PReventing early unplanned hOspital readmission aFter critical ILlnEss (PROFILE): protocol and analysis framework for a mixed methods study
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Date
2016-06-28
Citation
Walsh, T.S., Salisbury, L., Donaghy, E., Ramsay, P., Lee, R., Rattray, J. and Lone, N. (2016) ‘PReventing early unplanned hOspital readmission aFter critical ILlnEss (Profile): protocol and analysis framework for a mixed methods study’, BMJ Open, 6(6), p. e012590. Available at: https://doi.org/10.1136/bmjopen-2016-012590.
Abstract
Introduction: Survivors of critical illness experience
multidimensional disabilities that reduce quality of life,
and 25-30% require unplanned hospital readmission
within 3 months following index hospitalisation. We aim
to understand factors associated with unplanned
readmission; develop a risk model to identify intensive
care unit (ICU) survivors at highest readmission risk;
understand the modifiable and non-modifiable
readmission drivers; and develop a risk assessment tool
for identifying patients and areas for early intervention.
Methods and analysis: We will use mixed methods
with concurrent data collection. Quantitative data will
comprise linked healthcare records for adult Scottish
residents requiring ICU admission (1 January 2000-31
December 2013) who survived to hospital discharge.
The outcome will be unplanned emergency readmission
within 90 days of index hospital discharge. Exposures
will include pre-ICU demographic data, comorbidities
and health status, and critical illness variables
representing illness severity. Regression analyses will
be used to identify factors associated with increased
readmission risk, and to develop and validate a risk
prediction model. Qualitative data will comprise
recorded/transcribed interviews with up to 60 patients
and carers recently experiencing unplanned
readmissions in three health board regions. A deductive
and inductive thematic analysis will be used to identify
factors contributing to readmissions and how they may
interact. Through iterative triangulation of quantitative
and qualitative data, we will develop a construct/
taxonomy that captures reasons and drivers for
unplanned readmission. We will validate and further
refine this in focus groups with patients/carers who
experienced readmissions in six Scottish health board
regions, and in consultation with an independent expert
group. A tool will be developed to screen for ICU
survivors at risk of readmission and inform anticipatory
interventions.