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Fatigue in CML patients
the finding that adverse events lead to lower TKI treatment adherence3 and therefore to poorer disease control.4 Although TKI-induced fatigue is one of the most frequently reported adverse effects,5,6 its actual prevalence is unknown because of the heterogeneity in measurement techniques used across studies and it has not been compared to that in the general population. Furthermore, clinicians are unable to identify patients at risk of fatigue since predictors have never been assessed in this specific population of patients. Although a variety of predictors of fatigue, such as gender, age and socioeconomic status have been described in liter- ature,7-9 it is unknown whether these predictors, even when obtained in cancer populations, can be extrapolated to this unique group of CML patients on TKI therapy. Aside from these unmodifiable predictors of fatigue, physical activity has been identified as a modifiable predictor of fatigue in several patient populations.10 The aim of this multicenter observational study was threefold. First, to assess the preva- lence of fatigue in CML patients on TKI therapy compared to that in the general population. Second, to identify predic- tors of fatigue in CML patients. Third, to objectively assess physical activity levels and compare these between fatigued and non-fatigued patients. In this way, we will facilitate the identification of patients at risk of fatigue and provide insight into the association between fatigue and physical activity in the CML population.
Methods
CML patients aged ≥18 years who were receiving TKI therapy were invited to complete an online questionnaire to assess the prevalence and predictors of TKI-induced fatigue (Part 1). Control subjects were selected from a database consisting of over 20,000 subjects without CML who participated in previous research at the Department of Physiology at Radboud University Medical Center (Nijmegen, the Netherlands). Controls were matched for gender and age (±3 years) in a 1:2 ratio to the CML patients. A subgroup of CML patients was asked to wear an activity monitor in order to measure physical activity levels objectively (Part 2). Patients were recruited through the outpatient clinics at the Radboud University Medical Center and Amsterdam University Medical Center (Amsterdam, the Netherlands), and via CMyLife, a Dutch online platform for CML patients.11 Informed consent was obtained from all participants. This study was approved by the Medical Review Ethics Committee region Arnhem-Nijmegen and registered at The Netherlands Trial Registry with numbers NTR7308 (Part 1) and NTR7309 (Part 2).
Part 1: questionnaire
Fatigue severity was measured by the Checklist Individual Strength subscale “subjective experience of fatigue” (CIS-fatigue), which is a validated fatigue questionnaire assessing fatigue over the preceding 2 weeks.12 A score of 35 or above was considered as severe fatigue. The following general characteristics were collect- ed: age, gender, body mass index, education level, and marital sta- tus. Time since CML diagnosis, TKI type and dose, duration of TKI treatment, and disease control (major molecular response, defined as ≤0.1% BCR-ABL transcripts on the International Scale) were collected to assess CML-related medical history. The Charlson Comorbidity Index (CCI)13 was used to quantify participants’ med- ical comorbidities. Both over-the-counter and prescribed medica- tion known to cause fatigue (e.g., benzodiazepines, opioids, β- blockers, and metformin) were assessed. Lastly, potential lifestyle predictors were collected, including smoking, daily fluid and caf-
feine intake, alcohol consumption (beer and wine), and physical activity. Physical activity (defined as Metabolic Equivalent of Task [MET] min/week) was classified into four categories: inactive (<500 MET min/week), moderately active (500-1,499 MET min/week), vigorously active (1,500-2,999 MET min/week), and very vigorously active (>3,000 MET min/week).
Part 2: activity monitor
Physical activity was measured with the activPAL3 micro (PAL Technologies Ltd., Glasgow, UK)14 in a subgroup of 143 CML patients. The sample size calculation was based on data from pre- vious research published on differences in objectively assessed activity levels between fatigued and non-fatigued elderly subjects15 using a power of 80%, with a two-tailed a level of 0.05, an esti- mated effect size of 0.50 and a drop-out rate of 10%. Participants wore the activity monitor 24 hours per day for 7 consecutive days and were asked to maintain normal daily activities. In addition, employment status and total work time were reported. BCR-ABL transcript levels, hemoglobin concentration, white blood cell count and platelet count were extracted from the patients’ electronic records.
Statistical analysis
Continuous data are reported as means ± standard deviation or median (interquartile range [IQR]) and categorical variables as counts and percentages. Logistic regression was performed to identify predictors of severe fatigue. Predictor variables with P val- ues <0.10 in univariable analysis were selected for multivariable logistic regression analysis. Odds ratios (OR) with 95% confidence intervals (95% CI) were calculated to estimate the effect size. The optimal model was selected based on the discriminative ability, assessed by the area under the receiving operating characteristic curve, and calibration slope. Differences in activity patterns were tested using Student t tests for independent samples when data were normally distributed, and Wilcoxon rank sum tests when data were skewed. To correct for potential confounding factors, multivariable linear regression was used. All data were analyzed using SPSS (version 22.0, IBM, Armonk, NY, USA). Statistical sig- nificance was set at a P value <0.05.
Detailed information on the questionnaire and activity monitor is provided in the Online Supplementary Methods S1.
Results
A total of 357 participants were enrolled in the study, consisting of 247 CML patients and 110 controls. Figure 1 shows a schematic flowchart of participants in the two parts of the study. Two-hundred twenty CML patients (58% females, mean age 56 ± 13 years) and 110 gender- and age-matched controls completed the online questionnaire between May 2018 and May 2019 (Part 1). Of these 220 patients, 216 (98.2%) had no missing data and were includ- ed in the multivariable regression analysis. The activity monitor was worn by 143 CML patients, but five patients were excluded from analysis because of an invalid number of days registered by the activity monitor (Part 2).
Part 1: prevalence and predictors of severe fatigue
The prevalence of severe fatigue was 55.5% in the CML patients and 10.9% in the matched controls (P<0.001). Reported QoL was significantly poorer in CML patients than in controls (mean QoL scores 6.9 ± 1.5 and 8.1 ± 1.0, respectively; P<0.001), and also in severely fatigued CML patients when compared to patients without severe fatigue
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