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The study aimed to identify predictive factors for diabetic ketoacidosis (DKA) in individuals with type 1 diabetes using a subgroup discovery algorithm. The data from over 100,000 individuals with type 1 diabetes were analyzed, and 11 profiles associated with increased risk of DKA were identified using the algorithm. These profiles included low body mass index, DKA at diagnosis, high HbA1c levels, no fast-acting insulin intake, and autoimmune thyroiditis. The risk of DKA increased with the number of risk profiles matching the patient's characteristics. The algorithm confirmed previously identified risk profiles and generated new ones that may help predict patients with type 1 diabetes at greater risk of experiencing DKA.
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