A dverse drug reactions (ADRs) are a major public health problem given such events are the most common type of injuries experienced by hospitalized patients. 1 ADRs may lead to hospitalization or occur during hospitalization and contribute to an increased length of stay. The recent focus on patient safety and the concern about the number of negative outcomes resulting from drug use, rather than the underlying diseases, has prompted health care professionals to take a critical look at these drug responses. A series of studies examined ADRs among hospitalized patients in the US and Australia 2-6 ; however, less research is available about these events in hospitalized patients in Canada. A US-based metaanalysis revealed that the incidence of serious ADRs in hospitalized patients was 2.1%, with the incidence in those newly admitted to a hospital 4.7%. The same study reported ADRs to be between the fourth and sixth leading cause of death. 6 Other studies have found that ADRs occurred in 2-20% of hospitalized patients. 4-6 Baker et al. provided a national estimate of the incidence of adverse events among adult patients in Canada (7.5 per 100 hospital admissions); after extrapolation, the number of hospital admissions attributed

P roprotein convertase subtilisin/kexin type 9 (PCSK9) has been recognized for its pivotal role in low-density lipoprotein-cholesterol (LDL-C) metabolism.1-3 PCSK9 plays a key role in the post-translational regulation 4 of hepatic LDLreceptor (LDL-R) activity, with the binding of PCSK9 to LDL-R promoting receptor degradation and impeding the ability of the liver to remove circulating LDL particles. 5,6 Because an elevated plasma concentration of LDL-C has been well established as an atherosclerotic risk factor, 7 PCSK9 presents itself as a potential novel predictor of cardiovascular risk and as a target for lipid-lowering therapy.Flow-mediated dilation (FMD) and carotid intima-media thickness (cIMT) are measures of vascular function and subclinical atherosclerosis 8,9 ; along with reactive hyperemic velocity time integral (VTI), 10 a more recently described marker of microvascular function, these 3 measures have been established as surrogate markers of atherosclerotic risk. The relationship between traditional cardiovascular risk factors and these vascular measures has been well studied, but the impact of PCSK9 concentration on vascular measures has yet to be evaluated. Consequently, this study sought to examine the impact of PCSK9 concentration on measures of vascular function in a population of healthy, middle-aged men. Predictors of PCSK9 concentration and the relationship between the proprotein and the cardiovascular events were also investigated as secondary objectives. Materials and MethodsMaterials and Methods are available in the online-only Data Supplement.© 2015 American Heart Association, Inc. Objective-Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays an important role in the modulation of low-density lipoprotein metabolism. This study was conducted to evaluate the relationship between serum PCSK9 concentrations and measures of vascular health, subclinical atherosclerosis, and adverse cardiovascular events. The relationship between traditional risk factors and PCSK9 concentrations was also examined. Approach and Results-The cohort consisted of 1527 middle-aged men enrolled in the Firefighters and Their Endothelium (FATE) study, who were free of vascular disease and followed up over a mean period of 7.2±1.7 years. Baseline evaluation included assessment of traditional cardiovascular risk factors and measurements of flow-mediated dilation, reactive hyperemic velocity time integral, and carotid intima-media thickness. Biochemical parameters, including serum PCSK9 concentrations, were analyzed to determine predictors of vascular measures and to evaluate the role of PCSK9 in the occurrence of adverse cardiovascular events. Multivariate linear regression analyses indicated that body mass index, insulin, low-density lipoprotein-cholesterol, and triglycerides were independent predictors of PCSK9. Further modeling revealed no correlation between PCSK9 concentration and carotid intima media thickness, flow-mediated dilation, or reactive hyperemic velocity time integral. Analyses indicated no signific...

The burden of illness from diabetes in NL is considerable. Using cause-eliminated LE and HALE provides a robust approach for assessing HRQOL that may have important implications for diabetes surveillance, prevention, and management strategies.

The rate of unintentional injury among children and adolescents in Aboriginal communities is higher than non-Aboriginal communities. Sex (male) and place of residence (Aboriginal communities) were strong predictors of unintentional injury in NL.

Adult ADE-related ED visits are frequent in Newfoundland and Labrador, and in many cases are preventable. Further efforts are needed to reduce the occurrence of preventable ADEs leading to ED visits.

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BackgroundAlthough it is well-known that early detection of colorectal cancer (CRC) is important for optimal patient survival, the relationship of patient and health system factors with delayed diagnosis are unclear. The purpose of this study was to identify the demographic, clinical and healthcare factors related to mode of CRC detection and length of the diagnostic interval.MethodsAll residents of Alberta, Canada diagnosed with first-ever incident CRC in years 2004–2010 were identified from the Alberta Cancer Registry. Population-based administrative health datasets, including hospital discharge abstract, ambulatory care classification system and physician billing data, were used to identify healthcare services related to CRC diagnosis. The time to diagnosis was defined as the time from the first CRC-related healthcare visit to the date of CRC diagnosis. Mode of CRC detection was classified into three groups: urgent, screen-detected and symptomatic. Quantile regression was performed to assess factors associated with time to diagnosis.Results9626 patients were included in the study; 25% of patients presented as urgent, 32% were screen-detected and 43% were symptomatic. The median time to diagnosis for urgent, screen-detected and symptomatic patients were 6 days (interquartile range (IQR) 2–14 days), 74 days (IQR 36–183 days), 84 days (IQR 39–223 days), respectively. Time to diagnosis was greater than 6 months for 27% of non-urgent patients. Healthcare factors had the largest impact on time to diagnosis: 3 or more visits to a GP increased the median by 140 days whereas 2 or more visits to a GI-specialist increased it by 108 days compared to 0–1 visits to a GP or GI-specialist, respectively.ConclusionA large proportion of CRC patients required urgent work-up or had to wait more than 6 months for diagnosis. Actions are needed to reduce the frequency of urgent presentation as well as improve the timeliness of diagnosis. Findings suggest a need to improve coordination of care across multiple providers.

Objective We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. Methods We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist. Results Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73–0.77] for the traditional regression-based models and 0.76 [0.72–0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models. Conclusion We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.