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Record W4411321377 · doi:10.1002/hon.70096_549

549 | PROSPECTIVE PATIENT PREFERENCE STUDY FOR CHRONIC LYMPHOCYTIC LEUKEMIA TREATMENT ATTRIBUTES IMPACTING PATIENT SHARED‐DECISION MAKING

2025· article· en· W4411321377 on OpenAlexaff
Sikander Ailawadhi, Swetha Challagulla, Dominic Pilon, Todor Totev, Yan Meng, Lilián Díaz, Zhiguo Chen, Kehu Yang

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsGroup for Research in Decision Analysis
FundersBeiGene
KeywordsChronic lymphocytic leukemiaPreferenceMedicineLeukemiaOncologyIntensive care medicineInternal medicineStatistics

Abstract

fetched live from OpenAlex

Introduction: Treatments for CLL differ in efficacy, safety, treatment duration and monitoring needs, all of which can impact overall treatment experience and outcomes. To better understand patient preferences for various treatment attributes, a comprehensive quantitative analysis was conducted. Methods: A patient survey with a discrete choice experiment (DCE) design was conducted from December 2024 to February 2025 among adults (≥ 18 years) from the United States with confirmed diagnosis of CLL, recruited through online patient panels, physician referrals, and support groups. Treatment attributes were selected based on results of a targeted literature review and clinical inputs. Patients responded to DCE questions on attributes related to efficacy (PFS), safety (impacts of diarrhea, headache, atrial fibrillation, hypertension and tumor lysis syndrome [TLS]/kidney dysfunction on quality of life [QoL]) and treatment convenience (treatment duration: continuous vs. fixed duration with monitoring/hospitalization requirements). A conditional logistic regression model was used to calculate the relative importance of each attribute. Results: A total of 199 patients with CLL completed the survey (median age: 60 years; 91% White; 46% female; 66% with a bachelor’s degree or above; 46% employed; 54% commercially insured; 86% suburban/urban residence; 49% diagnosed ≥ 5 years ago). While 30% of patients had received ≥ 3 lines of therapy, 23% of all patients were treatment-naïve, 25% received 1 line of prior therapy, and 23% received 2 lines of prior therapy. Most patients (88%) reported having experienced ≥ 1 AE from treatment previously, with the most common AEs being headache (53%), fatigue (53%), diarrhea (44%), and nausea and/or vomiting (34%). Based on DCE preference results, patients favored treatments with longer PFS and less impact of headache, atrial fibrillation and TLS/kidney dysfunction on QoL (p < 0.001). Impact of diarrhea and hypertension on QoL and treatment convenience did not have a statistically significant influence on treatment preferences. The top 3 treatment attributes with the highest relative importance to patients were PFS (30%), impact of headache on QoL (26%) and impact of atrial fibrillation on QoL (24%), followed by impact of TLS/kidney dysfunction (10%) on QoL, treatment convenience (5%), and impact of diarrhea (4%) and hypertension (1%) on QoL (Figure). Conclusions: This patient preference survey showed that efficacy measured by PFS, the impact of headache, and impact of atrial fibrillation on QoL, were the most important attributes of treatment for patients with CLL. To support patient-centered care, shared decision-making in CLL treatment selection should incorporate a comprehensive discussion on AEs in addition to efficacy endpoints, as patients may prioritize treatments with less impact of AEs on their QoL. Future studies should assess the impact of shared decision-making on treatment adherence and outcomes. Research funding declaration: This study was funded by BeiGene, Ltd. Keywords: quality of life, late effects, survivorship care; patient and family-centered care; chronic lymphocytic leukemia (CLL) Potential sources of conflict of interest: S. Ailawadhi Consultant or advisory role: GSK, Sanofi, BMS, Takeda, Beigene, Pharmacyclics, Amgen, Janssen, Regeneron, Cellectar, Pfizer Other remuneration: Research funding to institution: GSK, BMS, Pharmacyclics, Amgen, Janssen, Cellectar, AbbVie, and Ascentage S. Challagulla Employment or leadership position: BeiGene USA, Inc. Stock ownership: BeiGene USA, Inc. D. Pilon Employment or leadership position: Analysis Group, Inc. T. I. Totev Employment or leadership position: Analysis Group, Inc. Y. Meng Employment or leadership position: Analysis Group, Inc. L. Diaz Employment or leadership position: Analysis Group, Inc. Z. Chen Employment or leadership position: Analysis Group, Inc. K. Yang Employment or leadership position: BeiGene USA, Inc. Stock ownership: BeiGene USA, Inc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.399
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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