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

Abstract

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 fastacting 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.

Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU

“…[ [5] , [6] , [7] , [8] , [9] , [10] , [11] ], have been applied to the medical domain. Recent studies use subgroup discovery to identify subgroups of cancer patients [ 14 , 15 ], identify predictive factors for diabetic ketoacidosis [ 16 ], or discover subgroups of patients undergoing transcatheter aortic valve implantation with high model prediction error and their distribution over the centres [ 17 ]. Gamberger et al [ 11 ] demonstrated the applicability of SGD analysis for in brain ischaemia.…”

Incidence and predictors of mortality in children with diabetic ketoacidosis in the comprehensive specialized referral hospitals of West Amhara Region, Northwest Ethiopia: a retrospective follow-up study

“…It was defined as hyperglycemia (blood glucose level greater than 13.9 mmol/L or greater than 250mg/dl and any of the following: venous PH less than 7.3 or bicarbonate less than 15 mmol/L). In addition, clinical signs and symptoms of volume depletion, Kussmaul breathing, cerebral edema, acetone or fruity breath odor, mental status alterations, vomiting, nausea, and infection are indicators of DKA (2,3).…”