Aims: Diabetic ketoacidosis (DKA) is a serious and potentially fatal complication of type 1 diabetes and it is difficult to identify individuals at increased risk. The aim of this study was to identify predictive factors for DKA by retrospective analysis of registry data and use of a subgroup discovery algorithm.

Materials and methods: Data from adults and children with type 1 diabetes and >2 diabetes-related visits were analyzed 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 <7.3 during a hospitalization event.

Results: Data for 108,223 adults and children, of whom 5,609 (5.2%) had DKA, were studied. Q-Finder® analysis identified 11 profiles associated with increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; HbA_1c ≥8.87 [73 mmol/mol]; no fast-acting insulin intake; age <15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycemia; hypoglycemic coma; and autoimmune thyroiditis. Risk of DKA increased with 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. This article is protected by copyright. All rights reserved.