Medicine, Tel Aviv University, Israel 3 Department of Medicine A, Beilinson Hospital, Rabin Medical Center, Petah Tikva, Israel 4 Department of Hematology and Bone Marrow Transplantation, Rambam Health Care Campus, Haifa, Israel 5 Department of Pharmacy, Beilinson Hospital, Rabin Medical Center, Petah Tikva, Israel 6 Department of Pharmacy, Meir Medical Center, Kfar Saba, Israel 7 Hematology Institute and Blood Bank, Meir Medical Center, Kfar Saba, Israel 8 Israeli CML Patients Organization, Netanya, Israel

2152-2650/$ - see frontmatter ª 2018 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.clml.2018.06.007

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AARDEX group, Visé, Belgium Integrative medicine and Cancer Survivorship Program; Davidoff Cancer Center, Rabin Medical Center, Petah Tikva, Israel 11 Hematology Institute, Soroka University Medical Center, Beer-Sheva, Israel 12 Institute of Nursing Science, Department Public Health, University of Basel, Switzerland 13 Academic Center of Nursing and Midwifery, Department Public Health and Primary Care, KU Leuven, Belgium 10

Submitted: May 11, 2018; Accepted: Jun 7, 2018 Address for correspondence: Avi Leader, MD, Institute of Hematology, Rabin Medical Center, 39 Jabotinsky, Petah-Tikva 4941492, Israel Fax: þ972-3-9240145; e-mail contact: [email protected]

Clinical Lymphoma, Myeloma & Leukemia Month 2018

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Identifying Nonadherence in CML Introduction Tyrosine kinase inhibitors (TKIs) targeting the BCR-ABL1 protein1 have changed chronic myeloid leukemia (CML) from a terminal disease to a truly chronic one, with 86% overall survival at 7 years.2 Five oral TKIs are currently approved for treatment in CML,3-7 all necessitating long-term if not lifelong therapy provided as 1 to 2 daily doses.8 Nonadherence (NA) to TKIs is prevalent in this population9-12 and is associated with less favorable clinical outcomes.9,10,13,14 Adherence to medication is the process by which medication is taken as prescribed,15 and it consists of the following 3 phases as defined by the so-called ABC taxonomy: initiation with the first dose; implementation, which is the extent to which a patient’s actual dose matches the prescribed regimen from initiation until taking the last dose; and discontinuation, marking the end of therapy with the last dose. Furthermore, persistence delineates the time between initiation and last dose.15 Crucially, the prevalence, risk factors, and consequences of NA may differ between these phases. Nevertheless, most studies on TKI adherence in CML do not clearly state which phases of adherence were assessed, thus limiting the interpretation of some of the data.9,10,16 Across varying measures and definitions, perfect (ie, 100%) TKI adherence is seen in 20% to 53% of subjects,9,17-21 while mean adherence rates range from 76%22 to 98%.10,14,23,24 In CML, TKI adherence is not normally distributed, as exemplified by the 2 polarized adherence groups identified by Marin et al10 with electronic monitoring (EM) of imatinib. Using a cutoff of 90%, 2 groups were identified, one with near-perfect adherence and favorable outcomes and the other with a median adherence of 76% and adverse prognosis. Importantly, TKIs in CML appear to have little forgiveness, meaning that any deviation from the recommended dosing regimen may be clinically meaningful.25 Therefore, it is imperative to identify the nonadherent subgroup that is a candidate for adherence-enhancing interventions (AEIs). This could be achieved by measuring adherence to detect NA and identifying modifiable correlates (ie, risk factors) of NA. Measuring adherence to TKIs in CML is challenging and not standardized.26 In general, electronic methods of measuring adherence provide richer sampling and better reliability,27 are more sensitive,28 and correlate better with clinical outcomes10 than subjective measures, which overestimate adherence in CML and other settings.10,29 For instance, a recent study on pediatric acute lymphoblastic leukemia showed that self-reported intake exceeded EM at some point in 84% of patients.29 EM is therefore an appropriate reference for evaluating diagnostic accuracy of subjective measures, although not suited for widespread routine clinical use from cost and logistical standpoints. Subjective measures are convenient and inexpensive,30,31 and some, such as the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS),9,30 have been adopted and adapted for use in CML patients treated with TKIs.9,32-34 While the BAASIS discriminated clinically between adherent and non-adherent subjects with respect to clinical outcomes, further work is needed to assess its diagnostic accuracy.9 Indeed, there is a paucity of direct comparisons between subjective and objective measures in CML, and the diagnostic accuracy of these measures has not been assessed in this setting.32,35

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Accurate data on NA correlates is important because these can be used to select patients in need of closer monitoring, and if modifiable, they could be targeted by interventions. A recent literature review36 identified correlates of TKI NA across the 5 World Health Organization (WHO) domains,37 mostly using prescription data12,38 or clinician- or patient-reported outcomes.33,39,40 Examples of patient factors associated with NA are lack of knowledge on the impact of their disease and treatment9 and low self-efficacy,9 such as forgetfulness.32,41 Disease and treatment factors identified include longer disease duration9 and higher rates of adverse effects.10 Among the sociodemographic factors are living alone9 and low levels of social support,18 while higher medication copayment12,38 is an important health care system factor correlated with TKI NA. In addition, there are conflicting reports on the association between adherence and age,9,10,40 gender,9,42 TKI treatment duration,9,43 type of TKI,44,45 number of concomitant medications,9,18,40,43 and quality of life.9,16 Despite the abundance of studies on adherence risk factors in CML, only one prior cohort evaluated risk factors for EM NA.10,32 Nevertheless, this was a qualitative but not a quantitative substudy. Moreover, only a handful of studies specify the phase of adherence evaluated.12,15 Thus, there is still a knowledge gap regarding quantifiable correlates of EM TKI NA across the 3 phases of adherence.15 The aim of this study was to provide data needed to identify lack of adherence to TKIs by evaluating prevalence and correlates of TKI implementation NA in CML and testing the diagnostic accuracy of subjective adherence measures against EM TKI implementation adherence.

Patients and Methods Design This study is a part of a multiphase adherence research program called The Effect of Adherence-Enhancing Interventions on Adherence to Tyrosine Kinase Inhibitor Treatment in Chronic Myeloid Leukemia (TAKE-IT), which applied a hybrid design. Pertinent details of the TAKE-IT study design are described in brief below and are reported in full in the study protocol. As can be inferred from Figure 1, the current study focused on the 4-month preintervention period (V1-5). During this period, patients had TKI adherence measured with EM while receiving usual care with respect to adherence management. Prevalence and correlates of TKI implementation NA (V2-5) were assessed, as was the diagnostic accuracy of subjective adherence measures (V1-5). The results of the intervention phase of the TAKE-IT study have been recently published.46 The trial was registered at ClinicalTrials.gov (NCT01768689).

Sample and Setting A convenience sample of adult CML patients was prospectively enrolled at the hematology outpatient clinics of 4 academic university hospitals across Israel. Patients aged 18 years or older who had received at least 3 months of imatinib, nilotinib, or dasatinib for chronic-phase CML47 were eligible for the study. Basic health care insurance is compulsory in Israel, and thus essentially all citizens have access to care at hematology clinics without additional costs. Furthermore, TKIs are fully subsidized.

Avi Leader et al Figure 1 Study Design. Various Adherence Measures Were Utilized. Timing of Their Use (Top) Indicated Along Preintervention TAKE-IT Substudy Timeline. Also Shown are Different Study Periods Analyzed in Main Study Analyses (Bottom). aArrows Denote Measurement Points; Adjacent Gray Boxes Denote Recall Periods. Patient Self-Reported Adherence was Measured by BAASIS, Which has a 30-Day Recall. Clinician’s Collateral Report was Measured by Visual Analog Scale and Reflected Hematologist’s Estimation of Adherence Since Previous Clinic Visit (Usually 30 to 90 Days Earlier). bAdherence During Run-In Period (V1-V2) was Not Included in Analysis of Adherence Prevalence and Correlates. cEM was Used as Reference Measure of Adherence

Continuous electronic monitoring using MEMS®

Adherence Measurement: Patient self-report and clinician collateral report a

0 Months

1 Month

7 Months

4 Months

Usual adherence care Run-in b V1

Intervention

Baseline V2

V3

V4

Study index

V5 V6

V7

V8

V9

V10

End of study Pre-intervention sub-study Adherence prevalence & correlates

Analysis Periods:

Diagnostic accuracy of collateral and self-report c

Abbreviations: BAASIS ¼ Basel Assessment of Adherence to Immunosuppressive Medications Scale; EM ¼ electronic monitoring; MEMS ¼ Medical Events Monitoring System 6.

Usual (nonstudy) adherence monitoring and adherence intervention were similar in all the participating centers.46 Physicians used a crude collateral assessment to monitor adherence; no standard patient self-report measure was used. Monitoring was the sole responsibility of the hematologist, although nurses and social workers also participated in interventions. The AEIs used in usual care were not standardized and were based on change in dose or type of TKI to alleviate adverse effects, as well as patient education.

Compliance With Ethical Standards The study protocol was approved by the institutional review boards at all study centers. Informed consent was obtained from all individual participants included in the study before inclusion. This study was conducted in accordance with the Declaration of Helsinki.

reference measure of TKI adherence and was monitored continuously throughout the 4 study months (V1-5; Figure 1). A daily binary variable based on EM dosing histories was used to indicate whether the drug was taken as prescribed on a given day (¼ 1) or not (¼ 0), irrespective of timing. Period implementation adherence was derived from these indicators and expressed as the percentage of days when the drug was taken as prescribed (ie, proportion of 1). Implementation adherence was further divided into run-in (V1-2) and baseline (V2-5) periods. Discontinuation was defined as no medication taken for at least 2 consecutive weeks. Throughout, unless otherwise specified, “adherence” refers to implementation only.15

At study index (V1), information on selected sociodemographic, patient, treatment, condition, and health care systemerelated factors was obtained via interview and review of medical records. The variables collected are shown in Table 1, which cites prior studies justifying the choice of each variable as a potential correlate for NA. The implementation and persistence phases of TKI adherence were assessed using 3 methods (Figure 1), as detailed below.

TKI Adherence Self-report. Self-report TKI implementation adherence was measured by the BAASIS at 2 time points (V2, V5) corresponding with run-in and baseline adherence (Figure 1). BAASIS is a 4-item patient-reported outcome developed to assess adherence to immunosuppression in solid organ transplants30 that has been adapted and used in CML.9 The 3 yeseno primary items measuring implementation were considered in this study, while the frequency and drug holiday subitems were not. These 3 items assess dosing, timing, and dose reduction during the previous 4 weeks.46 BAASIS implementation NA was defined as any deviation from the dosing regimen, meaning a positive response to any of the items.

Electronic Monitoring. EM, using the Medical Events Monitoring System 6 (MEMS; AARDEX Group, Belgium/Switzerland), was the

TKI Adherence Clinician Collateral Report. The collateral report of adherence by the patient’s hematologist used a visual analog scale

Variables and Measurement

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4

Association With TKI Implementation NA in CMLb (N [ 55)

Citation With Prior Evidencec

WHO Classification of Risk Factors

< .01

9, 10

Sociodemographic

Adherence in women is 4.5% higher compared to men (1.9% lower to 10.8% higher)

.17

9, 42

In patients of Arab ethnicity, adherence is 13.2% lower (4.9% to 21.5% lower)

< .01

4 (7)

Patients with less than secondary education are 88.7% adherent (77.2% to 100%)

.74

Secondary

28 (49)

Patients with secondary education are 93.5% adherent (89.1% to 97.9%)

Tertiary

25 (44)

Patients with tertiary education are 92.8% adherent (88.1% to 97.4%)

No

7 (12)

Adherence is 12.4% higher in patients having a relationship (3.8% to 21% higher)

< .01

Yes

50 (88) Adherence is 8.4% lower in patients living alone (16.8% to 0 lower)

.05

Adherence is 8.5% higher in members of CML support group (2.8% to 14.2% higher)

< .01

Sample, N (%) (N [ 58)

Estimated Adherencea,g (95% CI)

P

60.5 [19]

Increase of 10 years in age is associated with increase of 3% in adherence (0.8% to 5.1% increase)

40 (69)/18 (31)

Arab

9 (16)

Other

49 (84)

Baseline Characteristics Aged (years), median [IQR] Male/femaled d

Ethnicity

Educationd (n ¼ 57) Less than secondary

9

In a relationshipe (n ¼ 57)

Living alonee (n ¼ 57) No

49 (86)

Yes

8 (14)

9

Member of CML support groupe (n ¼ 57) No

29 (51)

Yes

28 (49)

Comorbiditiesd

Patient-related

Diabetes mellitus

7 (12)

Adherence is 2.1% higher in diabetic patients (7% lower to 11.2% higher)

.65

Ischemic heart disease

6 (12)

Adherence is 6% lower in patients with ischemic heart disease (3.7% lower to 15.6% higher)

.23

Hypertension

11 (19)

Adherence is 0.7% higher in hypertensive patients (6.9% lower to 8.2% higher)

.87

Other

23 (40)

Adherence is 5.5% higher in patients with other comorbidities (0.4% lower to 11.5% higher)

.07

No

34 (60)

Adherence is 6.1% higher in patients with prior education on adherence (0% to 12.1% higher)

.05

Yes

23 (40) Adherence is 2.4% higher in patients with performance status 1 (5.1% lower to 10% higher)

.53

39

Prior education on adherence in CMLe (n ¼ 57)

ECOG performance statusd 0

47 (81)

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Clinical Lymphoma, Myeloma & Leukemia Month 2018

Table 1 Baseline Characteristics and Risk Factors for TKI Implementation NA in CMLa

Table 1 Continued

Baseline Characteristics 1

Sample, N (%) (N [ 58)

Association With TKI Implementation NA in CMLb (N [ 55) Estimated Adherencea,g (95% CI)

P

Citation With Prior Evidencec

WHO Classification of Risk Factors

11 (19) d

Condition-related

Stage at diagnosis Chronic

Prior disease durationd (months), median [IQR]

58 (100)

Not applicable

43 [81]

increase of 5 years in disease duration is associated with increase of 3.7% in adherence (0.4% to 6.9% increase)

.03

.69

9

Sokal risk scored,f (n ¼ 39) Low

19 (49)

Patients with low Sokal score are 93.4% adherent (89.5% to 97.3%)

Intermediate

16 (41)

Patients with intermediate Sokal score are 95.6% adherent (91.5% to 99.6%)

High

4 (10)

Patients with high Sokal score are 96.2% adherent (88.2% to 100%)

Imatinib

33 (57)

Adherence is 15% lower in patients treated with nilotinib (8.2% to 21.8% lower)

< .01

44, 45

Dasatinib

13 (22)

Nilotinib

12 (21) Adherence is 13.2% lower in patients with twice-daily regimen (6.5% to 19.9% lower)

< .01

40

Adherence is 13.2% lower in patients with third-line treatment, compared to other lines (3.3 to 23.2% lower)

.01

34 [60]

Increase of 1 year in TKI treatment duration is associated with increase of 0.7% in adherence (0% to 1.5% increase)

.05

9, 43

Any

41 (71)

Presence of any of following adverse effects is associated with increase of 2% in adherence (4.7% decrease to 8.6% increase)

.56

10

Fatigue

16 (28)

Fatigue is associated with decrease of 1.8% in adherence (5% decrease to 8.6% increase)

.60

Nausea or vomiting

15 (26)

Nausea or vomiting is associated with decrease of 0.1% in adherence (6.7% decrease to 6.9% increase)

.97

5 (9)

Diarrhea is associated with increase of 4.3% in adherence (6.3% decrease to 14.8% increase)

.43

10 (17)

Rash is associated with increase of 3.1% in adherence (4.7% decrease to 11.0% increase)

.44

Type of TKI treatmente

Treatment-related

TKI dosing schedulee Once daily

45 (78)

Twice daily

13 (22)

Line of TKI treatmentd First

36 (62)

Second

17 (29)

Third

5 (9)

TKI-associated adverse effects of any gradee

Diarrhea Rash

Avi Leader et al

Clinical Lymphoma, Myeloma & Leukemia Month 2018

TKI treatment durationd (months), median [IQR]

-5

6

Baseline Characteristics

Sample, N (%) (N [ 58)

Association With TKI Implementation NA in CMLb (N [ 55) Estimated Adherencea,g (95% CI)

P

Headache

4 (7)

Headache is associated with a decrease of 9% in adherence (2.4% decrease to 20.5% increase)

.13

Leg edema

3 (5)

Leg edema is associated with increase of 6.8% in adherence (6.5% decrease to 20.0% increase)

.32

Periorbital Edema

9 (16)

Periorbital edema is associated with increase of 5.7% in adherence (2.4% decrease to 13.7% increase)

.18

Myalgia

28 (48)

Myalgia is associated with increase of 1.9% in adherence (4.2% decrease to 7.9% increase)

.55

16 [16]

Increase of 5 years in professional experience is associated with increase of 1.2% in adherence (0.9% decrease to 3.3% increase)

.28

1-5

8 (47)

Not applicable

6-10

4 (23)

11-20

3 (18)

> 20

2 (12)

Hematologist: professional experiencee (years), median [IQR] (n ¼ 17)

Citation With Prior Evidencec

WHO Classification of Risk Factors

9

Health care system-related

Hematologist: CML patients seen in previous year (n ¼ 17)

Study centere Center 2

12 (21)

Center 1

31 (53)

Center 3

10 (17)

Center 4

5 (9)

Adherence is 11.4% lower in patients treated in center 2 (4.7% to 18.1% lower)

< .01

Abbreviations: CI ¼ confidence interval; CML ¼ chronic myeloid leukemia; ECOG ¼ Eastern Cooperative Oncology Group; IQR ¼ interquartile range; NA ¼ nonadherence; TKI ¼ tyrosine kinase inhibitor; WHO ¼ World Health Organization. a Single regression of correlates of TKI implementation NA was performed. b Single regression was performed on 55 patients with electronically measured adherence during preintervention period and data on covariates. c Citation numbers refer to prior studies justifying selection of this variable as potential risk factor for TKI NA in CML. d This is a nonmodifiable risk factor for NA. e This is a modifiable risk factor for NA. f Sokal risk score is a prognostic discriminator in CML. g Adherence was defined as percentage of days with medication taken as prescribed.

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Clinical Lymphoma, Myeloma & Leukemia Month 2018

Table 1 Continued

Avi Leader et al Figure 2 Patient Flow Using CONSORT Guidelines

Enrollment

Eligibility (n=294)

Excluded (n=236) Declined to participate (n=30) ♦ Not approached (206) during convenience sampling ♦

Enrolled (n=58)

Analysis of baseline adherence prevalence and correlates Analysed (n=55) Excluded from analysis (n=3) o No electronically measured adherence data available, due to early (within 30 days) withdrawal of consent (n=2) o Missing data on covariates (n=1)

♦

(VAS) ranging from 0 (none of the prescribed doses taken) to 10 (all doses taken), making no distinction between phases of adherence. A score of less than 10 was defined as NA. This scale was measured at the same time points as the BAASIS (Figure 1).

Study Procedures Data collection procedures are detailed in the study protocol.46 In brief, data were collected from October 2013 until June 2015. Measures were taken to ensure fidelity of EM data, such as monthly tests to identify MEMS technical issues. Patients were aware that adherence was being monitored but were unaware of the EM adherence data during this study. The hematologists were unaware of the EM and BAASIS data.

Diagnostic Accuracy of Adherence Measures. The diagnostic accuracy of BAASIS and collateral report was evaluated using EM adherence below 95% as the NA reference, equivalent to  2 missed daily doses per month. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the BAASIS and collateral report. EM data corresponding with the 1month BAASIS recall periods (Figure 1) was used for this comparison. The paired EM-BAASIS measurements from the 2 time points (run-in, baseline) were grouped together for this analysis, as patients received the same usual care at both time points. A sensitivity analysis without the baseline values was planned to rule out changes over time. The same applied for the EM-VAS pairings. Analyses were performed by SAS/STAT 9.3 software (SAS Institute, Cary, NC). Significance was set at P < .05.

Data Analysis Prevalence and Correlates of NA. TKI adherence was measured as the percentage of days when the drug was taken as prescribed according to EM data. Linear models were used to analyze baseline EM adherence (V2-5). Correlates of baseline EM adherence were identified using a simple linear model, considering all baseline characteristics (Table 1) as covariates. Covariates were then entered into a multiple stepwise regression model.46 As shown in Figure 1, adherence during the run-in period was not included in these analyses, to account for the intervention effect of EM.48

Results Sample Characteristics Fifty-eight chronic-phase CML patients were enrolled between October 2013 and August 2014 from an eligible population of 294 patients. Baseline EM adherence and data on covariates were available for 55 patients (Figure 2). Baseline characteristics of the sample are shown in Table 1 and are categorized as potential risk factors of NA using the WHO classification.37 The median patient age was 60.5 years (interquartile range [IQR] ¼ 19), 40 were men (69%), the

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Identifying Nonadherence in CML Table 2 Multiple Regression of Adherence Correlates With Adherence as Response Model Parameters

Adherence Estimate (95% CI)

Intercept (ie, adherence without any factors listed below)

99.2% (95.3 to 100)

Differential effect of selected factors on adherence Living alone

8.1% (13.4 to 2.8)

Lack of membership in CML support group

8.0% (15.5 to 0.6)

Receiving third-line treatment

11.9% (21.0 to 2.7)

Adherence was defined as electronically measured TKI implementation adherence, reported as percentage of days when drug was taken as prescribed. Abbreviations: CI ¼ confidence interval; CML ¼ chronic myeloid leukemia; TKI ¼ tyrosine kinase inhibitor.

alone (P < .05), and third-line TKI treatment (P < .05) were associated with a statistically significant decrease in baseline adherence. The estimated reduction in adherence with each of these correlates is shown in Table 2. The estimated EM adherence was 99.2% (95% confidence interval, 95.3-100) in the 21 patients (38%) with none of the above risk factors.

median prior disease duration was 43 months (IQR ¼ 81), and the majority (93%) had experienced optimal response according to European LeukemiaNet milestones.47 Imatinib was the most common TKI used (n ¼ 33), followed by dasatinib (n ¼ 13) and nilotinib (n ¼ 12). Forty-one patients (71%) had previously experienced at least one TKI-associated adverse effect of any grade (Table 1). Seventeen hematologists with a median experience of 16 years (IQR ¼ 16) caring for varying numbers of CML patients (Table 1) were involved in the routine care of the study subjects. The average baseline EM adherence (n ¼ 55) was 93% (95% confidence interval, 90-96.1). The median adherence was 97.5% (range, 48-100%; IQR ¼ 7.2%) while the 10th and 25th percentiles were 82.6 % and 92.1%, respectively. There was no early TKI discontinuation, and thus the persistence was 100%.

Testing Diagnostic Value of Self-Report and Collateral Report Out of a potential 116 sets, 98 BAASIS questionnaires (85%) and 91 clinician collateral reports (78%) were available from the run-in and baseline measurements, with missingness increasing over time. The median collateral report VAS was 9 (range, 2-10) on a scale of 0 to 10. The diagnostic accuracy analysis was performed for the 94 BAASIS self-reports (81%) and 89 collateral reports (77%), which could be linked with the corresponding 1-month period of EM adherence. The detailed performance of these adherence measures in diagnosing TKI NA (ie, EM adherence < 95%) is shown in Table 3. The BAASIS self-report had a sensitivity of 67% and a specificity of 71% for diagnosing NA, while the clinician collateral report had a comparable sensitivity (78%) but lower specificity (42%; Table 3).

Correlates of NA On simple linear regression analysis, a number of factors representing different WHO risk factor categories were identified as statistically significant correlates of TKI NA (Table 1). The estimated adherence was between 3% and 15% lower in the presence of one of these factors (Table 1). Multiple regression analysis revealed that lack of membership in a CML patient support group (P < .005), living

Table 3 Diagnostic Accuracy of Self-Report (BAASIS) and Clinician Collateral Report in Diagnosing Implementation NAa NAa Positive, N (26%)

Negative, N (74%)

BAASIS Self-Report Measure BAASIS self-report NA (n ¼ 94)b,c Positive, n (38%)

16 (TP)

20 (FP)

PPV ¼ 44%

Negative, n (62%)

8 (FN)

50 (TN)

NPV ¼ 86%

Sensitivity ¼ 67%

Specificity ¼ 71%

Clinician Collateral Report Clinician report NA (n ¼ 89)c,d Positive, n (63%)

18 (TP)

38 (FP)

PPV ¼ 32%

Negative, n (37%)

5 (FN)

28 (TN)

NPV ¼ 85%

Sensitivity ¼ 78%

Specificity ¼ 42%

EM NA used as reference measure. Abbreviations: BAASIS ¼ Basel Assessment of Adherence to Immunosuppressive Medications Scale; EM ¼ electronic monitoring; FN ¼ false negative; FP ¼ false positive; NA ¼ nonadherence; NPV ¼ negative predictive value; PPV ¼ positive predictive value; TN ¼ true negative; TP ¼ true positive. a NA (implementation phase) was condition tested, and adherence was expressed as percentage of days with drug taken as prescribed. NA was defined as EM implementation adherence < 95% during corresponding 1-month periods before BAASIS or collateral report. b Operational definition of BAASIS self-report implementation NA was any deviation from dosing schedule, meaning a positive answer to any of 3 implementation items. c Both baseline and preintervention measurements were considered in this analysis. d Operational definition of collateral report NA was a score of less than 10 on a scale from 0 (none of prescribed doses taken) to 10 (all taken). Scale scored by patients’ hematologists.

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Avi Leader et al A sensitivity analysis excluding adherence measures from the second time point (ie, baseline; V5) showed the same sensitivity (67%) and almost identical specificity (72%) for the BAASIS selfreport. In contrast, the clinician collateral report showed large variations in sensitivity between the first measurement (ie, run-in; V2) and the second one taken 3 months later (91% and 67%, respectively), while specificity was similarly low at both points (data not shown).

Discussion This prospective study of TKI adherence in 58 CML patients is the first study to assess correlates of TKI NA using EM and to test the diagnostic accuracy of subjective measures against EM in CML. EM TKI implementation adherence was high yet showed large variability, whereby over 50% had near-perfect adherence (> 97.5%) but 25% had adherence below 92%, confirming similar data from a prior EM adherence study in CML.10 This proportion of suboptimal adherence is important because outcomes are adversely affected by EM adherence below 90%,10 and probably by any degree of NA.25 These results emphasize the need to identify the nonadherent group in order to avoid intervening in a large group of those with optimal adherence. The disparate adherence groups could potentially be identified by monitoring adherence with readily available tools while focusing on patients with risk factors for NA, both of which were assessed in this pilot study. In addition, there was no early TKI discontinuation, showing that implementation is the main driver of NA in this sample, and not so much persistence. Admittedly we only monitored EM adherence over 3 months, which might limit the chance to assess discontinuation over time in our CML sample. Given that persistence has been shown to drive NA in nonmalignant chronic disorders,49 both aspects of adherence management should be incorporated in adherence programs in the future. The correlates of EM implementation NA identified on multiple regression analysis were living alone, third-line therapy, and lack of membership in a CML support group. Patients with these factors had estimated adherence 8% to 11.9% lower than patients without any of these factors. These could theoretically be used to select patients in need of closer monitoring, thus underlining the potential importance of these findings. Living alone has been previously associated with TKI NA in CML.9 Third-line therapy may in theory be a surrogate of previous NA to therapy, which contributed to disease progression,10 because physicians may change therapy in the absence of clinical effect whereas the real reason for treatment failure is undetected NA. This underlines the importance of addressing NA when progressing to another line of therapy. Lack of membership in a patient support group as a risk factor for NA is an interesting and novel finding that can theoretically be linked to low levels of social support, which has already been associated with TKI NA.18 This is because the CML support group may have an intervention effect via informational, social, and other support. Alternatively, this could simply be a marker of NA because nonadherent patients may be less likely to join support groups. Several predictors, some of which were previously reported,9,40 were identified in the single regression model (Table 1) but did not remain in the multiple regression model. This may be explained by an insufficient sample size but also by multicollinearity (ie, correlation between predictors). For instance, most patients of Arab

ethnicity and those treated in center 2 were not members of a patient support group, while most patients who had received prior education on adherence9 were part of a support group. Furthermore, increasing age tended to be associated with not living alone, while increased disease duration9 correlated with support group membership (data not shown). In addition, there was a fixed effect for center that disappeared after stepwise analysis, suggesting that this may be a proxy for other covariates. Some inconsistencies with previously reported NA correlates may stem from varying settings,9,16 differences in measurement,26 and lack of differentiation between the 3 adherence components15 in prior studies. To address the aspect of adherence monitoring, a preplanned exploratory analysis of the diagnostic accuracy of the BAASIS selfreport and VAS-based clinician collateral report was performed. BAASIS self-report had moderate sensitivity (67%) and specificity (71%), and high negative predictive value (86%) in diagnosing TKI NA. Importantly, the BAASIS’s performance was identical at 2 time points (3 months apart), thus supporting reliability of this measure. The clinician-reported VAS, on the other hand, had comparable sensitivity but considerably lower specificity. Moreover, the variation in diagnostic accuracy over time raises questions about the reliability of this measure. This was not influenced by interrater variability because the same physicians filled in both assessments in a given patient. Thus, in this analysis, BAASIS, compared to physician-reported VAS, appears to be the better option, as it is more consistent and could result in less unnecessary AEIs (given to false-positive patients). This is a significant finding because crude clinician collateral assessment was a dominant component of routine (out of study) adherence assessment in CML patients at the 4 study centers, while no standard self-report measure was used.46 This probably holds true for additional centers in Israel and worldwide, bearing in mind that there is neither hard evidence nor recommendations on adherence assessment in this population. Importantly, the “2 or more pills missed per month” cutoff for defining reference NA using EM was chosen to facilitate implementation of the AEI assessed in the intervention phase of the TAKEIT study in routine care, if found effective. The TAKE-IT AEI uses a similar threshold for selecting nonadherent patients in need of intervention.46 This clinically meaningful high EM threshold for adherence is comparable to the operational definitions used to dichotomize BAASIS and clinician-reported VAS adherence in that it does not allow for any margin of NA.10,25 It must be noted that BAASIS measures additional adherence components, such as timing, that were not captured by EM in our study. Nonetheless, all 3 implementation-related items in the BAASIS were considered as we hypothesized that the other items, such as correct timing, may be correlated with implementation adherence, thus improving the sensitivity. Prior publications comparing subjective measures with objective adherence in CML are scarce.32,35 We are aware of only a single study in which a patient self-report question was used to assess adherence of a subgroup of 21 CML patients who had TKI EM adherence previously measured.32 All of the 4 EM-adherent patients were identified as adherent, whereas only 47% of the EMnonadherent patients were identified as nonadherent. A retrospective analysis of the diagnostic value of a 10-item patient questionnaire using medication possession ratio < 90% as a NA reference

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demonstrated a 50% sensitivity and 97% specificity using the optimal cutoff score of < 8.35 Despite their shortcomings, there is a need for integration of subjective adherence measures in routine care. Because TKI NA can lead to severe consequences,10 we propose that such a measure should have a negligible false-negative rate, unlike that seen in the above-mentioned studies. This is especially true if it is used to decide who receives an AEI. It is therefore useful to discuss the BAASIS self-report in the context of its potential use in this setting, focusing on the patients with false-negative and false-positive results and the consequences of misdiagnosis. For every 100 patients assessed, 38 would be deemed nonadherent by BAASIS and receive an AEI. Twenty-one of these patients would in fact be adherent (ie, false-positive result), meaning that they received an intervention that they did not need. This may, at most, lead to resource wasting and patienteprovider mistrust. On the other hand, 9 of the 62 subjects diagnosed as adherent would in fact be nonadherent (ie, false-negative result) and would be at risk for adverse outcomes if no other measures are taken.10 Accordingly, this false-negative rate is suboptimal, and future work needs to be done to reduce it. One possibility is to optimize adherence measurement by combining adherence measures50 such as self-report and medication refill data. In this study, the unreliability of the clinician collateral report precluded the testing of the combined diagnostic value together with the BAASIS. Another theoretical approach is incorporating NA correlates such as those identified in this study into the management strategy, whereby patients with a negative BAASIS but presence of NA risk factors would receive the AEI. Because patients with no risk factors had near-perfect estimated EM adherence (Table 2), this has the potential to reduce the false-negative rate. It must be emphasized that the adherence measures’ diagnostic accuracy analysis is exploratory and hypothesis generating, and is limited by sample size, missingness, and repeated measures in the same patient. As such, it should be considered as a feasibility project and part of a learning process. Thus, this proposed BAASIS-based strategy for identifying TKI NA warrants evaluation in prospective management studies considering adherence as the fifth vital sign. Nevertheless, given the lack of a proven strategy for identifying TKI NA in CML, we argue that already using this strategy on the basis of exploratory data in CML is more reasonable than the current heterogeneous practice, which is not based on any published data in this field. This study has a number of strengths. First and foremost, EM was used as the reference adherence measure for all analyses. EM provides superior sensitivity28,29 and correlates better with clinically relevant outcomes in CML,10 although it remains an indirect measure. Second, in contrast with previous comparisons between subjective and objective measures, all measures were measured prospectively and simultaneously.32,35 Third, we estimated the quantitative effect of each risk factor on adherence. The data derived from this novel analysis may help in quantifying risk in an individual patient and for planning future studies. Finally, adherence is described using the ABC taxonomy, thus increasing clarity and improving the ability to correctly interpret the data.15 There are several limitations that warrant discussion. First and foremost, the sample size was small. Therefore, the study may not

Clinical Lymphoma, Myeloma & Leukemia Month 2018

have been sufficiently powered to identify some associations between baselines variables and adherence. Second, selection bias may affect internal and external validity because the study population may be more adherent or less adherent than other patient populations, especially in view of the convenience sampling strategy that we used (20% of potentially eligible patients were included) and the exclusion of patients who did not understand Hebrew.46 Nevertheless, this cohort represented 7% of the roughly estimated 850 CML patients in Israel at that time, and was performed in 4 centers representing different sociodemographic areas across 3 geographic regions. Third, the findings may not be generalizable outside the context of the Israeli health care system. Specifically, lack of cost-sharing subsidies as a NA risk factor38 could not be assessed because TKIs are universally subsidized in Israel. Last, some potential NA risk factors were not evaluated in this study, especially at the provider and organizational levels,37 which are underrepresented; in addition, the timing aspect of adherence was not evaluated either.32

Conclusion Median EM TKI implementation adherence is remarkably high, but a quarter of patients have potentially clinically meaningful NA. NA is associated with lack of membership in a CML support group, third-line therapy, and living alone. BAASIS self-report is a promising adherence measure in this context, while clinician-reported VAS should not be relied upon. This study provides a solid basis for identifying patients in daily clinical practice who can be targeted for interventions.

Clinical Practice Points  Identifying NA to TKIs in CML is crucial because NA is asso-











ciated with adverse outcomes. This is a challenge because of inconsistencies in reports on NA risk factors, while the diagnostic accuracy of subjective adherence measures is unknown. Median EM implementation adherence (defined as percentage of days with correct dosing) was 97.5% while the 25th percentile was 92.1%. Lack of membership in a CML patient support group, living alone, and third-line treatment were associated with an 8% to 11.9% decrease in adherence (P < .05, multiple regression). Patients with no risk factors had 99.2% adherence. The BAASIS self-report had 67% sensitivity and 71% specificity for diagnosing NA, using EM adherence < 95% as the reference. The clinician collateral report had a sensitivity of 78% and specificity of 42%. There is therefore a significant subgroup of nonadherent patients that needs to be identified, while the remainder have optimal implementation adherence. Patients with study risk factors should be monitored closely for NA. Because adherence measures are used to determine who needs an adherence intervention, we hypothesize that the false-negative rate should be negligible. BAASIS self-report is a promising adherence measure in this context, while clinician collateral report should not be relied upon. The BAASIS could be combined with other adherence measures and/or NA risk factors to improve diagnostic accuracy. The BAASIS and the NA risk factors found in this study provide a basis for identifying nonadherent patients who can be targeted by interventions.

Avi Leader et al Acknowledgments The authors thank the Israel Society of Hematology and Transfusion Medicine for a donating research grant, the Foundation of the Clalit Research Institute for Health Policy Planning (Clalit Health Services) for donating a research grant, and Novartis for research funding. Supported in part by a research grant from the Israel Society of Hematology and Transfusion Medicine; a research grant from the Foundation of the Clalit Research Institute for Health Policy Planning (Clalit Health Services, Israel); and research funding from Novartis, Israel. The funding sources had no involvement in any aspects of the study, including the following: study design, collection, analysis or interpretation of data; the writing of the report; the decision to submit the report for publication.

Disclosure P.R. served on advisory boards for Pfizer, Novartis, Medison, BMS. G.S. served on patient advocate advisory boards for Pfizer, Novartis, Roche, Abbvie, BMS, Takeda, Incyte, received honoraria from Pfizer and Novartis, and chairs the Israeli patient organization, which receives unconditional grants from Novartis, Pfizer, Medison, and BMS. E.T. is an employee of AARDEX group, Visé, Belgium. The other authors have stated that they have no conflict of interest.

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