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Original article
Predicting risk of unplanned hospital readmission in survivors of critical illness: a population-level cohort study
  1. Nazir I Lone1,2,
  2. Robert Lee2,
  3. Lisa Salisbury1,3,
  4. Eddie Donaghy1,4,
  5. Pamela Ramsay1,5,
  6. Janice Rattray6,
  7. Timothy S Walsh1,2,4
  1. 1University Department of Anaesthesia, Critical Care, and Pain Medicine, School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
  2. 2Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
  3. 3Queen Margaret Drive, Queen Margaret University Edinburgh, Musselburgh, UK
  4. 4MRC Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
  5. 5Edinburgh Napier University, Edinburgh, UK
  6. 6School of Nursing and Health Sciences, University of Dundee, Dundee, UK
  1. Correspondence to Dr Nazir I Lone, University Department of Anaesthesia, Critical Care, and Pain Medicine, School of Clinical Sciences, University of Edinburgh, Edinburgh, EH16 4SA, UK; nazir.lone{at}ed.ac.uk

Abstract

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.

  • Intensive care
  • critical care
  • patient readmission
  • hospitalization
  • outcome

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Footnotes

  • Contributors All contributed to conception and design of the work. NIL and RL contributed to data acquisition and analysis. All authors contributed to interpretation of data for the work. NIL and TSW drafted the work. All authors revised it critically for important intellectual content. All authors gave final approval of the version to be published. All authors 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 The project was funded through a research grant from the Chief Scientist Office for Scotland (reference CZH/401026). The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

  • Competing interests None declared.

  • Ethics approval All data relating to patients were anonymised and analysed in a safe haven environment. This study gained approval from the Privacy Advisory Committee of NHS National Services Scotland (Reference PAC 12/14). South East Scotland Research Ethics Committee granted a waiver (Reference NR/1403AB5).

  • Provenance and peer review Not commissioned; externally peer reviewed.

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