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This\u00a0research is particularly exciting for the MPH team as\u00a0both the first and second authors\u00a0are recent MPH graduates.\u00a0Additionally,\u00a0 the preliminary work for the project was\u00a0completed as a MPH dissertation project by the first author.<\/em><\/p>\n <\/p>\n <\/p>\n Abstract:<\/strong><\/u> Methods<\/strong> Results<\/strong> Conclusions<\/strong> The final version of this research paper has now been published online by Diabetic Medicine. It can be accessed here:\u00a0<\/em>http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/dme.13409\/abstract<\/a><\/strong><\/p>\n <\/p>\n Written by\u00a0K Stuart, N Adderley, T Marshall, G Rayman, A Sitch, S Ghosh, K A Toulis & K Nirantharakumar\u00a0<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" This\u00a0research is particularly exciting for the MPH team as\u00a0both the first and second authors\u00a0are recent MPH graduates.\u00a0Additionally,\u00a0 the preliminary work for the project was\u00a0completed as a MPH dissertation project by the first author. Abstract: Aims To explore whether a quantitative approach to identifying hospitalized patients with diabetes at risk of hypoglycaemia would be … Continue reading “Research Highlight:\u00a0Predicting inpatient hypoglycaemia in hospitalized patients with diabetes: a retrospective analysis of 9584 admissions with diabetes\u00a0(09.10.17)”<\/span><\/a><\/p>\n","protected":false},"author":223,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-238","post","type-post","status-publish","format-standard","hentry","category-uncategorised"],"_links":{"self":[{"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/posts\/238","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/users\/223"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/comments?post=238"}],"version-history":[{"count":4,"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/posts\/238\/revisions"}],"predecessor-version":[{"id":244,"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/posts\/238\/revisions\/244"}],"wp:attachment":[{"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/media?parent=238"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/categories?post=238"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.bham.ac.uk\/publichealth\/wp-json\/wp\/v2\/tags?post=238"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nAims<\/strong>
\nTo explore whether a quantitative approach to identifying hospitalized patients with diabetes at risk of hypoglycaemia would be feasible through incorporation of routine biochemical, haematological and prescription data.<\/p>\n
\nA retrospective cross-sectional analysis of all diabetic admissions (n=9584) from 1 January 2014 to 31 December 2014 was performed. Hypoglycaemia was defined as a blood glucose level of <4 mmol\/l. The prediction model was constructed using multivariable logistic regression, populated by clinically important variables and routine laboratory data.<\/p>\n
\nUsing a prespecified variable selection strategy, it was shown that the occurrence of inpatient hypoglycaemia could be predicted by a combined model taking into account background medication (type of insulin, use of sulfonylureas), ethnicity (black and Asian), age (\u226575 years), type of admission (emergency) and laboratory measurements (estimated GFR, C-reactive protein, sodium and albumin). Receiver-operating curve analysis showed that the area under the curve was 0.733 (95% CI 0.719 to 0.747). The threshold chosen to maximize both sensitivity and specificity was 0.15. The area under the curve obtained from internal validation did not differ from the primary model [0.731 (95% CI 0.717 to 0.746)].<\/p>\n
\nThe inclusion of routine biochemical data, available at the time of admission, can add prognostic value to demographic and medication history. The predictive performance of the constructed model indicates potential clinical utility for the identification of patients at risk of hypoglycaemia during their inpatient stay.<\/p>\n