TY - JOUR
T1 - Development and validation of a risk score to predict unplanned hospital readmissions in ICU survivors
T2 - A data linkage study
AU - Pilowsky, Julia K.
AU - von Huben, Amy
AU - Elliott, Rosalind
AU - Roche, Michael A.
N1 - Funding Information:
Ms Pilowsky was supported by an Australian Government Research Training Program Scholarship and a UTS Research Excellence Scholarship . Ms Von Huben is supported by an Australian Government Research Scholarship , The University of Sydney , the School of Public Health , Scholarship in Health Economics (Patient-Centred Care and Outcomes in Chronic Disease), and a National Health and Medical Research Council Medical Research Future Fund Grant ( APP1199902 ). This work was also supported by a grant from the Ramsay Research and Teaching Fund Scheme . The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources.
Publisher Copyright:
© 2023 Australian College of Critical Care Nurses Ltd
PY - 2023/6
Y1 - 2023/6
N2 - Background: Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service. Objectives: The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services. Methods: A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation. Results: 12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40–1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39–1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14–2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67–0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups—high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died). Conclusions: Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.
AB - Background: Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service. Objectives: The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services. Methods: A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation. Results: 12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40–1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39–1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14–2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67–0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups—high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died). Conclusions: Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.
KW - Critical care
KW - Follow-up
KW - Humans
KW - Risk prediction
KW - Risk scores
KW - Unplanned readmission
UR - http://www.scopus.com/inward/record.url?scp=85162094253&partnerID=8YFLogxK
U2 - 10.1016/j.aucc.2023.05.002
DO - 10.1016/j.aucc.2023.05.002
M3 - Article
AN - SCOPUS:85162094253
SN - 1036-7314
SP - 1
EP - 8
JO - Australian Critical Care
JF - Australian Critical Care
ER -
