Abstract
Abstract 4471
Patient-Reported Outcome (PRO) measures help clinicians and researchers monitor symptoms, HRQOL, satisfaction, and adherence related to cancer treatment. Symptoms affect HRQOL, and when both are reported frequently and longitudinally, a patient-reported data stream emerges that reflects physiological functioning and complements traditional laboratory and clinician-based assessments. Such data could significantly enhance risk prediction and safety monitoring in patients undergoing HCT. This study evaluates the feasibility of collecting daily and weekly PRO measurements to inform our ability to capture variation in patient experiences over time.
We enrolled 32 patients undergoing planned HCT (10 autologous, 11 myeloablative allogeneic, 11 reduced intensity allogeneic) in a feasibility study of frequent HRQOL and symptom surveillance following HCT. All surveys were administered electronically though patients could opt for pen and paper. PRO measures were derived from the NIH PROMIS and PRO-CTCAE measures, which have not been previously used extensively or at all in HCT patients. All patients completed a 10-question HRQOL measure (PROMIS-Global Health) and a 34-question symptom measure (a pre-selected subset of the 83-question PRO-CTCAE, with 7-day recall period) prior to HCT and weekly until D+100. Auto patients completed a daily 21-question symptom measure (a pre-selected subset of the weekly symptom surveys, with 24-hour recall period) until hospital discharge, and allo patients completed daily symptom surveys until 100 days after stem cell infusion (D+100). Kruskal-Wallis tests were used to compare groups.
Median age of the sample was 55 years (range 18–70). 16 patients (50%) were female. Most auto patients had myeloma (N=8, 80%) and most allo patients had acute leukemia (16, 72%); other diagnoses included NHL (4), CML, MDS and AA. Twenty-six (81%) patients were Caucasian, 4 (12.5%) were African American, 2 were other (6.2%). Thirteen (41%) had a high school education or lower.
Median daily survey completion percentages prior to hospital discharge for surviving patients were 94% among auto patients, 90% among reduced intensity allo patients and 70% among myeloablative allo patients (p=0.07). Prior to D+100, median daily survey completion percentages were 87% among reduced intensity allo patients and 58% among myeloablative allo patients (p=0.004). Median weekly survey completion percentages prior to hospital discharge were 100% in all cohorts. Prior to D+100, these were 100% in auto and reduced intensity allo cohorts, and 80% among myeloablative allo patients (p=0.002). Daily surveys were completed in a median of 3 minutes, and longer weekly surveys in a median of 4.3 minutes. 93% of respondents were satisfied with survey length and 85% of respondents were satisfied with the electronic self-report system. Median weekly total symptom scores (higher scores indicated greater symptom severity) prior to conditioning were 16 in autos, 12 in myeloablative allos, and 5 in reduced intensity allos (p=0.3) and at D+7 were 23 in autos, 40 in myeloablative allos and 18 in reduced intensity allos (p=0.01). For the physical health subscale of the PROMIS measure (lower scores indicated greater impairment), baseline mean weekly HRQOL scores were 47.7 in autos, 50.8 in myeloablative allos and 50.8 in reduced intensity allos (p=0.9). By D+7, mean HRQOL scores were 37.4 in autos, 37.4 in myeloablative allos and 52.5 in reduced intensity allos (p=0.005).
Frequent symptom and HRQOL surveillance is feasible and acceptable to HCT patients, and survey data correlates with toxicity and physiological function after transplant. Compliance rates were lower in myeloablative allo patients, especially for daily surveys, perhaps reflecting the higher burden of critical illness in this population. Future studies may be enhanced by caregiver-reported proxy data. Analyses of weekly symptom and HRQOL surveys beyond D+7, daily surveys, symptom clusters, biologic correlates and individualized profiles are ongoing. Larger studies are warranted to explore and develop risk prediction models based on this technique.
No relevant conflicts of interest to declare.
Author notes
Asterisk with author names denotes non-ASH members.
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