There is increasing use of open-source artificial pancreas systems (APS) in the management of Type 1 diabetes. Our aim was to assess the safety and efficacy of the automated insulin delivery system AndroidAPS (AAPS), compared with stand-alone pump therapy in people with type 1 diabetes. The primary outcome was the difference in the percentage of time in range (TIR, 70-180 mg/dL). Secondary aims included mean sensor glucose value and percent continuous glucose monitor (CGM) time below range (TBR, <70 mg/dL).
Aims The prevalence of diabetes is rising, and people with diabetes have higher rates of musculoskeletal-related comorbidities. HbA1c testing is a superior option for diabetes diagnosis in the inpatient setting. This study aimed to (i) demonstrate the feasibility of routine HbA1c testing to detect the presence of diabetes mellitus, (ii) to determine the prevalence of diabetes in orthopedic inpatients and (iii) to assess the association between diabetes and hospital outcomes and post-operative complications in orthopedic inpatients. Methods All patients aged ≥54 years admitted to Austin Health between July 2013 and January 2014 had routine automated HbA1c measurements using automated clinical information systems (CERNER). Patients with HbA1c ≥6.5% were diagnosed with diabetes. Baseline demographic and clinical data were obtained from hospital records. Results Of the 416 orthopedic inpatients included in this study, 22% (n = 93) were known to have diabetes, 4% (n = 15) had previously unrecognized diabetes and 74% (n = 308) did not have diabetes. Patients with diabetes had significantly higher Charlson comorbidity scores compared to patients without diabetes (median, IQR; 1 [0,2] vs 0 [0,0], p<0.001). After adjusting for age, gender, comorbidity score and estimated glomerular filtration rate, no significant differences in the length of stay (IRR = 0.92; 95%CI: 0.79–1.07; p = 0.280), rates of intensive care unit admission (OR = 1.04; 95%CI: 0.42–2.60, p = 0.934), 6-month mortality (OR = 0.52; 95%CI: 0.17–1.60, p = 0.252), 6-month hospital readmission (OR = 0.93; 95%CI: 0.46–1.87; p = 0.828) or any post-operative complications (OR = 0.98; 95%CI: 0.53–1.80; p = 0.944) were observed between patients with and without diabetes. Conclusions Routine HbA1c measurement using CERNER allows for rapid identification of inpatients admitted with diabetes. More than one in four patients admitted to a tertiary hospital orthopedic ward have diabetes. No statistically significant differences in the rates of hospital outcomes and post-operative complications were identified between patients with and without diabetes.
Lipid-lowering therapy (LLT) should be accompanied by dietary guidance for cardiovascular risk reduction; however, current evidence suggests sub-optimal dietary behaviors in those on LLT. We examined the associations between the dietary intake of key food groups (vegetables, fruit, cereal, protein, and dairy) and LLT use in Australian adults using quantile regression. We used data from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), a prospective population-based study of adults aged ≥25 years, conducted over 5 years (1999–2005). Measurements included a 121-item food frequency questionnaire and LLT use. LLT use was categorized as: LLT users (n = 446), commenced LLT (n = 565), ceased LLT (n = 71), and non-users (n = 4813). Less than 1% of the cohort met recommended intakes of all food groups at the baseline and follow up. The median daily dietary intake at the follow up among LLT users was 2.2 serves of vegetables, 1.4 serves of fruit, 2.8 serves of cereal, 2.0 serves of protein, and 1.4 serves of dairy. Adjusted analysis showed no differences across the quantiles of intake of key food groups in LLT users and commenced LLT compared to non-users. The LLT medication status is not associated with any difference in meeting recommended intakes of key foods.
Polycystic ovary syndrome (PCOS) affects 9-21% of reproductive-age women. The relations between PCOS, body mass index (BMI) and breastfeeding are unclear. Our aim was to examine breastfeeding in women with and without PCOS and the relation with BMI.This is a cross-sectional study set in the general community. Participants are women, aged 31-36 years, from the Australian Longitudinal Study on Women's Health (ALSWH), a large community-based study. Data was analyzed from the first child of respondents to Survey five (2009) reporting at least one live born child. Logistic regression analysis was used to examine factors associated with breastfeeding. The main outcome measures studied were breastfeeding initiation and duration and the main explanatory variables included self-reported PCOS and BMI.Of the 4898 women, 6.5% reported PCOS (95% confidence interval 5.8-7.2%). Median duration of breastfeeding was lower in women reporting PCOS (6 months, range 2-10 months) than in women not reporting PCOS (7 months, range 3-12 months) (p = 0.001). On multivariable regression analysis, there was no association between PCOS and breastfeeding outcomes. However, being overweight or obese was associated with not initiating breastfeeding and with breastfeeding for less than 6 months, after adjusting for confounders.High BMI is negatively associated with breastfeeding, whereas PCOS status per se does not appear to be related to breastfeeding initiation and duration, after adjusting for BMI.
The aim of this study was to assess the relative validity and reproducibility of a six-item Australian Short Dietary Screener (Aus-SDS). The Aus-SDS assessed the daily intake of core food groups (vegetables, fruits, legumes and beans, cereals, protein sources and dairy sources) in 100 Australians (52 males and 48 females) aged ≥70 years. Relative validity was assessed by comparing intakes from the Aus-SDS1 with an average of three 24-hour recalls (24-HRs), and reproducibility using two administrations of the Aus-SDS (Aus-SDS1 and Aus-SDS2). Cohen's kappa statistic between the Aus-SDS1 and 24-HRs showed moderate to good agreement, ranging from 0.44 for fruits and dairy to 0.64 for protein. There was poor agreement for legume intake (0.12). Bland-Altman plots demonstrated acceptable limits of agreement between the Aus-SDS1 and 24-HRs for all food groups. Median intakes obtained from Aus-SDS1 and Aus-SDS2 did not differ. For all food groups, Cohen's kappa statistic ranged from 0.68 to 0.89, indicating acceptable agreement between the Aus-SDS1 and Aus-SDS2. Spearman's correlation coefficient between Aus-SDS1 and 24-HRs across all food groups ranged from 0.64 for fruit to 0.83 for protein. We found the Aus-SDS to be a useful tool in assessing daily intake of core food groups in this population.
Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies. A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected. Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017. Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.
Introduction: Type 2 diabetes is increasingly diagnosed among younger people. Patient engagement with self-care practices is crucial for the optimal management of type 2 diabetes. This study examines the self-care practices of younger and older patients with type 2 diabetes. Methods: Data were analysed from the Australian National Diabetes Audit (ANDA) that included 2552 adult patients with type 2 diabetes from 56 participating Diabetes Centres. Pre-specified demographic and clinical variables were obtained. Self-care variables (physical activity, following dietary recommendations, medication adherence and monitoring blood glucose levels) were compared in patients ≤64 years and >64 years of age. Results: Mean age (±SD) of participants was 63±13 years overall, 53±9 years for the younger group and 73±6 years for the older group. Mean diabetes duration was 9±8 years and 15±10 years for younger and older patients, respectively (p<0.01). A greater proportion of younger patients had HbA1c levels above 7.0% compared with older patients (76% vs. 68%, p<0.001). A greater proportion of younger compared to older patients reported difficulty following dietary recommendations (50% vs. 32%) and forgetting medications (37% vs. 22%) (all p values <0.001). A smaller proportion of younger compared with older patients reported monitoring their blood glucose levels as often as recommended (60%vs. 70%, p<0.001). Younger age was associated with a 2-fold increase in the odds of not following the recommended self-care practices after adjustment for gender, smoking, insulin therapy, depression and allied health attendance (all p values <0.001). Conclusion: Despite shorter diabetes duration, younger age was associated with worse glycaemic control and poorer diabetes self-care practices among patients with type 2 diabetes. Targeted strategies are urgently required to optimise diabetes self-care practices and thereby improve glycaemic control. Disclosure N. Nanayakkara: None. A.J. Pease: None. S. Ranasinha: None. N. Wischer: None. B. de Courten: None. S. Zoungas: Advisory Panel; Self; AstraZeneca.
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