Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm
Authors' conclusion
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.
Study context
- Population
- Adults and children with T1D and more than two diabetes-related visits, Diabetes Prospective Follow-up Registry
- Participants
- 108,223 adults
- Outcome
- DKA (pH less than 7.3 during a hospitalization event)
Methodological detailssubgroup discovery algorithm (Q-Finder)
- Subgroup discovery algorithm (Q-Finder)
Main finding
As reported in the abstract
DKA
Patients with diabetic ketoacidosis (DKA)
5609
5.2%
Profiles associated with increased DKA risk
11
Observational data: associations, not effects.
Original publication
Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm. Diabetes Obes Metab. Published 16 April 2023. doi:10.
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