Cargando…
Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation
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 de...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423930/ https://www.ncbi.nlm.nih.gov/pubmed/28314943 http://dx.doi.org/10.1007/s00125-017-4235-1 |
_version_ | 1783235025736564736 |
---|---|
author | 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 |
author_facet | 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 |
author_sort | Zaccardi, Francesco |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: 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. |
format | Online Article Text |
id | pubmed-5423930 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-54239302017-05-25 Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation 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 Diabetologia Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: 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. Springer Berlin Heidelberg 2017-03-17 2017 /pmc/articles/PMC5423930/ /pubmed/28314943 http://dx.doi.org/10.1007/s00125-017-4235-1 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article 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 Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
title | Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
title_full | Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
title_fullStr | Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
title_full_unstemmed | Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
title_short | Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
title_sort | predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423930/ https://www.ncbi.nlm.nih.gov/pubmed/28314943 http://dx.doi.org/10.1007/s00125-017-4235-1 |
work_keys_str_mv | AT zaccardifrancesco predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT webbdavidr predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT daviesmelaniej predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT dhalwaninafeesan predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT graylauraj predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT chatterjeesudesna predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT housleygemma predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT shawdominick predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT hattonjamesw predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation AT khuntikamlesh predictinghospitalstaymortalityandreadmissioninpeopleadmittedforhypoglycaemiaprognosticmodelsderivationandvalidation |