Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/39340
Title: Predicting hospital stay, mortality, and readmission in people admitted for hypoglycaemia
Authors: Zaccardi, Francesco
Webb, David R.
Davies, Melanie J.
Dhalwani, Nafeesa N.
Gray, Laura J.
Chatterjee, Sudesna
Housley, Gemma
Shaw, Dominick
Hatton, James W.
Khunti, Kamlesh
First Published: 17-Mar-2017
Publisher: Springer Verlag (Germany)
Citation: Diabetologia, 2017, 60 (6), pp. 1007-1015
Abstract: 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.
DOI Link: 10.1007/s00125-017-4235-1
ISSN: 0012-186X
eISSN: 1432-0428
Links: https://link.springer.com/article/10.1007/s00125-017-4235-1
http://hdl.handle.net/2381/39340
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: 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.
Appears in Collections:Published Articles, Dept. of Health Sciences

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