Trends in end digit preference for blood pressure and associations with cardiovascular outcomes in Canadian and UK primary care : a retrospective observational study

This study received funding through a grant by the North York Genera Hospital Foundation’s Exploration Fund. MG held an investigator award from the Department of Family and Community Medicine, University of Toronto and was supported by a research stipend from North York General Hospital. The Canadian Primary Care Sentinel Surveillance Network was a committee of the College of Family Physicians of Canada and was funded through a contribution agreement with the Public Health Agency of Canada.Objectives: To study systematic errors in recording blood pressure (BP) as measured by end digit preference (EDP); to determine associations between EDP, uptake of Automated Office BP (AOBP) machines and cardiovascular outcomes. Design: Retrospective observational study using routinely collected electronic medical record data from 2006 to 2015 and a survey on year of AOBP acquisition in Toronto, Canada in 2017. Setting: Primary care practices in Canada and the UK. Participants: Adults aged 18 years or more. Main outcome measures: Mean rates of EDP and change in rates. Rates of EDP following acquisition of an AOBP machine. Associations between site EDP levels and mean BP. Associations between site EDP levels and frequency of cardiovascular outcomes. Results:  707 227 patients in Canada and 1 558 471 patients in the UK were included. From 2006 to 2015, the mean rate of BP readings with both systolic and diastolic pressure ending in zero decreased from 26.6% to 15.4% in Canada and from 24.2% to 17.3% in the UK. Systolic BP readings ending in zero decreased from 41.8% to 32.5% in the 3 years following the purchase of an AOBP machine. Sites with high EDP had a mean systolic BP of 2.0 mm Hg in Canada, and 1.7 mm Hg in the UK, lower than sites with no or low EDP. Patients in sites with high levels of EDP had a higher frequency of stroke (standardised morbidity ratio (SMR) 1.15, 95% CI 1.12 to 1.17), myocardial infarction (SMR 1.16, 95% CI 1.14 to 1.19) and angina (SMR 1.25, 95% CI 1.22 to 1.28) than patients in sites with no or low EDP. Conclusions:  Acquisition of an AOBP machine was associated with a decrease in EDP levels. Sites with higher rates of EDP had lower mean BPs and a higher frequency of adverse cardiovascular outcomes. The routine use of manual office-based BP measurement should be reconsidered.Publisher PDFPeer reviewe

Piloting electronic screening forms in primary care : findings from a mixed methods study to identify patients eligible for low dose CT lung cancer screening

This study was funded by The Ontario Cancer Screening Research Network.Background:  Recent evidence suggests that screening with low dose computed tomography (LDCT) scans significantly reduces mortality from lung cancer. However, optimal methods to identify potentially eligible patients in primary care are not known. Using brief electronic screening forms administered prior to a primary care visit is a strategy to identify high risk, asymptomatic patients eligible for LDCT screening. The objective of this study was to compare the acceptability and feasibility of using brief electronic versus paper screening forms to identify eligible patients at high risk of developing lung cancer in primary care. Methods:  A mixed method pilot comparative study was conducted in primary care. Practices were allocated to an electronic form (e-form) group or a paper-based form (p-form) group. Allocation was randomly assigned for the first practice then by alternation. Patients in the e-form practices completed forms at home via the web or in the waiting room on a tablet. Patients in p-form practices completed forms in waiting rooms. Interviews were conducted with patients, administrators, and primary care physicians (PCPs) about their experiences. Results:  Six of 30 (20%) eligible practices agreed to participate. Over the 16-week study period, a total of 831 of an expected 1442 patients (58%) aged 55–74 years were enrolled; 573/690 (83%) patients in the e-form group and 258/752 (34%) in the p-form group. Of the 573 participants in the e-form group, 335 (58%) completed forms via the web; 238 (29%) did so via tablet. Twenty-four interviews were conducted with 15 patients, 5 administrative staff and 4 PCPs. Patients were willing to discuss lung cancer screening eligibility with their PCP. Staff members expressed low administrative burden except for an extra step to link appointment information to patient demographics to identify eligible patients. PCPs indicated that forms were reminders to discuss smoking cessation. PCPs in the e-form group reported that patients asked questions about screening. Conclusion: There was fairly low uptake by primary care practices. For e-forms to be feasible in practice workflow, electronic medical record software needs to link appointment information with patient eligibility requirements. The use of brief pre-consultation electronic screening forms for LDCT eligibility encouraged PCPs to discuss smoking cessation with patients.Publisher PDFPeer reviewe

S-RAP: relevance-aware QoS prediction in web-services and user contexts

With quick advancement in web technology, web-services offered on internet are growing quickly, making it challenging for users to choose a web-service fit to their needs. Recommender systems save users the hassle of going through a range of products by product recommendations through analytical techniques on historical data of user experiences of the available items/products. Research efforts provide several methods for web-service recommendation in which QoS-related attributes play primary role such as response-time, throughput, security, privacy and web-service-delivery. Derivable attributes including, user-trustworthiness and web-services reputation in contexts of users and web-services can also affect the QoS prediction. The proposed research focuses on a web-service recommendation model, S-RAP, for QoS prediction based on derivable attributes to predict QoS of a web-service that a user who has not invoked it before would experience. Services-Relevance attribute is proposed in this publication, which emphasizes on employing the historical data and extracting the degree of relevance in the users and web-services context to predict the QoS values for a user. The proposed system produces satisfactorily accurate rating predictions in the experiments evaluated by the Mean Absolute Error and Normalized Mean Absolute Error metrics. The results compared with state-of-the-art models show a relative improvement by 4.0%

Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column

Although numerous works have been done, most of the studies in fault diagnosis are limited to single fault type at a time. Majority of the works reported in the literature do not extend the diagnosis of the root cause of the fault for simultaneous faults specifically in the distillation column. However, an industrial system is susceptible to more than one fault at a time, which may or may not be interrelated. These faults not only reduce the diagnosis performance but also increase the computational complexity of the diagnosis algorithm. In this work, therefore, a multiple kernel support vector machine (MK-SVM) algorithm is proposed to diagnose simultaneous faults in the distillation column. In the developed MK-SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. Dynamic simulation of a pilot-scale distillation column using Aspen Plus(R) is used for generating data in normal and faulty operation. Eight different fault types are considered, including valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. In the classification of simultaneous faults, a combination of two, three, and four faults is introduced for the performance evaluation of the proposed MK-SVM algorithm. The result showed that the proposed MK-SVM has a high fault detection rate (FDR) of 99.51% and a very low misclassification rate (MR) of 0.49%. The MK-SVM-based classification is better with the F1 score of >97% for all combinations of faults. Moreover, it is observed that the proposed MK-SVM shows better fault diagnosis for single, multiple, and simultaneous faults as compared to other established machine-learning algorithms