Incidence of diabetic ketoacidosis and its trends in patients with type 1 diabetes mellitus identified using a U.S. claims database, 2007-2019.

Diabetic ketoacidosis (DKA) is a common complication of type 1 diabetes mellitus (T1DM). We found that the incidence of DKA was 55.5 per 1000 person-years in US commercially insured patients with T1DM; age-sex-standardized incidence decreased at an average annual rate of 6.1% in 2018-2019 after a steady increase since 2011.

Performance assessment of different machine learning approaches in predicting diabetic ketoacidosis in adults with type 1 diabetes using electronic health records data.

PURPOSE: To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA. METHODS: This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3400 DKA cases and 11 780 controls nested in adults with type 1 diabetes identified from Optum® de-identified Electronic Health Record dataset (2007-2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using fivefold cross validation, and their 95% confidence intervals (CI) were established using 1000 bootstrap samples. The importance of predictors was compared across these models. RESULTS: In the training set, XGBoost and feedforward network yielded higher AUC values (0.89 and 0.86, respectively) than logistic regression (0.83), LASSO (0.83) and DRF (0.81). However, the AUC values were similar (0.82) among these approaches in the test set (95% CI range, 0.80-0.84). While the accuracy values >0.8 and the specificity values >0.9 for all models, the sensitivity values were only 0.4. The differences in these metrics across these models were minimal in the test set. All approaches selected some known risk factors for DKA as the top 10 features. XGBoost and DRF included more laboratory measurements or vital signs compared with conventional ML approaches, while feedforward network included more social demographics. CONCLUSIONS: In our empirical study, all ML approaches demonstrated similar performance, and identified overlapping, but different, top 10 predictors. The difference in selected top predictors needs further research.

Insulin Glulisine in Pregnancy - Experience from Clinical Trials and Post-marketing Surveillance.

Pregnancies complicated by gestational diabetes or pre-existing type 1 or type 2 diabetes mellitus are associated with a higher rate of adverse outcomes compared with pregnancies in the background population. These outcomes include miscarriage, pre-term delivery, pre-eclampsia, perinatal mortality and congenital malformations. Insulin glulisine (Apidra®, Sanofi) is a rapid-acting insulin analogue indicated for the treatment of adults, adolescents and children 6 years or older with diabetes mellitus where treatment with insulin is required. Here, all post-marketing and clinical trials safety data with insulin glulisine in pregnancy available to Sanofi up to June 2014 are summarised together with the findings of a comprehensive literature search. Cumulatively, a total of 303 pregnancy exposures to insulin glulisine were received. Of these 303 pregnancy exposures, there were 116 live births, 12 spontaneous abortions, two late foetal intra-uterine deaths (>28 weeks), three elective abortions and 170 cases without a known pregnancy outcome. There were six cases of congenital malformations; of these, there were five live births; in the other case a live birth was not confirmed. The congenital malformations reported to date do not reveal a pattern of defects. In conclusion, the evidence to date does not suggest a causal association between insulin glulisine and an increased risk of pregnancy complications or congenital malformations.