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

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. [ABSTRACT FROM AUTHOR]