Jochen Seufert

Aim Patient‐ and physician‐associated barriers impact the effectiveness of basal insulin (BI) titration in the management of type 2 diabetes (T2D). We evaluated the experiences of patients with T2D and physicians with BI titration education. Materials and Methods In this observational, cross‐sectional study, patients with T2D and physicians treating patients with T2D were identified by claims in the Optum Research Database and were invited to complete a survey. Eligible patients had 12 months of continuous health‐plan enrolment with medical and pharmacy benefits during the baseline period, and recent initiation of BI therapy. Eligible physicians had initiated BI for ≥1 eligible patient with T2D during the past 6 months. Results In total, 416 patients and 386 physicians completed the survey. Ninety per cent of physicians reported treating ≥50 patients with T2D; 66% treated ≥25% of patients with BI. Whereas 74% of patients reported that BI titration was explained to them by a physician, 96% of physicians reported doing so. Furthermore, 20% of patients stated they were offered educational materials whereas 56% of physicians reported having provided materials. Physicians had higher expectations of glycaemic target achievement than were seen in the patient survey; their main concern was the patients' ability to titrate accurately (79%). Conclusions There is a marked difference in patients' and physicians' experiences of BI titration education. Novel tools and strategies are required to enable effective BI titration, with more educational resources at the outset, and ongoing access to tools that provide clear, simple direction for self‐titration with less reliance on physicians/health care providers.

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.