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.
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, School of Public Health, Scholarship in Health Economics (Patient-Centred Care and Outcomes in Chronic Disease); and a National Health and Medical Research Council (NHMRC) Medical Research Future Fund (MRFF) 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 - 2024/5
Y1 - 2024/5
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
VL - 37
SP - 383
EP - 390
JO - Australian Critical Care
JF - Australian Critical Care
IS - 3
ER -
