Severe (level 3) hypoglycaemia occurrence in a real-world cohort of adults with type 1 or 2 diabetes mellitus (iNPHORM, United States).

AIMS: Among adults with insulin- and/or secretagogue-treated diabetes in the United States, very little is known about the real-world descriptive epidemiology of iatrogenic severe (level 3) hypoglycaemia. Addressing this gap, we collected primary, longitudinal data to quantify the absolute frequency of events as well as incidence rates and proportions. MATERIALS AND METHODS: iNPHORM is a US-wide, 12-month ambidirectional panel survey (2020-2021). Adults with type 1 diabetes mellitus (T1DM) or insulin- and/or secretagogue-treated type 2 diabetes mellitus (T2DM) were recruited from a probability-based internet panel. Participants completing ≥1 follow-up questionnaire(s) were analysed. RESULTS: Among 978 respondents [T1DM 17%; mean age 51 (SD 14.3) years; male: 49.6%], 63% of level 3 events were treated outside the health care system (e.g. by family/friend/colleague), and <5% required hospitalization. Following the 12-month prospective period, one-third of individuals reported ≥1 event(s) [T1DM 44.2% (95% CI 36.8%-51.8%); T2DM 30.8% (95% CI 28.7%-35.1%), p = .0404, α = 0.0007]; and the incidence rate was 5.01 (95% CI 4.15-6.05) events per person-year (EPPY) [T1DM 3.57 (95% CI 2.49-5.11) EPPY; T2DM 5.29 (95% CI 4.26-6.57) EPPY, p = .1352, α = 0.0007]. Level 3 hypoglycaemia requiring non-transport emergency medical services was more common in T2DM than T1DM (p < .0001, α = 0.0016). In total, >90% of events were experienced by <15% of participants. CONCLUSIONS: iNPHORM is one of the first long-term, prospective US-based investigations on level 3 hypoglycaemia epidemiology. Our results underscore the importance of participant-reported data to ascertain its burden. Events were alarmingly frequent, irrespective of diabetes type, and concentrated in a small subsample.

Development and validation of a real-world model to predict 1-year Level 3 (severe) hypoglycaemia risk in adults with diabetes (the iNPHORM study, United States).

AIMS: We aimed to develop and internally validate a real-world prognostic model for Level 3 hypoglycaemia risk compatible with outpatient care in the United States. MATERIALS AND METHODS: iNPHORM is a 12-month, US-based panel survey. Adults (18-90 years old) with type 1 diabetes mellitus or insulin- and/or secretagogue-treated type 2 diabetes mellitus were recruited from a nationwide, probability-based internet panel. Among participants completing ≥ 1 follow-up questionnaire(s), we modelled 1-year Level 3 hypoglycaemia risk using Andersen and Gill's Cox survival and penalized regression with multiple imputation. Candidate variables were selected for their clinical relevance and ease of capture at point-of-care. RESULTS: In total, 986 participants [type 1 diabetes mellitus: 17%; men: 49.6%; mean age: 51 (SD: 14.3) years] were analysed. Across follow-up, 035.1 (95% CI: 32.2-38.1)% reported ≥1 Level 3 event(s), and the rate was 5.0 (95% CI: 4.1-6.0) events per person-year. Our final model showed strong discriminative validity and parsimony (optimism corrected c-statistic: 0.77). Numerous variables were selected: age; sex; body mass index; marital status; level of education; insurance coverage; race; ethnicity; food insecurity; diabetes type; glycated haemoglobin value; glycated haemoglobin variability; number, type and dose of various medications; number of SH events requiring hospital care (past year and over follow-up); type and number of comorbidities and complications; number of diabetes-related health care visits (past year); use of continuous/flash glucose monitoring; and general health status. CONCLUSIONS: iNPHORM is the first US-based primary prognostic study on Level 3 hypoglycaemia. Future model implementation could potentiate risk-tailored strategies that reduce real-world event occurrence and overall diabetes burden.

Real-World Effects of Second-Generation Versus Earlier Intermediate/Basal Insulin Analogues on Rates of Hypoglycemia in Adults with Type 1 and 2 Diabetes (iNPHORM, US).

INTRODUCTION: Second-generation basal insulin analogues have been shown to reduce hypoglycemia in several trials and observational studies of select populations; however, it remains unclear whether these results persist in real-world settings. Using self-reported hypoglycemia events, we assessed whether second-generation basal insulin analogues reduce rates of hypoglycemia events (non-severe/severe; overall/daytime/nocturnal) compared to earlier intermediate/basal insulin analogues among people with insulin-treated type 1 or 2 diabetes. METHODS: We used prospectively collected data from the Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-World Models (iNPHORM) panel survey. This US-wide, 1-year internet-based survey assessed hypoglycemia experiences and related sociodemographic and clinical characteristics of people with diabetes (February 2020-March 2021). We estimated population-average rate ratios for hypoglycemia comparing second-generation to earlier intermediate/basal insulin analogues using negative binomial regression, adjusting for confounders. Within-person variability of repeated observations was addressed with generalized estimating equations. RESULTS: Among iNPHORM participants with complete data, N = 413 used an intermediate/basal insulin analogue for ≥ 1 month during follow-up. After adjusting for baseline and time-updated confounders, average second-generation basal insulin analogue users experienced a 19% (95% CI 3-32%, p = 0.02) lower rate of overall non-severe hypoglycemia and 43% (95% CI 26-56%, p < 0.001) a lower rate of nocturnal non-severe hypoglycemia compared to earlier intermediate/basal insulin users. Overall severe hypoglycemia rates were similar among second-generation and earlier intermediate/basal insulin users (p = 0.35); however, the rate of severe nocturnal hypoglycemia was reduced by 44% (95% CI 10-65%, p = 0.02) among second-generation insulin users compared to earlier intermediate/basal insulin users. CONCLUSION: Our real-world results suggest second-generation basal insulin analogues reduce rates of hypoglycemia, especially nocturnal non-severe and severe events. Whenever possible and feasible, clinicians should prioritize prescribing these agents over first-generation basal or intermediate insulin in people with type 1 and 2 diabetes.

Tracking teen food marketing: Participatory research to examine persuasive power and platforms of exposure.

Food marketing has long been recognized to influence children's food preferences and consumption patterns, yet only in recent years have teenagers been recognized as a uniquely vulnerable audience for food marketing appeals. Marketing pressures on teenagers around food promotion continue to intensify, yet little is known about the marketing channels and specific persuasive appeals targeting this audience. Given this research gap, this participatory research study engages teenagers to capture the food marketing targeting them and to identify its persuasive "power" and platforms of exposure. Using a specially designed mobile app called GrabFM! (Grab Food Marketing!) teenagers (ages 13-17, n = 309) identified and tagged examples of teen-targeted food marketing in their physical and digital environments over a 7-day period. Results reveal that: 1) digital platforms dominate teen-targeted food marketing, with over three quarters of the ads found on Instagram, Snapchat, TikTok, ad YouTube; 2) branded beverages, fast food, and candy/chocolate comprise the majority (72%) of ads; and 3) the most powerful techniques for attracting teens attention are visual style, special offer and theme. In 40% of advertisements submitted, teenagers used only one indicator to identify "teen-targeted", although older teenagers (ages 15-17) were more likely to report multiple indicators per ad. This study provides important insights into the platforms targeting teenagers (and their relative importance), the food products endorsed, and the specific appeals that teenagers find persuasive. For the purposes of monitoring, it is helpful to know that digital platforms comprise the majority of teen-directed food promotions, and that the Big Food brands have been joined by countless smaller players to sell food to teens.

An introduction to machine learning for classification and prediction.

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

Understanding data provenance when using electronic medical records for research: Lessons learned from the Deliver Primary Healthcare Information (DELPHI) database.

Introduction: We set out to assess the impact of Choosing Wisely Canada recommendations (2014) on reducing unnecessary health investigations and interventions in primary care across Southwestern Ontario. Methods: We used the Deliver Primary Healthcare Information (DELPHI) database, which stores deidentified electronic medical records (EMR) of nearly 65,000 primary care patients across Southwestern Ontario. When conducting research using EMR data, data provenance (i.e., how the data came to be) should first be established. We first considered DELPHI data provenance in relation to longitudinal analyses, flagging a change in EMR software that occurred during 2012 and 2013. We attempted to link records between EMR databases produced by different software using probabilistic linkage and inspected 10 years of data in the DELPHI database (2009 to 2019) for data quality issues, including comparability over time. Results: We encountered several issues resulting from this change in EMR software. These included limited linkage of records between software without a common identifier; data migration issues that distorted procedure dates; and unusual changes in laboratory test and medication prescription volumes. Conclusion: This study reinforces the necessity of assessing data provenance and quality for new research projects. By understanding data provenance, we can anticipate related data quality issues such as changes in EMR data over time-which represent a growing concern as longitudinal data analyses increase in feasibility and popularity.

Real-world risk factors of confirmed or probable COVID-19 in Americans with diabetes: A prospective, community-based study (iNPHORM).

INTRODUCTION: Americans with diabetes are clinically vulnerable to worse COVID-19 outcomes; thus, insight into how to prevent infection is imperative. Using longitudinal, prospective data from the real-world iNPHORM study, we identify the intrinsic and extrinsic risk factors of confirmed or probable COVID-19 in people with type 1 or 2 diabetes. METHODS: The iNPHORM study recruited 1206 Americans (18-90 years) with insulin- and/or secretagogue-treated type 1 or 2 diabetes from a probability-based internet panel. Online questionnaires (screener, baseline and 12 monthly follow-ups) assessed COVID-19 incidence and various plausible intrinsic and extrinsic factors. Multivariable Cox regression was used to model the rate of COVID-19 (confirmed or probable). Risk factors were selected using a repeated backwards-selection 'voting' procedure. RESULTS: A sub-sample of 817 iNPHORM participants (type 1 diabetes: 16.9%; age: 52.1 [SD: 14.2] years; female: 50.2%) was analysed between May 2020 and March 2021. During this period, 13.7% reported confirmed or probable COVID-19. Age, body mass index, number of chronic comorbidities, most recent A1C, past severe hypoglycaemia, and employment status were selected in our final model. Body mass index ≥30 kg/m2 versus <30 kg/m2 (HR 1.63 [1.05; 2.52]95% CI ), and increased number of comorbidities (HR 1.16 [1.05; 1.27]95% CI ) independently predicted COVID-19 incidence. Marginally significant effects were observed for overall A1C (p = .06) and employment status (p = .07). CONCLUSIONS: This is the first US-based epidemiologic investigation to characterize community-based COVID-19 susceptibility in diabetes. Our results reveal specific and promising avenues to prevent COVID-19 in this at-risk population. CLINICALTRIALS: gov Identifier: NCT04219514.

Predicting Real-world Hypoglycemia Risk in American Adults With Type 1 or 2 Diabetes Mellitus Prescribed Insulin and/or Secretagogues: Protocol for a Prospective, 12-Wave Internet-Based Panel Survey With Email Support (the iNPHORM [Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models] Study).

BACKGROUND: Hypoglycemia prognostic models contingent on prospective, self-reported survey data offer a powerful avenue for determining real-world event susceptibility and interventional targets. OBJECTIVE: This protocol describes the design and implementation of the 1-year iNPHORM (Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models) study, which aims to measure real-world self-reported severe and nonsevere hypoglycemia incidence (daytime and nocturnal) in American adults with type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues, and develop and internally validate prognostic models for severe, nonsevere daytime, and nonsevere nocturnal hypoglycemia. As a secondary objective, iNPHORM aims to quantify the effects of different antihyperglycemics on hypoglycemia rates. METHODS: iNPHORM is a prospective, 12-wave internet-based panel survey that was conducted across the United States. Americans (aged 18-90 years) with self-reported type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues were conveniently sampled via the web from a pre-existing, closed, probability-based internet panel (sample frame). A sample size of 521 baseline responders was calculated for this study. Prospective data on hypoglycemia and potential prognostic factors were self-assessed across 14 closed, fully automated questionnaires (screening, baseline, and 12 monthly follow-ups) that were piloted using semistructured interviews (n=3) before fielding; no face-to-face contact was required as part of the data collection. Participant responses will be analyzed using multivariable count regression and machine learning techniques to develop and internally validate prognostic models for 1-year severe and 30-day nonsevere daytime and nocturnal hypoglycemia. The causal effects of different antihyperglycemics on hypoglycemia rates will also be investigated. RESULTS: Recruitment and data collection occurred between February 2020 and March 2021 (ethics approval was obtained on December 17, 2019). A total of 1694 participants completed the baseline questionnaire, of whom 1206 (71.19%) were followed up for 12 months. Most follow-up waves (10,470/14,472, 72.35%) were completed, translating to a participation rate of 179% relative to our target sample size. Over 70.98% (856/1206) completed wave 12. Analyses of sample characteristics, quality metrics, and hypoglycemia incidence and prognostication are currently underway with published results anticipated by fall 2022. CONCLUSIONS: iNPHORM is the first hypoglycemia prognostic study in the United States to leverage prospective, longitudinal self-reports. The results will contribute to improved real-world hypoglycemia risk estimation and potentially safer, more effective clinical diabetes management. TRIAL REGISTRATION: ClinicalTrials.gov NCT04219514; https://clinicaltrials.gov/ct2/show/NCT04219514. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33726.

COVID-19 hinterland: surveilling the self-reported impacts of the pandemic on diabetes management in the USA (cross-sectional results of the iNPHORM study).

MAIN OBJECTIVE: To determine how and to what extent COVID-19 has affected real-world, self-reported glycaemic management in Americans with type 1 or type 2 diabetes taking insulin and/or secretagogues, with or without infection. DESIGN: A cross-sectional substudy using data from the Investigating Novel Predictions of Hypoglycemia Occurrence using Real-world Models panel survey. SETTING: USA. PARTICIPANTS: Americans 18-90 years old with type 1 or 2 diabetes taking insulin and/or secretagogues were conveniently sampled from a probability-based internet panel. PRIMARY OUTCOME MEASURE: A structured, COVID-19-specific questionnaire was administered to assess the impact of the pandemic (irrespective of infection) on socioeconomic, behavioural/clinical and psychosocial aspects of glycaemic management. RESULTS: Data from 667 respondents (type 1 diabetes: 18%; type 2 diabetes: 82%) were analysed. Almost 25% reported A1c values ≥8.1%. Rates of severe and non-severe hypoglycaemia were 0.68 (95% CI 0.5 to 0.96) and 2.75 (95% CI 2.4 to 3.1) events per person-month, respectively. Ten respondents reported a confirmed or probable COVID-19 diagnosis. Because of the pandemic, 24% of respondents experienced difficulties affording housing; 28% struggled to maintain sufficient food to avoid hypoglycaemia; and 19% and 17% reported challenges accessing diabetes therapies and testing strips, respectively. Over one-quarter reported issues retrieving antihyperglycaemics from the pharmacy and over one-third reported challenges consulting with diabetes providers. The pandemic contributed to therapeutic non-adherence (14%), drug rationing (17%) and reduced monitoring (16%). Many struggled to keep track, and in control, of hypoglycaemia (12%-15%) and lacked social support to help manage their risk (19%). Nearly half reported decreased physical activity. Few statistically significant differences were observed by diabetes type. CONCLUSIONS: COVID-19 was found to cause substantial self-reported deficiencies in glycaemic management. Study results signal the need for decisive action to restabilise routine diabetes care in the USA. TRIAL REGISTRATION NUMBER: NCT04219514.

Development of a prognostic prediction model to estimate the risk of multiple chronic diseases: constructing a copula-based model using Canadian primary care electronic medical record data.

INTRODUCTION: The ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Current methods for prognostic prediction modelling are insufficient for the estimation of risk for multiple outcomes, as they do not properly capture the dependence that exists between outcomes. OBJECTIVES: We developed a multivariate prognostic prediction model for the 5-year risk of diabetes, hypertension, and osteoarthritis that quantifies and accounts for the dependence between each disease using a copula-based model. METHODS: We used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2009 onwards, a collection of electronic medical records submitted by participating primary care practitioners across Canada. We identified patients 18 years and older without all three outcome diseases and observed any incident diabetes, osteoarthritis, or hypertension within 5-years, resulting in a large retrospective cohort for model development and internal validation (n=425,228). First, we quantified the dependence between outcomes using unadjusted and adjusted Ø coefficients. We then estimated a copula-based model to quantify the non-linear dependence between outcomes that can be used to derive risk estimates for each outcome, accounting for the observed dependence. Copula-based models are defined by univariate models for each outcome and a dependence function, specified by the parameter θ. Logistic regression was used for the univariate models and the Frank copula was selected as the dependence function. RESULTS: All outcome pairs demonstrated statistically significant dependence that was reduced after adjusting for covariates. The copula-based model yielded statistically significant θ parameters in agreement with the adjusted and unadjusted Ø coefficients. Our copula-based model can effectively be used to estimate trivariate probabilities. DISCUSSION: Quantitative estimates of multimorbidity risk inform discussions between patients and their primary care practitioners around prevention in an effort to reduce the incidence of multimorbidity.

Antimicrobial Prescribing during Infant Hospital Admissions in a Birth Cohort in Dhaka, Bangladesh.

Empirical antimicrobial use is common in hospitalized infants and may contribute to antimicrobial resistance in low- and middle-income countries. In this observational birth cohort study nested in a randomized controlled trial in Dhaka, Bangladesh, inpatient antimicrobial prescription data were extracted from serious adverse event forms completed for hospitalizations of infants (0-12 months of age). The primary outcome was the proportion of inpatient admissions where systemic antimicrobials were prescribed. Infant and hospitalization-related factors associated with antimicrobial prescriptions were determined. Among 1254 infants, there were 448 admissions to 32 facilities from 2014 to 2016. Antimicrobials were prescribed in 73% of admissions with a mean antimicrobial exposure rate of 0.25 antimicrobials per day of admission [95% confidence intervals (95% CIs): 0.24-0.27]. The most common antibiotics were aminoglycosides (29%), penicillins (26%) and third-generation cephalosporins (25%). In all, 58% of antibiotics were classified as 'access', 38% 'watch' and 1% 'reserve' using the World Health Organization (WHO) Essential Medicines List classification. WHO-recommended antimicrobial regimens were used in 68% of neonatal sepsis and 9% of lower respiratory tract infection (LRTI) admissions. 'Watch' antimicrobials were used in 26% of neonatal sepsis and 76% of LRTI admissions. Compared with private facilities, antimicrobial prescription rates were lower at government [rate ratio (RR) 0.71; 95% CI: 0.61-0.83] and charitable facilities (RR 0.39; 95% CI: 0.28-0.53), after adjustment for household wealth index and parental education. Younger infant age, older maternal age and longer admission were associated with higher prescription rates. These findings highlight the need for paediatric antimicrobial stewardship programs in Bangladesh.

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology.

BACKGROUND: We developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction. METHODS: We used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived. RESULTS: The resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology. CONCLUSIONS: If the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.

Manipulation of Breath Alcohol Tests: Can Specific Techniques Alter Breath Alcohol Content?

OBJECTIVE: The most common form of measurement of breath alcohol content (BrAC) is through the use of a diode catheter. This study aims to test the accuracy of breath alcohol analysis through different manipulations. METHODS: BrAC was measured after individuals consumed each standardized beer until they reached a 0.1 BrAC. Then, the individuals were breath analyzed while not providing full effort, using the side of their mouths, immediately after hyperventilating, 5 and 10 min after hyperventilation, immediately after a sip of water, and 5 min after that water. RESULTS: There were 54 individuals. Two baselines were used as the controls. The first baseline was a mean BrAC of. 104 with standard deviation of +0.008 for poor effort, side of mouth, and hyperventilating. The second baseline used for drinking water manipulations was a BrAC of 0.099 + 0.11. Poor effort (mean + standard deviation: 0.099 ± 0.10, P < 0.0001), immediately after hyperventilating (0.086 ± 0.011, P < 0.0001), 5 min after hyperventilating (0.099 ± 0.009, P < 0.0001), and 10 min after hyperventilating (0.099 ± 0.011, P < 0.0001) were all found to be statistically significant in their ability to lower BrAC. Both immediately after water (0.084 ± 0.011, P < 0001) and 5 min after drinking water (0.096 ± 0.13, P < 0.0001) were found to have significantly altered the BrAC. CONCLUSION: Our research shows that manipulations can alter BrAC readings significantly. Breath analyzer operators should be cognizant of these methods that may lead to falsely lower BrAC readings.

Enhancing Chemotherapy Capabilities in Rural Hospitals: Implementation of a Telechemotherapy Model (QReCS) in North Queensland, Australia.

INTRODUCTION: The Queensland Remote Chemotherapy Supervision (QReCS) model enables rural nurses to administer chemotherapy in smaller rural towns under supervision by health professionals from larger centers using telehealth. Its implementation began in North Queensland, Australia (population, 650,000), in 2014 between two regional cancer centers (Townsville and Cairns as primary sites) and six rural sites (125 to 1,000 kilometers from primary sites). Our study examined the implementation processes, feasibility, and safety of this model. METHODS: Details of implementation and patients' clinical details for the period of 2014 to 2016 for descriptive analysis were extracted from telechemotherapy project notes and oncology information systems of North Queensland, respectively. RESULTS: After a successful pilot study in Townsville Cancer Centre, statewide rural and cancer networks of Queensland Health, in collaboration with clinicians and managers across the state of Queensland, developed the QReCS model and a guide for operationalizing it. QReCS was implemented at six sites from 2014 to 2016. Main enablers across North Queensland included collaboration among clinicians and managers, availability of common electronic medical records, funding from Queensland Health, and installation of telehealth infrastructure by statewide telehealth services. Main barriers included turnover of senior management and nursing staff at two rural towns. Sixty-two patients received 327 cycles of low- to medium-risk chemotherapy agents. Rates of treatment delays, adverse events, and hospital admissions were similar to those in face-to-face care. CONCLUSION: Implementation of the QReCS model across a large geographic region is feasible with acceptable safety profiles. Leadership by and collaboration among clinicians and managers, adequacy of resources and common governance are key enablers.

Real-world crude incidence of hypoglycemia in adults with diabetes: Results of the InHypo-DM Study, Canada.

OBJECTIVE: Very few real-world studies have been conducted to assess the incidence of diabetes-related hypoglycemia. Moreover, there is a paucity of studies that have investigated hypoglycemia among people taking secretagogues as a monotherapy or in combination with insulin. Accordingly, our research team developed and validated the InHypo-DM Person with Diabetes Mellitus Questionnaire (InHypo-DMPQ) with the aim of capturing the real-world incidence of self-reported, symptomatic hypoglycemia. The questionnaire was administered online to a national sample of Canadians (≥18 years old) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) treated with insulin and/or insulin secretagogues. RESEARCH DESIGN AND METHODS: Self-report data obtained from the InHypo-DMPQ were descriptively analyzed to ascertain the crude incidence proportions and annualized incidence densities (rates) of 30-day retrospective non-severe and 1-year retrospective severe hypoglycemia, including daytime and nocturnal events. RESULTS: A total of 552 people (T2DM: 83%; T1DM: 17%) completed the questionnaire. Over half (65.2%) of the total respondents reported experiencing at least one event (non-severe or severe) at an annualized crude incidence density of 35.1 events per person-year. The incidence proportion and rate of non-severe events were higher among people with T1DM versus T2DM (77% and 55.7 events per person-year vs 54% and 28.0 events per person-year). Severe hypoglycemia was reported by 41.8% of all respondents, at an average rate of 2.5 events per person-year. CONCLUSIONS: The results of the InHypo-DMPQ, the largest real-world investigation of hypoglycemia epidemiology in Canada, suggest that the incidence of hypoglycemia among adults with diabetes taking insulin and/or insulin secretagogues is higher than previously thought.

Rapid Web-Based Platform for Assessment of Orthopedic Surgery Patient Care Milestones: A 2-Year Validation.

OBJECTIVE: To determine the validity, feasibility, and responsiveness of a new web-based platform for rapid milestone-based evaluations of orthopedic surgery residents. SETTING: Single academic medical center, including a trauma center and pediatrics tertiary hospital. PARTICIPANTS: Forty residents (PG1-5) in an orthopedic residency program and their faculty evaluators. METHODS: Residents and faculty were trained and supported in the use of a novel trainee-initiated web-based evaluation system. Residents were encouraged to use the system to track progress on patient care subcompetencies. Two years of prospectively collected data were reviewed from residents at an academic program. The primary outcome was Spearman's rank correlation between postgraduate year (PGY) and competency level achieved as a measure of validity. Secondary outcomes assessed feasibility, resident self-evaluation versus faculty evaluation, the distributions among subcompetencies, and responsiveness over time. RESULTS: Between February 2014 and February 2016, 856 orthopedic surgery patient care subcompetency evaluations were completed (1.2 evaluations per day). Residents promptly requested feedback after a procedure (median = 0 days, interquartile range: 0-2), and faculty responded within 2 days in 51% (median = 2 days, interquartile range: 0-13). Primary outcome showed a correlation between PGY and competency level (r = 0.78, p < 0.001), with significant differences in competency among PGYs (p < 0.001 by Kruskal-Wallis rank sum test). Self-evaluations by residents substantially agreed with faculty-assigned competency level (weighted Cohen's κ = 0.72, p < 0.001). Resident classes beginning the study as PGY1, 2, and 3 separately demonstrated gains in competency over time (Spearman's rank correlation 0.39, 0.60, 0.59, respectively, each p < 0.001). There was significant variance in the number of evaluations submitted per subcompetency (median = 43, range: 6-113) and competency level assigned (p < 0.01). CONCLUSIONS: Rapid tracking of trainee competency with milestone-based evaluations in a learner-centered mobile platform demonstrated validity, feasibility, and responsiveness. Next Accreditation System-mandated data may be efficiently collected and used for trainee and program self-study.

Antibacterial Activity of Partially Oxidized Ag/Au Nanoparticles against the Oral Pathogen Porphyromonas gingivalis W83.

Advances in nanotechnology provide opportunities for the prevention and treatment of periodontal disease. While physicochemical properties of Ag containing nanoparticles (NPs) are known to influence the magnitude of their toxicity, it is thought that nanosilver can be made less toxic to eukaryotes by passivation of the NPs with a benign metal. Moreover, the addition of other noble metals to silver nanoparticles, in the alloy formulation, is known to alter the silver dissolution behavior. Thus, we synthesized glutathione capped Ag/Au alloy bimetallic nanoparticles (NPs) via the galvanic replacement reaction between maltose coated Ag NPs and chloroauric acid (HAuCl4) in 5% aqueous triblock F127 copolymer solution. We then compared the antibacterial activity of the Ag/Au NPs to pure Ag NPs on Porphyromonas gingivalis W83, a key pathogen in the development of periodontal disease. Only partially oxidized glutathione capped Ag and Ag/Au (Au:Ag≈0.2) NPs inhibited the planktonic growth of P. gingivalis W83. This effect was enhanced in the presence of hydrogen peroxide, which simulates the oxidative stress environment in the periodontal pocket during chronic inflammation.

Resolution of ipilimumab induced severe hepatotoxicity with triple immunosuppressants therapy.

We describe a case of a patient from Far North Queensland, Australia, with life-threatening hepatotoxicity caused by ipilimumab induced immune-related adverse events (irAEs). Our patient presented with non-specific symptoms including malaise, lethargy and fevers. Her work up revealed acute hepatitis, which was presumed to be related to ipilimumab treatment for her metastatic melanoma. Causality for ipilimumab was assessed with the CIOMS scale (Council for International Organizations of Medical Sciences) and provided a causality level of 'highly probable' (score +9). She was started on methylprednisolone as per guidelines for ipilimumab induced irAEs. On the second day of treatment her transaminases enzymes unexpectedly rose several hundred times. Investigations for other causes of acute hepatitis including abdominal imaging were negative. She was started up front on equine antithymocyte globulin, mycophenolate moefetil and continued on methylprednisolone. She recovered clinically and biochemically in 2 weeks and continues to remain well.

Dehiscence of corticosteroid-induced abdominal striae in a 14-year-old boy treated with bevacizumab for recurrent glioblastoma.

Corticosteroids have been the mainstay for management of cerebral edema caused by leaky angiogenic vessels associated with high-grade brain tumors since the early 1960s. Chronic corticosteroid use can cause iatrogenic Cushing syndrome, which is associated with weight gain and abdominal striae (striae distensae). The anti-vascular endothelial growth factor therapy, bevacizumab, has recently been introduced for the management of recurrent glioblastoma. Vascular endothelial growth factor plays multiple roles in wound healing, including promoting angiogenesis, acting as a chemo-attractant for inflammatory cells, and stimulating collagen production. We report the first pediatric case of a 14-year-old boy with corticosteroid-induced abdominal striae who developed ulceration and dehiscence of the striae following the introduction of bevacizumab therapy. The combination of high-dose corticosteroids and anti-vascular endothelial growth factor therapy may cause significant complications, especially in children who are susceptible to abdominal striae and therefore should be avoided.

What are patients' priorities when facing the end of life? A critical review.

This critical review aimed to investigate what patients' priorities are when facing the end of life, in order to gain further understanding of this issue. Academic databases were searched using key terms, and through a method of elimination and deduction using specific inclusion/exclusion criteria, suitable research studies were found. These articles were then assessed for their quality, and specific data was extracted from the final selection using appropriate information-gathering tools. In these final four articles the methodological processes used to explore terminally ill patients' needs were generally appropriate, although there was a lack of reflexivity (researcher reflection on the experience). Useful narrative themes were produced from all four papers for further discussion. The patients had similar priorities across all four articles, which were related to understanding and accepting their changing health status, the need to hold on to some normality in life, the need to feel supported by friends and family and to know they will be taken care of after the death, and the need to have good and trusting relationships with health professionals.