SummaryMulticentric Castleman disease (MCD) is a rare lymphoproliferative disease with little known about its epidemiology or treatment modalities. Clinical and demographic data of MCD patients identified between 2000 and 2009 were collected from medical records at two United States (US) MCD referral centres. ZIP codes identified patient residences; prevalence and incidence were estimated based on catchment areas. Patient clinical, demographic, and biochemical characteristics, drug therapies and medical utilization were descriptively reported. MCD patients (n = 59) were 61% male, mean age of 53 years (median = 55 years) and 68% Caucasian. Of those with known human immunodeficiency virus (HIV) status (n = 41), 85% (n = 35) were negative, 15% (n = 6) were positive. Most frequent physician-reported symptoms (n = 33) were fatigue (49%, n = 16), fever (39%, n = 13), and night sweats (30%, n = 10). The estimated US 10-year prevalence was 2Á4 per million. During first year of follow-up after study entry, the top two systemic therapies (n = 27) were monotherapies: prednisone (33%, n = 9) and rituximab (19%, n = 5). After a follow-up of 2 years, 92% of patients were alive. This study provides new information on MCD population demographics, treatment patterns, and medical utilization; a minimal US period prevalence rate is proposed. Study replication is needed to improve external validity.

Overview: We performed a retrospective study that analyzed outcomes from a subset (n=20) of the Do It Yourself (DIY) closed loop community. This already well-controlled, highly motivated T1D population realized further improvements in A1c and glucose time in range (TIR), and a reduction in time spent high and low during all timeframes after beginning a DIY hybrid closed loop “artificial pancreas” system. Objective: To compare mean BG, TIR (70-180 mg/dl), and time above and below clinically meaningful thresholds before and after OpenAPS initiation. Methods: We performed a retrospective cross-over analysis of continuous BG readings recorded during 2-week segments 4-6 weeks before and after initiation of OpenAPS (Johns Hopkins IRB00121066). Mean BG and TIR were analyzed overall, as well as by day (7am-11pm) and night (11pm-7am), and statistical significance was assessed using paired t-tests. Results: Mean BG and TIR improved in every time category. Overall, mean BG (mg/dl) improved (135.7 to 128.3); as did mean estimated HbA1c (6.4 to 6.1%). TIR increased from 75.8 to 82.2% overall. Overnight, BG time <70 was reduced from 6.4 to 4.2%, and time <50 was reduced from 2.3 to 1.0%. Overall, BG excursions >300 were reduced from 1.7 to 0.35%. Conclusion: Even with tight initial control, persons with T1D saw meaningful improvements in estimated A1c, TIR, and a reduction in time spent high and low, during the day and at night, after initiating OpenAPS. Disclosure D.M. Lewis: Consultant; Self; Tandem Diabetes Care, Inc.. R.S. Swain: None. T.W. Donner: Research Support; Self; Novo Nordisk Inc., Viacyte, Inc..

Background Suicidal outcomes, including ideation, attempt, and completed suicide, are an important drug safety issue, though few epidemiological studies address the accuracy of suicidal outcome ascertainment. Our primary objective was to evaluate validated methods for suicidal outcome classification in electronic health care database studies. Methods We performed a systematic review of PubMed and EMBASE to identify studies that validated methods for suicidal outcome classification published 1 January 1990 to 15 March 2016. Abstracts and full texts were screened by two reviewers using prespecified criteria. Sensitivity, specificity, and predictive value for suicidal outcomes were extracted by two reviewers. Methods followed PRISMA-P guidelines, PROSPERO Protocol: 2016: CRD42016042794. Results We identified 2202 citations, of which 34 validated the accuracy of measuring suicidal outcomes using International Classification of Diseases (ICD) codes or algorithms, chart review or vital records. ICD E-codes (E950-9) for suicide attempt had 2–19% sensitivity, and 83–100% positive predictive value (PPV). ICD algorithms that included events with ‘uncertain’ intent had 4–70% PPV. The three best-performing algorithms had 74–92% PPV, with improved sensitivity compared with E-codes. Read code algorithms had 14–68% sensitivity and 0–56% PPV. Studies estimated 19–80% sensitivity for chart review, and 41–97% sensitivity and 100% PPV for vital records. Conclusions Pharmacoepidemiological studies measuring suicidal outcomes often use methodologies with poor sensitivity or predictive value or both, which may result in underestimation of associations between drugs and suicidal behaviour. Studies should validate outcomes or use a previously validated algorithm with high PPV and acceptable sensitivity in an appropriate population and data source.

To estimate real-world off-label use of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with type 1 diabetes, estimate rates of diabetic ketoacidosis (DKA), and compare them with DKA rates observed in sotagliflozin clinical trials. RESEARCH DESIGN AND METHODSWe identified initiators of SGLT2 inhibitors in the Sentinel System from March 2013 to June 2018, determined the prevalence of type 1 diabetes using a narrow and a broad definition, and measured rates of DKA using administrative claims data. Standardized incidence ratios (SIRs) were calculated using age-and sexspecific follow-up time in Sentinel and age-and sex-specific DKA rates from sotagliflozin trials 309, 310, and 312. RESULTSAmong 475,527 initiators of SGLT2 inhibitors, 0.50% and 0.92% met narrow and broad criteria for type 1 diabetes, respectively. Rates of DKA in the narrow and broad groups were 7.1/100 person-years and 4.3/100 person-years, respectively. Among patients who met narrow criteria for type 1 diabetes, rates of DKA were highest for patients aged 25-44 years, especially females aged 25-44 years (19.7/100 person-years). More DKA events were observed during off-label use of SGLT2 inhibitors in Sentinel than would be expected based on sotagliflozin clinical trials (SIR 5 1.83; 95% CI 1.45-2.28). CONCLUSIONSReal-world off-label use of SGLT2 inhibitors among patients with type 1 diabetes accounted for a small proportion of overall SGLT2 inhibitor use. However, the risk for DKA during off-label use was notable, especially among young, female patients. Although real-word rates of DKA exceeded the expectation based on clinical trials, results should be interpreted with caution due to differences in study methods, patient samples, and study drugs.Inhibition of sodium-glucose cotransporter 2 (SGLT2) in the proximal tubules suppresses renal glucose reabsorption, resulting in urinary glucose excretion and lowering of blood glucose in patients with diabetes. Canagliflozin (1), dapagliflozin (2), empagliflozin (3), and ertugliflozin (4) are SGLT2 inhibitors indicated as adjuncts to diet and exercise to improve glycemic control in adults with type 2 diabetes. Because of proven cardiovascular benefits (5,6

Purpose: To develop and validate algorithms to classify diabetes type in newly diagnosed pediatric patients with DM. Method: Data from the United States Department of Defense health system were used to identify patients aged 10 to 18 years with incident DM. Two independent sets of 200 children were randomly sampled for algorithm development and validation. Algorithms were developed based on clinical insight, published literature, and quantitative approaches. The actual DM type was ascertained via chart review.Finally, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated.Results: Among the 400 patients, mean age was 14.2 (±2.5 years), and 50% were female. The best performing algorithms were based on data available in claims. They consisted of several logical expressions based on one predictor or more, which classified patients by use of glucose-lowering drugs or testing, DM ICD-9 diagnosis codes, and comorbidities. The best performing T2DM and T1DM algorithms achieved 90% and 98% sensitivity, 95% and 95% specificity, 87% and 98% PPV, and 96% and 96% NPV, respectively. Conclusions:Our results suggest that claims algorithms can accurately identify newly diagnosed T1DM and T2DM pediatric patients, which can facilitate large database studies in children with T1DM and T2DM. However, external validation in other data sources is needed. KEYWORDS administrative electronic health data, algorithm, classification, pharmacoepidemiology, type 1 diabetes mellitus, type 2 diabetes mellitus

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