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Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation.pdf (386.02 kB)

Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation.

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posted on 2017-10-31, 11:39 authored by Francesco Zaccardi, David R. Webb, Melanie J. Davies, Nafeesa N. Dhalwani, Laura J. Gray, Sudesna Chatterjee, Gemma Housley, Dominick Shaw, James W. Hatton, Kamlesh Khunti
AIMS/HYPOTHESIS: Hospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia. METHODS: We used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics. RESULTS: In derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models. CONCLUSIONS/INTERPRETATION: This simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.

Funding

We acknowledge support from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care – East Midlands (NIHR CLAHRC – EM), the Leicester Clinical Trials Unit and the NIHR Leicester–Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit, which is a partnership between University Hospitals of Leicester NHS Trust, Loughborough University and the University of Leicester. FZ is a Clinical Research Fellow funded with an unrestricted educational grant from Sanofi-Aventis to the University of Leicester.

History

Citation

Diabetologia, 2017, 60 (6), pp. 1007-1015

Author affiliation

/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Cardiovascular Sciences

Version

  • VoR (Version of Record)

Published in

Diabetologia

Publisher

Springer Verlag (Germany)

issn

0012-186X

eissn

1432-0428

Acceptance date

2017-02-06

Copyright date

2017

Available date

2017-10-31

Publisher version

https://link.springer.com/article/10.1007/s00125-017-4235-1

Notes

The online version of this article (doi:10.1007/s00125-017-4235-1) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Language

en

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