The Impact of COVID-19 Pandemic on Prevalence of Diabetic Ketoacidosis at Diagnosis of Type 1 Diabetes: A Single-Centre Study in Central Pennsylvania.

Objective: We conducted this study to investigate whether the COVID-19 pandemic impacted the rate of DKA and previously identified risk factors in children presenting with T1D. Methods: We performed an extension of a retrospective analysis of all paediatric patients (age ≤ 18) newly diagnosed with T1D within a tertiary care referral centre between 01/01/2017 and 09/14/2020. Demographics, insurance coverage and clinical documents 30 days before their T1D diagnosis were abstracted to assess for symptoms at diagnosis, laboratory values (blood glucose, HbA1c, venous pH and bicarbonate) and any healthcare encounters within 30 days of their diagnosis of T1D. Results: 412 patients with T1D [171 F:241 M; 370 pre-COVID era:42 post-COVID era] were included. The percentages of DKA diagnoses at admission were very similar between the pre-COVID and post-COVID groups (47% vs. 48%), as were the severity (13% vs. 14% mild DKA; 33% vs. 31% moderate or severe DKA). Conclusion: There were no fluctuations in the rate of DKA among paediatric patients newly diagnosed with T1D throughout the coronavirus pandemic in central Pennsylvania.

Prevention of diabetic ketoacidosis at diagnosis of type 1 diabetes: Are certified athletic trainers an untapped resource?

Aims: To assess the knowledge of certified athletic trainers (ATs) on the presenting signs and symptoms of type 1 diabetes (T1D). Methods: We conducted a 31-question survey of secondary school ATs recruited from the National Athletic Training Association that established demographic information, knowledge of presenting signs and symptoms of T1D, and previous personal or professional exposure to individuals with T1D. We report descriptive statistics and univariate analyses evaluating the characteristics associated with T1D knowledge. We then report a multivariable model incorporating age, gender, years of experience and education level with T1D knowledge as the dependent variable. Results: 128 participants (92f:34m) met inclusion criteria and were included in this study. The majority of participants correctly identified frequent thirst (96.1%, n = 123) and frequent urination (85.9%, n = 110) as common presenting signs and symptoms of T1D, while fewer participants identified weight gain (58.6%, n = 75) or joint pain (39.1%, n = 50) as incorrect presenting signs and symptoms of T1D. Participants with over ten years of experience or previous exposure to individuals with T1D had increased T1D knowledge. Participants with advanced education (Master's degree or Doctorate) had no statistically significant difference in T1D knowledge compared to those with a Bachelor's degree. The only factor that demonstrated a significant association with T1D knowledge on multivariable analysis was the female gender. Conclusions: Educational awareness campaigns of T1D symptoms to reduce the rate of DKA at diagnosis of T1D have never included ATs. This study illustrates the importance of targeting future educational interventions on newly trained ATs.

Separating Putative Pathogens from Background Contamination with Principal Orthogonal Decomposition: Evidence for Leptospira in the Ugandan Neonatal Septisome.

Neonatal sepsis (NS) is responsible for over 1 million yearly deaths worldwide. In the developing world, NS is often treated without an identified microbial pathogen. Amplicon sequencing of the bacterial 16S rRNA gene can be used to identify organisms that are difficult to detect by routine microbiological methods. However, contaminating bacteria are ubiquitous in both hospital settings and research reagents and must be accounted for to make effective use of these data. In this study, we sequenced the bacterial 16S rRNA gene obtained from blood and cerebrospinal fluid (CSF) of 80 neonates presenting with NS to the Mbarara Regional Hospital in Uganda. Assuming that patterns of background contamination would be independent of pathogenic microorganism DNA, we applied a novel quantitative approach using principal orthogonal decomposition to separate background contamination from potential pathogens in sequencing data. We designed our quantitative approach contrasting blood, CSF, and control specimens and employed a variety of statistical random matrix bootstrap hypotheses to estimate statistical significance. These analyses demonstrate that Leptospira appears present in some infants presenting within 48 h of birth, indicative of infection in utero, and up to 28 days of age, suggesting environmental exposure. This organism cannot be cultured in routine bacteriological settings and is enzootic in the cattle that often live in close proximity to the rural peoples of western Uganda. Our findings demonstrate that statistical approaches to remove background organisms common in 16S sequence data can reveal putative pathogens in small volume biological samples from newborns. This computational analysis thus reveals an important medical finding that has the potential to alter therapy and prevention efforts in a critically ill population.