Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015).

Objectives: To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015.

Design: Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases.

Nonmedical use of d-Amphetamines and Methylphenidate in Medical Students.

Objective: The purpose of this study was to determine the prevalence of medical and nonmedical use of prescription attention deficit hyperactive disorder (ADHD) stimulant medication among medical students.

Materials And Methods: An IRB approved 19-question web survey was sent out to all students from a Puerto Rico (PR) medical school to assess use of ADHD medication. Out of the 250 stu-dents consulted there was a response of 152 surveys.

The relationship between sleep duration, cognition and dementia: a Mendelian randomization study.

Background: Short and long sleep duration have been linked with poorer cognitive outcomes, but it remains unclear whether these associations are causal.

Methods: We conducted the first Mendelian randomization (MR) study with 77 single-nucleotide polymorphisms (SNPs) for sleep duration using individual-participant data from the UK Biobank cohort (N = 395 803) and summary statistics from the International Genomics of Alzheimer's Project (N cases/controls = 17 008/37 154) to investigate the potential impact of sleep duration on cognitive outcomes.

Results: Linear MR suggested that each additional hour/day of sleep was associated with 1% [95% confidence interval (CI) = 0-2%; P = 0.

Insights into Pharmacotherapy Management for Parkinson's Disease Patients Using Wearables Activity Data.

Unlabelled: We investigate what supervised classification models using clinical and wearables data are best suited to address two important questions about the management of Parkinson's Disease (PD) patients: 1) does a PD patient require pharmacotherapy or not, and 2) whether therapies are having an effect. Currently, patient management is suboptimal due to using subjective patient reported episodes to answer these questions.

Methodology: Clinical and real environment sensor data (memory, tapping, walking) was provided by the mPower study (6805 participants).

Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting.

Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.

Relative clinical and economic impact of exercise echocardiography vs. exercise electrocardiography, as first line investigation in patients without known coronary artery disease and new stable angina: a randomized prospective study.

Aims: Exercise electrocardiography (ExECG) is widely used in suspected stable angina (SA) as the initial test for the evaluation of coronary artery disease (CAD). We hypothesized that exercise stress echo (ESE) would be efficacious with cost advantage over ExECG when utilized as the initial test.

Methods And Results: Consecutive patients with suspected SA, without known CAD were randomized into ExECG or ESE.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place.

Relative diagnostic, prognostic and economic value of stress echocardiography versus exercise electrocardiography as initial investigation for the detection of coronary artery disease in patients with new onset suspected angina.

Objectives: We hypothesised that stress echocardiography (SE), may be superior to exercise ECG (ExECG), for predicting CAD and outcome, and cost-beneficial, when performed as initial investigation in newly suspected angina.

Methods: All patients seen in 2011, with suspected angina, no history of CAD, pre-test likelihood of CAD of > 10% and who underwent SE or ExECG as first line were identified retrospectively. Cost to diagnosis was calculated by adding the cost of all tests, up to and including coronary angiography (CA), on an intention-to-treat basis.

Using computerised decision support to improve compliance of cancer multidisciplinary meetings with evidence-based guidance.

Objectives: The cancer multidisciplinary team (MDT) meeting (MDM) is regarded as the best platform to reduce unwarranted variation in cancer care through evidence-compliant management. However, MDMs are often overburdened with many different agendas and hence struggle to achieve their full potential. The authors developed an interactive clinical decision support system called MATE (Multidisciplinary meeting Assistant and Treatment sElector) to facilitate explicit evidence-based decision making in the breast MDMs.

Cancer multidisciplinary team meetings: evidence, challenges, and the role of clinical decision support technology.

Multidisciplinary team (MDT) model in cancer care was introduced and endorsed to ensure that care delivery is consistent with the best available evidence. Over the last few years, regular MDT meetings have become a standard practice in oncology and gained the status of the key decision-making forum for patient management. Despite the fact that cancer MDT meetings are well accepted by clinicians, concerns are raised over the paucity of good-quality evidence on their overall impact.

MRS quality assessment in a multicentre study on MRS-based classification of brain tumours.

This paper reports on quality assessment of MRS in the European Union-funded multicentre project INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance; http://azizu.uab.es/INTERPRET), which has developed brain tumour classification software using in vivo proton MR spectra.

Enhancing parameter precision and the minimal modeling approach in type I diabetes.

An evaluation of a simple model including external perturbations was evaluated for its usefulness in predicting diabetic patients' behavior. The model proposed has been derived from Cobelli and Marl's comprehensive model and is structurally identifiable. The optimization was carried out on data gathered using CGMS (Medtronic MiniMed) on 3 subjects.

Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database.

Object: The aim of this study was to estimate the accuracy of routine magnetic resonance (MR) imaging studies in the classification of brain tumors in terms of both cell type and grade of malignancy.

Methods: The authors retrospectively assessed the correlation between neuroimaging classifications and histopathological diagnoses by using multicenter database records from 393 patients with brain tumors. An ontology was devised to establish diagnostic agreement.

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case.

A multi-centre, web-accessible and quality control-checked database of in vivo MR spectra of brain tumour patients.

Objective: To describe an Internet-accessible database that contains validated in vivo MR spectra and clinical data of brain tumour patients.

Materials And Methods: All data from patients entering the INTERPRET project (International Network for Pattern Recognition of Tumours Using Magnetic Resonance, View Article and Find Full Text PDF