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

• Published in: Diabetes, obesity & metabolism Vol. 25; no. 7; pp. 1823 - 1829
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.07.2023; Wiley Subscription Services, Inc
• Subjects: Adolescent; Adult; Age; Algorithms; Blood Glucose; Blood Glucose Self-Monitoring; Body mass index; Child; Cohort analysis; cohort study; database research; Diabetes; diabetes complications; Diabetes mellitus (insulin dependent); Diabetes Mellitus, Type 1 - complications; Diabetic ketoacidosis; Diabetic Ketoacidosis - complications; Diabetic Ketoacidosis - diagnosis; Diabetic Ketoacidosis - epidemiology; Diagnosis; Glucose monitoring; Humans; Hypoglycemia; Hypoglycemia - complications; Ketoacidosis; Kidney diseases; Prospective Studies; Retrospective Studies; Thyroiditis; type 1 diabetes; type 1 diabetes; cohort study; diabetes complications; database research
• ISSN: 1462-8902; 1463-1326; 1463-1326
• DOI: 10.1111/dom.15039

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.