Primary data on symptom burden and quality of life among elderly patients at risk of dying during unplanned admissions to an NHS hospital: a cohort study using EuroQoL and the integrated palliative care outcome scale

Abstract Background Older people account heavily for palliative care needs at the population level and are growing in number as the population ages. There is relatively little high-quality data on symptom burden and quality of life, since these data are not routinely collected, and this group are under-recruited in primary research. It is unclear which measurement tools are best suited to capture burdens and experience. Methods We recruited a cohort of 221 patients aged 75 + years with poor prognosis who had an unplanned admission via the emergency department in a large urban hospital in England between 2019 and 2020. Risk of dying was assessed using the CriSTAL tool. We collected primary data and combined these with routine health records. Baseline clinical data and patient reported quality of life outcomes were collected on admission and reassessed within the first 72 h of presentation using two established tools: EQ-5D-5 L, EQ-VAS and the Integrated Palliative Outcomes Scale (IPOS). Results Completion rate was 68% ( n = 151) and 33.1% were known to have died during admission or within 6 months post-discharge. The vast majority (84.8%) reported severe difficulties with at least one dimension of EQ-5D-5 L at baseline and improvements in EQ-VAS observed at reassessment in 51.7%. The baseline IPOS revealed 78.2% of patients rating seven or more items as moderate, severe or overwhelming, but a significant reduction (-3.6, p = 1) and negatively associated with baseline IPOS and CriSTAL scores. Conclusion Older people with poor prognosis admitted to hospital have very high symptom burden compared to population norms, though some improvement following assessment was observed on all measures. These data provide valuable descriptive information on quality of life among a priority population in practice and policy and can be used in future research to identify suitable interventions and model their effects.