Impaired glucose tolerance and diabetes mellitus in a rural population in South India

In the present study the prevalence of impaired glucose tolerance and non-insulin dependent diabetes mellitus in a rural population in South India was assessed and its associations with body mass index and a family history of diabetes mellitus. Data were obtained from inhabitants of two villages located in the North Arcot District of Tamil Nadu. After an overnight fast, 467 randomly selected subjects, aged 40 years or over, were given 75 g glucose orally. After two hours the capillary glucose level was determined. The prevalence of impaired glucose tolerance (2 h value ≥ 7.8 mmol/l and < 11.1 mmol/l) was 6.6% (31 subjects). Non-insulin dependent diabetes mellitus (2 h value ≥ 11.1 mmol/l) was found in 23 subjects (4.9%). Of these, 53% were previously unknown. Age and sex adjusted mean body mass index was significantly higher among subjects with impaired glucose tolerance compared to subjects without glucose intolerance, with a mean difference of 1.4 kg/m2 (95% confidence interval (CI) 0.2, 2.6). A positive family history of diabetes was non-significantly higher in subjects with impaired glucose tolerance. Subjects with non-insulin-dependent diabetes mellitus had a higher mean body mass index compared to subjects with normal glucose levels with a mean difference of 1.9 kg/m2 (95% CI 0.5, 3.3). A positive family history of diabetes was more common among diabetics with a difference of 20% (95% CI 10, 30). Our findings suggest that in a considerable proportion (11.5%) of the rural South Indian population aged 40 years or over glucose intolerance is present. These results may indicate that apart from other important causes of morbidity and mortality, a substantial proportion of the rural Indian population will suffer from cardiovascular morbidity and mortality in the near future

Evaluating a cardiovascular disease risk management care continuum within a learning healthcare system: a prospective cohort study

Background: Many patients now present with multimorbidity and chronicity of disease. This means that multidisciplinary management in a care continuum, integrating primary care and hospital care services, is needed to ensure high quality care. Aim: To evaluate cardiovascular risk management (CVRM) via linkage of health data sources, as an example of a multidisciplinary continuum within a learning healthcare system (LHS). Design & setting: In this prospective cohort study, data were linked from the Utrecht Cardiovascular Cohort (UCC) to the Julius General Practitioners' Network (JGPN) database. UCC offers structured CVRM at referral to the University Medical Centre (UMC) Utrecht. JGPN consists of electronic health record (EHR) data from referring GPs. Method: The cardiovascular risk factors were extracted for each patient 13 months before referral (JGPN), at UCC inclusion, and during 12 months follow-up (JGPN). The following areas were assessed: registration of risk factors; detection of risk factor(s) requiring treatment at UCC; communication of risk factors and actionable suggestions from the specialist to the GP; and change of management during follow-up. Results: In 52% of patients, >1 risk factors were registered (that is, extractable from structured fields within routine care health records) before UCC. In 12%—72% of patients, risk factor(s) existed that required (change or start of) treatment at UCC inclusion. Specialist communication included the complete risk profile in 67% of letters, but lacked actionable suggestions in 86%. In 29% of patients, at least one risk factor was registered after UCC. Change in management in GP records was seen in 21%-58% of them. Conclusion: Evaluation of a multidisciplinary LHS is possible via linkage of health data sources. Efforts have to be made to improve registration in primary care, as well as communication on findings and actionable suggestions for follow-up to bridge the gap in the CVRM continuum

Analyse a posteriori d'une démarche d'observatoire dans un contexte conflictuel : cas de l'irrigation en Charente

La situation de l'irrigation en Poitou-Charentes est emblématique des tensions entre le monde agricole et la société. Pour dépasser ces conflits, un observatoire alliant système d'information et action collective a été mis en place en Charente à l'initiative d'un collectif regroupant responsables politiques, chercheurs et conseillers agricoles. Cet article a pour objectif de restituer l'analyse des différentes perspectives et attentes des acteurs ayant participé à l'élaboration de l'observatoire. L'analyse proposée s'appuie sur une enquête réalisée en 2006 auprès des acteurs concernés. Il en ressort un cadre que nous proposons d'utiliser lors de la conception d'observatoires afin d'expliciter la diversité des attentes des parties prenantes et de faciliter l'élaboration d'un accord préalable à leur mise en ½uvre. / Irrigation in Poitou-Charentes exemplifies the tensions that exist between agriculture and other societal sectors. To overcome these conflicts, a Community Information System (CIS) for the purpose of both data management and community development has been set up in Charente by a group of policymakers, researchers and agricultural advisers. This paper describes our analysis of the differences among participants in the development of this CIS in terms of their points of view and expectations. Drawing on an analysis of a set of interviews conducted with these stakeholders in 2006, we propose a framework for use during the initial design phase of a CIS to make stakeholder expectations explicit and to promote a shared understanding prior to setting up a CIS

Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports

Background and Objective. Electronic health records (EHRs) contain free-text information on symptoms, diagnosis, treatment, and prognosis of diseases. However, this potential goldmine of health information cannot be easily accessed and used unless proper text mining techniques are applied. The aim of this project was to develop and evaluate a text mining pipeline in a multimodal learning architecture to demonstrate the value of medical text classification in chest radiograph reports for cardiovascular risk prediction. We sought to assess the integration of various text representation approaches and clinical structured data with state-of-the-art deep learning methods in the process of medical text mining. Methods. We used EHR data of patients included in the Second Manifestations of ARTerial disease (SMART) study. We propose a deep learning-based multimodal architecture for our text mining pipeline that integrates neural text representation with preprocessed clinical predictors for the prediction of recurrence of major cardiovascular events in cardiovascular patients. Text preprocessing, including cleaning and stemming, was first applied to filter out the unwanted texts from X-ray radiology reports. Thereafter, text representation methods were used to numerically represent unstructured radiology reports with vectors. Subsequently, these text representation methods were added to prediction models to assess their clinical relevance. In this step, we applied logistic regression, support vector machine (SVM), multilayer perceptron neural network, convolutional neural network, long short-term memory (LSTM), and bidirectional LSTM deep neural network (BiLSTM). Results. We performed various experiments to evaluate the added value of the text in the prediction of major cardiovascular events. The two main scenarios were the integration of radiology reports (1) with classical clinical predictors and (2) with only age and sex in the case of unavailable clinical predictors. In total, data of 5603 patients were used with 5-fold cross-validation to train the models. In the first scenario, the multimodal BiLSTM (MI-BiLSTM) model achieved an area under the curve (AUC) of 84.7%, misclassification rate of 14.3%, and F1 score of 83.8%. In this scenario, the SVM model, trained on clinical variables and bag-of-words representation, achieved the lowest misclassification rate of 12.2%. In the case of unavailable clinical predictors, the MI-BiLSTM model trained on radiology reports and demographic (age and sex) variables reached an AUC, F1 score, and misclassification rate of 74.5%, 70.8%, and 20.4%, respectively. Conclusions. Using the case study of routine care chest X-ray radiology reports, we demonstrated the clinical relevance of integrating text features and classical predictors in our text mining pipeline for cardiovascular risk prediction. The MI-BiLSTM model with word embedding representation appeared to have a desirable performance when trained on text data integrated with the clinical variables from the SMART study. Our results mined from chest X-ray reports showed that models using text data in addition to laboratory values outperform those using only known clinical predictors

Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data

BACKGROUND: Direct feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice. OBJECTIVE: This study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center. METHODS: We extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht. We assessed LDL-c target attainment at the time of referral and per year. In patients with multiple measurements, we analyzed LDL-c trajectories, truncated at 6 follow-up measurements. Lastly, we performed a logistic regression analysis to investigate factors associated with improvement of LDL-c at the next measurement. RESULTS: Between February 2003 and December 2017, 250,749 LDL-c measurements were taken from 95,795 patients, of whom 23,932 had a history of CVD. At the time of referral, 51% of patients had not reached their LDL-c target. A large proportion of patients (55%) had no follow-up LDL-c measurements. Most of the patients with repeated measurements showed no change in LDL-c levels over time: the transition probability to remain in the same category was up to 0.84. Sequence clustering analysis showed more women (odds ratio 1.18, 95% CI 1.07-1.10) in the cluster with both most measurements off target and the most LDL-c measurements furthest from the target. Timing of drug prescription was difficult to determine from our data, limiting the interpretation of results regarding medication management. CONCLUSIONS: Routine care data can be used to provide feedback on quality of care, such as LDL-c target attainment. These routine care data show high off-target prevalence and little change in LDL-c over time. Registrations of diagnosis; follow-up trajectory, including primary and secondary care; and medication use need to be improved in order to enhance usability of the EHR system for adequate feedback

Heart disease in the Netherlands: A quantitative update

In this review we discuss cardiovascular mortality, incidence and prevalence of heart disease, and cardiac interventions and surgery in the Netherlands. We combined most recently available data from various Dutch cardiovascular registries, Dutch Hospital Data (LMR), Statistics Netherlands (CBS), and population-based cohort studies, to provide a broad quantitative update. The absolute number of people dying from cardiovascular diseases is declining and cardiovascular conditions are no longer the leading cause of death in the Netherlands. However, a substantial burden of morbidity persists with 400,000 hospitalisations for cardiovascular disease involving over 80,000 cardiac interventions annually. In the Netherlands alone, an estimated 730,000 persons are currently diagnosed with coronary heart disease, 120,000 with heart failure, and 260,000 with atrial fibrillation. These numbers emphasise the continuous need for dedicated research on prevention, diagnosis, and treatment of heart disease in our country

Data mining information from electronic health records produced high yield and accuracy for current smoking status

OBJECTIVES: Researchers are increasingly using routine clinical data for care evaluations and feedback to patients and clinicians. The quality of these evaluations depends on the quality and completeness of the input data. STUDY DESIGN AND SETTING: We assessed the performance of an electronic health record (EHR)-based data mining algorithm, using the example of the smoking status in a cardiovascular population. As a reference standard, we used the questionnaire from the Utrecht Cardiovascular Cohort (UCC). To assess diagnostic accuracy, we calculated sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). RESULTS: We analyzed 1,661 patients included in the UCC to January 18, 2019. Of those, 14% (n = 238) had missing information on smoking status in the UCC questionnaire. Data mining provided information on smoking status in 99% of the 1,661 participants. Diagnostic accuracy for current smoking was sensitivity 88%, specificity 92%, NPV 98%, and PPV 63%. From false positives, 85% reported they had quit smoking at the time of the UCC. CONCLUSION: Data mining showed great potential in retrieving information on smoking (a near complete yield). Its diagnostic performance is good for negative smoking statuses. The implications of misclassification with data mining are dependent on the application of the data

An international randomised placebo-controlled trial of a four-component combination pill ("polypill") in people with raised cardiovascular risk.

BACKGROUND:There has been widespread interest in the potential of combination cardiovascular medications containing aspirin and agents to lower blood pressure and cholesterol ('polypills') to reduce cardiovascular disease. However, no reliable placebo-controlled data are available on both efficacy and tolerability. METHODS:We conducted a randomised, double-blind placebo-controlled trial of a polypill (containing aspirin 75 mg, lisinopril 10 mg, hydrochlorothiazide 12.5 mg and simvastatin 20 mg) in 378 individuals without an indication for any component of the polypill, but who had an estimated 5-year cardiovascular disease risk over 7.5%. The primary outcomes were systolic blood pressure (SBP), LDL-cholesterol and tolerability (proportion discontinued randomised therapy) at 12 weeks follow-up. FINDINGS:At baseline, mean BP was 134/81 mmHg and mean LDL-cholesterol was 3.7 mmol/L. Over 12 weeks, polypill treatment reduced SBP by 9.9 (95% CI: 7.7 to 12.1) mmHg and LDL-cholesterol by 0.8 (95% CI 0.6 to 0.9) mmol/L. The discontinuation rates in the polypill group compared to placebo were 23% vs 18% (RR 1.33, 95% CI 0.89 to 2.00, p = 0.2). There was an excess of side effects known to the component medicines (58% vs 42%, p = 0.001), which was mostly apparent within a few weeks, and usually did not warrant cessation of trial treatment. CONCLUSIONS:This polypill achieved sizeable reductions in SBP and LDL-cholesterol but caused side effects in about 1 in 6 people. The halving in predicted cardiovascular risk is moderately lower than previous estimates and the side effect rate is moderately higher. Nonetheless, substantial net benefits would be expected among patients at high risk. TRIAL REGISTRATION:Australian New Zealand Clinical Trials Registry ACTRN12607000099426