Predicting risk of unplanned hospital readmission in survivors of critical illness: A population-level cohort study
Lee, Robert J.
Walsh, Timothy S.
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Lone, N., Lee, R., Salisbury, L., Donaghy, E., Ramsay, P., Rattray, J. & Walsh, T. (2018) Predicting risk of unplanned hospital readmission in survivors of critical illness: a population-level cohort study. Thorax, 74(11), pp. 1046-1054.
Background Intensive care unit (ICU) survivors experience high levels of morbidity after hospital discharge and are at high risk of unplanned hospital readmission. Identifying those at highest risk before hospital discharge may allow targeting of novel risk reduction strategies. We aimed to identify risk factors for unplanned 90-day readmission, develop a risk prediction model and assess its performance to screen for ICU survivors at highest readmission risk. Methods Population cohort study linking registry data for patients discharged from general ICUs in Scotland (2005-2013). Independent risk factors for 90-day readmission and discriminant ability (c-index) of groups of variables were identified using multivariable logistic regression. Derivation and validation risk prediction models were constructed using a time-based split. Results Of 55 975 ICU survivors, 24.1% (95%CI 23.7% to 24.4%) had unplanned 90-day readmission. Pre-existing health factors were fair discriminators of readmission (c-index 0.63, 95%-CI 0.63 to 0.64) but better than acute illness factors (0.60) or demographics (0.54). In a subgroup of those with no comorbidity, acute illness factors (0.62) were better discriminators than pre-existing health factors (0.56). Overall model performance and calibration in the validation cohort was fair (0.65, 95%-CI 0.64 to 0.66) but did not perform sufficiently well as a screening tool, demonstrating high false-positive/false-negative rates at clinically relevant thresholds. Conclusions Unplanned 90-day hospital readmission is common. Pre-existing illness indices are better predictors of readmission than acute illness factors. Identifying additional patient-centred drivers of readmission may improve risk prediction models. Improved understanding of risk factors that are amenable to intervention could improve the clinical and cost-effectiveness of post-ICU care and rehabilitation.