Predicting risk of unplanned hospital readmission in survivors of critical illness: a population-level cohort study

BackgroundIntensive care unit 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.MethodsPopulation 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.ResultsOf 55,975 ICU survivors, 24.1% (95%CI 23.7%,24.4%) had unplanned 90-day readmission. Pre-existing health factors were fair discriminators of readmission (c-index 0.63,95%CI 0.63,0.64), 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 0.64,0.66) but did not perform sufficiently well as a screening tool, demonstrating high false positive/false negative rates at clinically relevant thresholds.ConclusionsUnplanned 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