Factors Associated with Early Quality of Life Response to Palliative Care during Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Background: Integrated specialty palliative care (PC) during hematopoietic cell transplantation (HCT) improves quality of life (QOL) and reduces physical and psychological symptom burden compared to usual HCT care. However, limited data exist on factors associated with early QOL response to PC during HCT. Objectives: We aimed to describe the association of patient, clinical, HCT, and PC visit characteristics and early QOL response to PC during HCT hospitalization. We additionally evaluated the association of longitudinal patient-reported QOL and coping and early QOL response to PC. Methods: We conducted secondary analyses of data from two randomized clinical trials of integrated PC and HCT care versus usual HCT care in adult patients with hematologic malignancies undergoing HCT. Patients completed self-reported assessments at the time of HCT admission. We measured patient-reported QOL (Functional Assessment of Cancer Therapy Bone Marrow Transplant [FACT-BMT]: emotional, functional, social, physical, BMT subscales), symptom burden (Edmonton Symptom Assessment Scale), anxiety and depression (Hospital Anxiety and Depression Scale), posttraumatic stress symptoms (PTSD Checklist), and coping (Brief COPE: categorized with composite approach-oriented and avoidant coping) at time of HCT admission, hematologic nadir (week-2), 3- and 6- months post HCT. During hospitalizations, PC clinicians completed weekly surveys documenting PC domains addressed during the PC visits. We defined early QOL response to PC as change in QOL (FACT-BMT Total score) from HCT admission to week-2 and used the median change in QOL in the study sample to define “high” and “low” responders to PC during HCT hospitalization. We used univariate logistic regression to identify patient, clinical, and HCT factors associated with being a high PC responder. We used linear mixed effect models to characterize QOL and coping trajectories by response to PC. Results: This analysis included 252 participants who received the PC intervention and completed study assessments at baseline and week-2. Most participants identified as male (54.8%) and White (78.7%). The most common diagnoses were multiple myeloma (32.3%) and acute leukemia (acute myeloid leukemia and acute lymphoblastic leukemia, 27.1%). 129 (51.2%) participants underwent autologous HCT. Of the 123 participants undergoing allogeneic HCT, 58 (47.2%) received myeloablative conditioning, and 65 (52.3%) received reduced intensity conditioning. The median change in QOL from HCT admission to week-2 was -10.7 (range: -77.0, +52.0). High PC responders (n=126) reported mean improvements in FACT emotional (+2.3 v -0.7, p<0.001), social (+1.1 v -1.2, p<0.001), functional (+1.1 v -6.3, p<0.001), and BMT subscales (+0.8 v -6.9, p<0.001) and a lower decline in FACT physical wellbeing (-1.4 v -10.2, p<0.001) compared to low PC responders (n=126). Minoritized (non-White) race (OR=3.2, 95% CI=[1.7,6.3], p<0.001), lower baseline QOL (OR=0.97, CI=[0.96,0.99], p<0.001), and higher baseline physical symptom burden (OR=1.02, CI=[1.0,1.04], p=0.004) and PTSD symptoms (OR=1.04, CI=[1.01,1.07], p=0.008) were associated with being a high PC responder. Diagnosis, conditioning intensity, and baseline depression and anxiety symptoms were not associated with early response to PC. During HCT hospitalization, high PC responders had earlier and more frequent PC visits focused on coping, HCT education, and symptom education compared to low PC responders based on the PC clinician survey data. There were no differences in QOL at 3 and 6 months post-HCT among high and low PC responders. High PC responders reported higher use of approach-oriented coping at week-2 (mean difference=2.5, CI=[0.9,4.1], p=0.002), 3 months (difference=1.7, CI=[0.1,3.3], p=0.04), and 6 months post-HCT (difference=2.6, CI=[0.8,4.4], p=0.003). Conclusions: Minoritized race and worse pre-HCT QOL and symptom burden were associated with early QOL response during HCT hospitalization. High PC responders had more frequent and earlier PC visits focused on coping and symptom education and maintained higher approach-oriented coping up to six months post-HCT. These findings provide helpful insights into factors associated with early QOL response to PC intervention in HCT and may guide development of future scalable supportive care interventions to improve QOL during HCT hospitalization.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".