Factors Associated With Diabetic Ketoacidosis at Onset of Type 1 Diabetes Among Pediatric Patients: A Systematic Review.

Importance: Presenting with diabetic ketoacidosis (DKA) at onset of type 1 diabetes (T1D) remains a risk. Following a 2011 systematic review, considerable additional articles have been published, and the review required updating. Objective: To evaluate factors associated with DKA at the onset of T1D among pediatric patients. Evidence Review: In this systematic review, PubMed, Embase, Scopus, CINAHL, Web of Science, and article reference lists were searched using the population, intervention, comparison, outcome search strategy for primary research studies on DKA and T1D onset among individuals younger than 18 years that were published from January 2011 to November 2021. These studies were combined with a 2011 systematic review on the same topic. Data were pooled using a random-effects model. Findings: A total of 2565 articles were identified; 149 were included, along with 46 from the previous review (total 195 articles). Thirty-eight factors were identified and examined for their association with DKA at T1D onset. Factors associated with increased risk of DKA were younger age at T1D onset (<2 years vs ≥2 years; odds ratio [OR], 3.51; 95% CI, 2.85-4.32; P < .001), belonging to an ethnic minority population (OR, 0.40; 95% CI, 0.21-0.74; P = .004), and family history of T1D (OR, 0.46; 95% CI, 0.37-0.57; P < .001), consistent with the 2011 systematic review. Some factors that were not associated with DKA in the 2011 systematic review were associated with DKA in the present review (eg, delayed diagnosis: OR, 2.27; 95% CI, 1.72-3.01; P < .001). Additional factors associated with risk of DKA among patients with new-onset T1D included participation in screening programs (OR, 0.35; 95% CI, 0.21-0.59; P < .001) and presentation during the COVID-19 pandemic (OR, 2.32; 95% CI, 1.76-3.06; P < .001). Conclusions and Relevance: In this study, age younger than 2 years at T1D onset, belonging to an ethnic minority population, delayed diagnosis or misdiagnosis, and presenting during the COVID-19 pandemic were associated with increased risk of DKA. Factors associated with decreased risk of DKA included greater knowledge of key signs or symptoms of DKA, such as a family history of T1D or participation in screening programs. Future work should focus on identifying and implementing strategies related to these factors to reduce risk of DKA among new patients with T1D.

INNODIA Master Protocol for the evaluation of investigational medicinal products in children, adolescents and adults with newly diagnosed type 1 diabetes.

BACKGROUND: The INNODIA consortium has established a pan-European infrastructure using validated centres to prospectively evaluate clinical data from individuals with newly diagnosed type 1 diabetes combined with centralised collection of clinical samples to determine rates of decline in beta-cell function and identify novel biomarkers, which could be used for future stratification of phase 2 clinical trials. METHODS: In this context, we have developed a Master Protocol, based on the "backbone" of the INNODIA natural history study, which we believe could improve the delivery of phase 2 studies exploring the use of single or combinations of Investigational Medicinal Products (IMPs), designed to prevent or reverse declines in beta-cell function in individuals with newly diagnosed type 1 diabetes. Although many IMPs have demonstrated potential efficacy in phase 2 studies, few subsequent phase 3 studies have confirmed these benefits. Currently, phase 2 drug development for this indication is limited by poor evaluation of drug dosage and lack of mechanistic data to understand variable responses to the IMPs. Identification of biomarkers which might permit more robust stratification of participants at baseline has been slow. DISCUSSION: The Master Protocol provides (1) standardised assessment of efficacy and safety, (2) comparable collection of mechanistic data, (3) the opportunity to include adaptive designs and the use of shared control groups in the evaluation of combination therapies, and (4) benefits of greater understanding of endpoint variation to ensure more robust sample size calculations and future baseline stratification using existing and novel biomarkers.