Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm

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Abstract: Aim
To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm.

Materials and Methods
Data from adults and children with type 1 diabetes and more than two diabetes-related visits were analysed from the Diabetes Prospective Follow-up Registry. Q-Finder, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH less than 7.3 during a hospitalization event.

Results
Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q-Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast-acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the number of risk profiles matching patients’ characteristics.

Conclusions
Q-Finder confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: Diabetes, obesity and metabolism. - 25, 7 (2023) , 1823-1829, ISSN: 1463-1326

Classification: Medizin, Gesundheit

Event: Veröffentlichung

(where): Freiburg

(who): Universität

(when): 2023

Creator: Ibald‐Mulli, Angela
Seufert, Jochen
Grimsmann, Julia M.
Laimer, Markus
Bramlage, Peter
Civet, Alexandre
Blanchon, Margot
Gosset, Simon
Templier, Alexandre
Paar, W. Dieter
Zhou, Fang Liz
Lanzinger, Stefanie

DOI: 10.1111/dom.15039

URN: urn:nbn:de:bsz:25-freidok-2343946

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:42 PM CET

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Associated

Ibald‐Mulli, Angela
Seufert, Jochen
Grimsmann, Julia M.
Laimer, Markus
Bramlage, Peter
Civet, Alexandre
Blanchon, Margot
Gosset, Simon
Templier, Alexandre
Paar, W. Dieter
Zhou, Fang Liz
Lanzinger, Stefanie
Universität

Time of origin

2023

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Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm

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