Objectives Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event.
Setting A single academic, tertiary care hospital in the UK.
Participants A set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ≤16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained.
Primary and secondary outcome measures Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge.
Results In 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test).
Conclusions Despite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.
- unplanned readmission
- machine learning
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Contributors TD, RD, JC and AE conceived the described experiments. AE acquired the Cambridge University Hospitals data, with assistance from Dr Afzal Chaudhry and Shaun Hyett. TD executed the experiments. TD, RD, JC and AE interpreted the results. TD and AE wrote the manuscript. TD, RD, JC, MT, CS, DJW and AE revised the manuscript, with assistance from Dr Jana Hoffman, as well as Emily Huynh and Siddharth Gampa. All authors approved the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding Research reported in this publication was supported by the National Institute of Nursing Research, of the National Institutes of Health, under award number R43NR015945. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Competing interests TD, JC and RD are employees or contractors of Dascena Inc, developers of the AutoTriage system.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement No data obtained from Cambridge University Hospitals in this study can be shared or made available for open access. MIMIC-III is a publicly available database.
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