End-of-Life Symptom Burden among Patients with Cancer Who Were Provided Medical Assistance in Dying (MAID): A Longitudinal Propensity-Score-Matched Cohort Study.
K Brooke RussellCaitlin ForbesSiwei QiClaire LinkLinda WatsonAndrea DeiureShuang LuJames SilviusBrian J KellyBarry D BultzFiona S M SchultePublished in: Cancers (2024)
Cancer is the primary underlying condition for most Canadians who are provided Medical Assistance in Dying (MAID). However, it is unknown whether cancer patients who are provided MAID experience disproportionally higher symptom burden compared to those who are not provided MAID. Thus, we used a propensity-score-matched cohort design to evaluate longitudinal symptom trajectories over the last 12 months of patients' lives, comparing cancer patients in Alberta who were and were not provided MAID. We utilized routinely collected retrospective Patient-Reported Outcomes (PROs) data from the Edmonton Symptom Assessment System (ESAS-r) reported by Albertans with cancer who died between July 2017 and January 2019. The data were analyzed using mixed-effect models for repeated measures to compare differences in symptom trajectories between the cohorts over time. Both cohorts experienced increasing severity in all symptoms in the year prior to death (β from 0.086 to 0.231, p ≤ .001 to .002). Those in the MAID cohort reported significantly greater anxiety (β = -0.831, p = .044) and greater lack of appetite (β = -0.934, p = .039) compared to those in the non-MAID cohort. The majority (65.8%) of patients who received MAID submitted their request for MAID within one month of their death. Overall, the MAID patients did not experience disproportionally higher symptom burden. These results emphasize opportunities to address patient suffering for all patients with cancer through routine collection of PROs as well as targeted and early palliative approaches to care.
Keyphrases
- patient reported outcomes
- end stage renal disease
- patient reported
- palliative care
- healthcare
- chronic kidney disease
- ejection fraction
- newly diagnosed
- depressive symptoms
- peritoneal dialysis
- electronic health record
- risk factors
- machine learning
- drug delivery
- quality improvement
- physical activity
- sleep quality
- data analysis