Prognostic Implications of Diabetic Ketoacidosis in Adults on Long-term Mortality and Diabetes-related Complications.

OBJECTIVES: Diabetic ketoacidosis (DKA) occurring after diabetes diagnosis is often associated with risk factors for other diabetes-related complications. In this study we aimed to determine the prognostic implications of DKA on all-cause mortality and complications in type 1 diabetes (T1D). METHODS: Previously collected data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study were obtained through the the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository. Using Cox proportional hazards models with time-dependent covariates, we examined age- and sex-adjusted, glycated hemoglobin-adjusted, and fully adjusted associations of DKA with all-cause mortality, cardiovascular disease, microvascular, and acute complications over 34 years. RESULTS: Of the 1,441 study participants, 297 had 488 DKA events. Prior DKA was associated with a higher risk of age- and sex-adjusted all-cause mortality (hazard ratio [HR] 8.28, 95% confidence interval [CI] 3.74 to 18.32, p<0.001), major adverse cardiovascular events (MACEs) (HR 2.05, 95% CI 1.34 to 3.13, p<0.001), and all advanced microvascular and acute complications compared with no prior DKA. Most associations except retinopathy were significant even after adjustment for covariates. In our fully adjusted analysis, prior DKA was associated with a significantly higher risk of subsequent all-cause mortality (HR 9.13, 95% CI 3.87 to 21.50, p<0.001), MACEs (HR 1.66, 95% CI 1.07 to 2.59, p=0.03), advanced kidney disease (HR 2.10, 95% CI 1.00 to 4.22, p=0.049), advanced neuropathy (HR 1.49, 95% CI 1.05 to 2.13, p=0.03), severe hypoglycemia (HR 1.53, 95% CI 1.28 to 1.81, p<0.001), and recurrent DKA (HR 3.24, 95% CI 2.41 to 4.36, p<0.001) compared with person-time without DKA. CONCLUSIONS: DKA is a prognostic marker for diabetes complications, including excess all-cause mortality. Intensified clinical interventions, such as cardiovascular prevention strategies, may be warranted after diagnosis of DKA.

Point-of-Care Capillary Blood Ketone Measurements and the Prediction of Future Ketoacidosis Risk in Type 1 Diabetes.

OBJECTIVE: Rather than during illness while diabetic ketoacidosis (DKA) is developing, we aimed to determine if levels of routine point-of-care capillary blood ketones could predict future DKA. RESEARCH DESIGN AND METHODS: We examined previously collected data from placebo-assigned participants in an adjunct-to-insulin medication trial program that included measurement of fasted capillary blood ketone levels twice per week in a 2-month baseline period. The outcome was 6- to 12-month trial-adjudicated DKA. RESULTS: DKA events occurred in 12 of 484 participants at a median of 105 (interquartile range 43, 199) days. Maximum ketone levels were higher in patient cases compared with in control patients (0.8 [0.6, 1.2] vs. 0.3 [0.2, 0.7] mmol/L; P = 0.002), with a nonparametric area under the receiver operating characteristic curve of 0.77 (95% CI 0.66-0.88). Ketone levels ≥0.8 mmol/L had a sensitivity of 64%, a specificity of 78%, and positive and negative likelihood ratios of 2.9 and 0.5, respectively. CONCLUSIONS: This proof of concept that routine capillary ketone surveillance can identify individuals at high risk of future DKA implies a role for future technologies including continuous ketone monitoring.

Participatory governance over research in an academic research network: the case of Diabetes Action Canada.

Digital data generated in the course of clinical care are increasingly being leveraged for a wide range of secondary purposes. Researchers need to develop governance policies that can assure the public that their information is being used responsibly. Our aim was to develop a generalisable model for governance of research emanating from health data repositories that will invoke the trust of the patients and the healthcare professionals whose data are being accessed for health research. We developed our governance principles and processes through literature review and iterative consultation with key actors in the research network including: a data governance working group, the lead investigators and patient advisors. We then recruited persons to participate in the governing and advisory bodies. Our governance process is informed by eight principles: (1) transparency; (2) accountability; (3) follow rule of law; (4) integrity; (5) participation and inclusiveness; (6) impartiality and independence; (7) effectiveness, efficiency and responsiveness and (8) reflexivity and continuous quality improvement. We describe the rationale for these principles, as well as their connections to the subsequent policies and procedures we developed. We then describe the function of the Research Governing Committee, the majority of whom are either persons living with diabetes or physicians whose data are being used, and the patient and data provider advisory groups with whom they consult and communicate. In conclusion, we have developed a values-based information governance framework and process for Diabetes Action Canada that adds value over-and-above existing scientific and ethics review processes by adding a strong patient perspective and contextual integrity. This model is adaptable to other secure data repositories.