Changes in the prevalence, treatment and control of hypertension in Germany? : a clinical-epidemiological study of 50.000 primary care patients

INTRODUCTION: Medical societies have developed guidelines for the detection, treatment and control of hypertension (HTN). Our analysis assessed the extent to which such guidelines were implemented in Germany in 2003 and 2001. METHODS: Using standardized clinical diagnostic and treatment appraisal forms, blood pressure levels and patient questionnaires for 55,518 participants from the cross-sectional Targets and Essential Data for Commitment of Treatment (DETECT) study (2003) were analyzed. Physician's diagnosis of hypertension (HTN(doc)) was defined as coding hypertension in the clinical appraisal questionnaire. Alternative definitions used were physician's diagnosis or the patient's self-reported diagnosis of hypertension (HTN(doc,pat)), physician's or patient's self-reported diagnosis or a BP measurement with a systolic BP≥140 mmHg and/or a diastolic BP≥90 (HTN(doc,pat,bp)) and diagnosis according to the National Health and Nutrition Examination Survey (HTN(NHANES)). The results were compared with the similar German HYDRA study to examine whether changes had occurred in diagnosis, treatment and adequate blood pressure control (BP below 140/90 mmHg) since 2001. Factors associated with pharmacotherapy and control were determined. RESULTS: The overall prevalence rate for hypertension was 35.5% according to HTN(doc) and 56.0% according to NHANES criteria. Among those defined by NHANES criteria, treatment and control rates were 56.0% and 20.3% in 2003, and these rates had improved from 55.3% and 18.0% in 2001. Significant predictors of receiving antihypertensive medication were: increasing age, female sex, obesity, previous myocardial infarction and the prevalence of comorbid conditions such as coronary heart disease (CHD), hyperlipidemia and diabetes mellitus (DM). Significant positive predictors of adequate blood pressure control were CHD and antihypertensive medication. Inadequate control was associated with increasing age, male sex and obesity. CONCLUSIONS: Rates of treated and controlled hypertension according to NHANES criteria in DETECT remained low between 2001 and 2003, although there was some minor improvement

Antisense-mediated exon skipping: a therapeutic strategy for titin-based dilated cardiomyopathy

Frameshift mutations in the TTN gene encoding titin are a major cause for inherited forms of dilated cardiomyopathy (DCM), a heart disease characterized by ventricular dilatation, systolic dysfunction, and progressive heart failure. To date, there are no specific treatment options for DCM patients but heart transplantation. Here, we show the beneficial potential of reframing titin transcripts by antisense oligonucleotide (AON)-mediated exon skipping in human and murine models of DCM carrying a previously identified autosomal-dominant frameshift mutation in titin exon 326. Correction of TTN reading frame in patient-specific cardiomyocytes derived from induced pluripotent stem cells rescued defective myofibril assembly and stability and normalized the sarcomeric protein expression. AON treatment in Ttn knock-in mice improved sarcomere formation and contractile performance in homozygous embryos and prevented the development of the DCM phenotype in heterozygous animals. These results demonstrate that disruption of the titin reading frame due to a truncating DCM mutation canbe restored by exon skipping in both patient cardiomyocytes invitro and mouse heart invivo, indicating RNA-based strategies as a potential treatment option for DCM

Determinants of a GP visit and cervical cancer screening examination in Great Britain.

Objective: In the UK, women are requested to attend a cervical cancer test every 3 years as part of the NHS Cervical Screening Programme. This analysis compares the determinants of a cervical cancer screening examination with the determinants of a GP visit in the same year and investigates if cervical cancer screening participation is more likely for women who visit their GP. Methods: A recursive probit model was used to analyse the determinants of GP visits and cervical cancer screening examinations. GP visits were considered to be endogenous in the cervical cancer screening examination. The analysed sample consisted of 52,551 observations from 8,386 women of the British Household Panel Survey. Results: The analysis showed that a higher education level and a worsening self-perceived health status increased the probability of a GP visit, whereas smoking decreased the probability of a GP visit. GP visits enhanced the uptake of a cervical cancer screening examination in the same period. The only variables which had the same positive effect on both dependent variables were higher education and living with a partner. The probability of a cervical cancer screening examination increased also with previous cervical cancer screening examinations and being in the recommended age groups. All other variables had different results for the uptake of a GP visit or a cervical cancer screening examination. Conclusions: Most of the determinants of visiting a GP and cervical cancer screening examination differ from each other and a GP visit enhances the uptake of a smear test

Proceedings of Patient Reported Outcome Measure’s (PROMs) Conference Oxford 2017: Advances in Patient Reported Outcomes Research

A33-Effects of Out-of-Pocket (OOP) Payments and Financial Distress on Quality of Life (QoL) of People with Parkinson’s (PwP) and their Carer

Issues possibly associated with misinterpreting survival data: A method study

The aftermarket holds a vital role in the Volvo Group value offer. Producing profitability by satisfying the customers needs for important spare parts, ensuring maximum uptime for the entire range of vehicles produced and sold. As the cost for keeping stock exponentially increases with a higher availability, the availability can never be 100%. This in effect means that there will be occasions where an order is placed on a part that is currently not in stock, creating a back order. And while not all of these back orders can be avoided completely, predicting them before they occur will allow for preemptive measures to be taken, potentially reducing lead times and costs. Deep learning is a sub-section of machine learning, the study of methods to make computers find complex patterns in data. Deep learning has had an increase in popularity as the computational power and available data has greatly increased in recent years and is something that Volvo sees potential in. This creates the aim of this study which is to develop a deep learning model to predict the occurrence of back orders. In order to fulfill this aim, two main research questions were formed. The first research question intends to find underlying causes and factors that can explain the occurrence of back orders, in order to create the input features that the model can be trained on. This was initiated with a basis in literature, where a theoretical framework was created from different areas in the field of logistics as well as previous studies that combine logistics and machine learning. After this an empirical study was conducted where four previous initiatives from Volvo were found, that aim to explain the occurrence of back orders. As this was concluded, the findings were combined and synthesized into a list of factors that explain the underlying causes of back orders. In the second research question the factors listed were translated into input features of the model, where all quantifiable factors that could be and located in the Volvo database were included. This created the data set used to train the deep learning model to predict back orders. After the feature creation was completed, the actual design and development of the model could commence. Based on literature concerning deep learning along with directives from Volvo, a deep recurrent neural network was developed. The exact size and shape of the model was varied and evaluated to find the best performance. Evaluating the results showed several interesting findings. After training the model on one year of weekly data for 20 000 part numbers, the model proved to be skillful in predicting the occurrence of back orders. The model was able to predict 73% of back orders one week before they occurred (recall), and 72% of what the model deemed to be back orders were actual back orders (precision). The main challenges with predicting back orders were the imbalance between back order and a non-back order and the limit of one year of data. As the nature of back orders is that on average, only a few weeks per year will there be a back order on a given part, the training of the model becomes difficult. The difficulty with this imbalance is that the model is always less likely to predict a back order if the occurrence of back order itself is rare. The advantage of deep learning can be found with a large amount of data, and not being limited to one year of data is likely to produce better results. Despite these difficulties the model was highly successful in predicting the occurrence of back orders

Uptake rate for a GP visit and cervical cancer screening examination during the 17 years period from 1992 to 2008 in Great Britain.

<p>Uptake rate for a GP visit and cervical cancer screening examination during the 17 years period from 1992 to 2008 in Great Britain.</p

Sample characteristics for the balanced sample of women from 1992 to 2008 in Great Britain.

<p>Sample characteristics for the balanced sample of women from 1992 to 2008 in Great Britain.</p

Univariate probit and recursive probit estimates of cervical cancer screening and GP visits in Great Britain.

<p>Univariate probit and recursive probit estimates of cervical cancer screening and GP visits in Great Britain.</p

Determinants of adequate blood pressure control among physician diagnosed hypertension cases.

<p>N = 20,164, %w = weighted percentages; OR = Odds Ratio estimated by logistic regression; 95% CI = 95% confidence interval.</p>#<p>unadjusted OR;</p>†<p>adjusted OR from multivariate analyses;</p>*<p>significant on 5% level.</p>‡<p>OR for increase of 1 year.</p