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

作者：Angela Ibald‐Mulli, Jochen Seufert, Julia M. Grimsmann, Markus Laimer, Peter Bramlage, Alexandre Civet, Margot Blanchon, Simon Gosset, Alexandre Templier, W. Dieter Paar, Fang Liz Zhou, Stefanie Lanzinger · 发表于：Diabetes Obesity and Metabolism · 年份：2023 · DOI：10.1111/dom.15039 · 被引用次数：9 · 研究领域：Diabetes and associated disorders、Diabetes Management and Research、Diabetes, Cardiovascular Risks, and Lipoproteins

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.