The humanistic burden of patients with chronic myeloid leukemia (CML) treated with first line (1L) tyrosine kinase inhibitors (TKIs)
Bibliographic record
Abstract
Abstract INTRODUCTION: While TKIs significantly extend survival in CML, their adverse events (AEs) may lead to treatment discontinuation and poor health-related quality of life (HRQoL). Few studies have described the HRQoL of US patients (pts) on TKIs. This study assessed the humanistic burden of pts with CML treated with first-line (1L) TKIs, including their AE profile, HRQoL, and work productivity, using patient-reported outcomes. We also evaluated communication barriers between pts and treating physicians. METHODS: Cross-sectional online surveys were conducted (June-December 2024) among US CML pts. Adults receiving 1L TKIs (imatinib, dasatinib, nilotinib, bosutinib) for ≥3 months (mos) were eligible to participate; asciminib in 1L was not yet approved at study start. AE data were collected via PRO-CTCAE, and pts completed surveys on patient-physician communication about AEs. HRQoL was evaluated using the PROMIS-Global Health-10 (Global Physical Health [GPH] and Global Mental Health [GMH], general population mean±SD of 50±10, lower T-scores=poorer health), and Work Productivity and Activity Impairment: Specific Health Problem (WPAI:SHP, higher percentages=greater impairment) questionnaires. “Low points,” defined as the time(s) when AEs had the greatest impact on HRQoL, were also reported. RESULTS: A cohort of 162 pts (median age 45 yrs [range 18-82], 60% female, 19% non-White) treated with a 1L TKI (42% imatinib, 38% dasatinib, 11% nilotinib, 9% bosutinib) participated. Half were employed (51%; 21% retired, 15% not employed, 6% on disability) and treated in a community-based setting (54%; 41% academic, 5% other/unsure). Two-thirds were commercially insured (65%; 26% Medicare, 7% Medicaid, 2% military/unsure). Over half had been on 1L TKI for ≥1 yr (59%; 11% 3 to <6 mos, 30% 6 mos to <1 yr). In the last 7 days, pts reported a median of 3 AEs (range 0-14); most commonly, fatigue (51%), pain (45%; joint/muscle pain), and gastrointestinal (33%; nausea, diarrhea, vomiting, constipation). Three-quarters (75%) had ≥1 AE, mostly chronic, in the last 7 days. Most pts (82%) experienced low points since TKI start, most commonly in the first 3 mos of treatment (59%), with 24% reporting ≥1 low point in the last 7 days. Nearly all pts (98%) reported discussing AEs at least once with their physician, most frequently at diagnosis (72%) or at subsequent visits (75%); half (54%) discussed AEs at every visit. Two-thirds (64%) reported being satisfied with their discussions about AEs. However, some delayed or did not report AEs to their physicians (17%), for reasons that they “just had to live with it” (70%), were “afraid the doctor may decide to change treatment” (44%), or did not want to “be a burden” (33%). PROMIS-GH-10 revealed pts with CML had worse health than the general population, with mean±SD GPH T-score of 43.2±7.4 and GMH T-score of 43.9±7.6. Among pts with low points in the last 7 days, GPH and GMH T-scores were even worse (GPH: 37.5±6.5; GMH: 38.0±7.4). WPAI SHP showed over half (54%) of pts reported employment change due to CML (12% retired early, 11% from full- to part-time, 10% from full-time to unemployed, 8% stopped working temporarily or reduced workload), with an overall mean activity impairment of 35.1% (range 0-100%). Among those employed, 80% reported work impairment due to CML, with a mean percent work productivity loss of 29.9%. Mean impairment while working (presenteeism) and work time missed (absenteeism) due to CML were 27.2% and 7.6%, respectively. Pts with low points in the last 7 days had even higher mean activity impairment (52.8%) and work productivity loss (38.8%) due to CML. CONCLUSIONS:Our real-world study demonstrates pts with CML treated with 1L TKIs in the US experience chronic AEs contributing to worse HRQoL, including physical and mental health, as well as work impairment. This finding of impaired work productivity due to CML is particularly significant in the context of employer-provided health insurance in the US. While most patients discussed AEs with physicians early in their disease course, only half discussed AEs at every visit and two-thirds were satisfied with their discussions. In addition, some patients delayed or avoided reporting AEs due to internal barriers. As CML requires lifelong treatment, pts may benefit from greater recognition of AEs and strategies to improve HRQoL and maintain work productivity, including TKIs with better tolerability and more consistent approaches to monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".