Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms

Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems - IEEE CBMS 201

Association between clinical presentations before myocardial infarction and coronary mortality: a prospective population-based study using linked electronic records.

BACKGROUND: Ischaemia in different arterial territories before acute myocardial infarction (AMI) may influence post-AMI outcomes. No studies have evaluated prospectively collected information on ischaemia and its effect on short- and long-term coronary mortality. The objective of this study was to compare patients with and without prospectively measured ischaemic presentations before AMI in terms of infarct characteristics and coronary mortality. METHODS AND RESULTS: As part of the CALIBER programme, we linked data from primary care, hospital admissions, the national acute coronary syndrome registry and cause-specific mortality to identify patients with first AMI (n = 16,439). We analysed time from AMI to coronary mortality (n = 5283 deaths) using Cox regression (median 2.6 years follow-up), comparing patients with and without recent ischaemic presentations. Patients with ischaemic presentations in the 90 days before AMI experienced lower coronary mortality in the first 7 days after AMI compared with those with no prior ischaemic presentations, after adjusting for age, sex, smoking, diabetes, blood pressure and cardiovascular medications [HR: 0.64 (95% CI: 0.57-0.73) P < 0.001], but subsequent mortality was higher [HR: 1.42 (1.13-1.77) P = 0.001]. Patients with ischaemic presentations closer in time to AMI had the lowest seven day mortality (P-trend = 0.001). CONCLUSION: In the first large prospective study of ischaemic presentations prior to AMI, we have shown that those occurring closest to AMI are associated with lower short-term coronary mortality following AMI, which could represent a natural ischaemic preconditioning effect, observed in a clinical setting. CLINICAL TRIALS REGISTRATION: Clinicaltrials.gov identifier NCT01604486

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms

Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.Comment: Accepted American Medical Informatics Association Annual Symposium 201

Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001-2010) with complete data on all covariates. Variables were artificially made "missing at random," and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data

Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data

Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their highly dimensional and heterogeneous nature. Low-dimensional representations of EHR data can potentially mitigate these challenges. In this paper, we use global vectors (GloVe) to learn word embeddings for diagnoses and procedures recorded using 13 million ontology terms across 2.7 million hospitalisations in national UK EHR. We demonstrate the utility of these embeddings by evaluating their performance in identifying patients which are at higher risk of being hospitalised for congestive heart failure. Our findings indicate that embeddings can enable the creation of robust EHR-derived disease risk prediction models and address some the limitations associated with manual clinical feature engineering.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.0721

A 10-year prognostic model for patients with suspected angina attending a chest pain clinic.

BACKGROUND AND OBJECTIVE: Diagnostic models used in the management of suspected angina provide no explicit information about prognosis. We present a new prognostic model of 10-year coronary mortality in patients presenting for the first time with suspected angina to complement the Diamond-Forrester diagnostic model of disease probability. METHODS AND RESULTS: A multicentre cohort of 8762 patients with suspected angina was followed up for a median of 10 years during which 233 coronary deaths were observed. Developmental (n=4412) and validation (n=4350) prognostic models based on clinical data available at first presentation showed good performance with close agreement and the final model utilised all 8762 patients to maximise power. The prognostic model showed strong associations with coronary mortality for age, sex, chest pain typicality, smoking status, diabetes, pulse rate, and ECG findings. Model discrimination was good (C statistic 0.83), patients in the highest risk quarter accounting for 173 coronary deaths (10-year risk of death: 8.7%) compared with a total of 60 deaths in the three lower risk quarters. When the model was simplified to incorporate only Diamond-Forrester factors (age, sex and character of symptoms) it underestimated coronary mortality risk, particularly in patients with reversible risk factors. CONCLUSIONS: For the first time in patients with suspected angina, a prognostic model is presented based on simple clinical factors available at the initial cardiological assessment. The model discriminated powerfully between patients at high risk and lower risk of coronary death during 10-year follow-up. Clinical utility was reflected in the prognostic value it added to the updated Diamond-Forrester diagnostic model of disease probability

The impact of the coronary collateral circulation on mortality: a meta-analysis

Aims The coronary collateral circulation as an alternative source of blood supply has shown benefits regarding several clinical endpoints in patients with myocardial infarction (MI) such as infarct size and left ventricular remodelling. However, its impact on hard endpoints such as mortality and its impact in patients with stable coronary artery disease (CAD) is more controversial. The purpose of this systematic review and meta-analysis was to explore the impact of collateral circulation on all-cause mortality. Methods and results We searched MEDLINE, EMBASE, ISI Web of Science (2001 to 25 April 2011), and conference proceedings for studies evaluating the effect of coronary collaterals on mortality. Random-effect models were used to calculate summary risk ratios (RR). A total of 12 studies enrolling 6529 participants were included in this analysis. Patients with high collateralization showed a reduced mortality compared with those with low collateralization [RR 0.64 (95% confidence interval 0.45-0.91); P= 0.012]. The RR for ‘high collateralization' in patients with stable CAD was 0.59 [0.39-0.89], P= 0.012, in patients with subacute MI it was 0.53 [0.15-1.92]; P= 0.335, and for patients with acute MI it was 0.63 [0.29-1.39]; P= 0.257. Conclusions In patients with CAD, the coronary collateralization has a relevant protective effect. Patients with a high collateralization have a 36% reduced mortality risk compared with patients with low collateralizatio

A national initiative in data science for health: an evaluation of the UK Farr Institute

ObjectiveTo evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.&#x0D; Design and Outcome measuresWe evaluated evidence of six domains characterising a new field of science:&#x0D; &#x0D; defining central scientific challenges,&#x0D; demonstrating how the central challenges might be solved,&#x0D; creating novel interactions among groups of scientists,&#x0D; training new types of experts,&#x0D; re-organising universities,&#x0D; demonstrating impacts in society.&#x0D; &#x0D; We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.&#x0D; SettingFour UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.&#x0D; Results1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (&gt;70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training &gt;400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: &gt; 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.&#x0D; ConclusionThe Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.</jats:p

COVID-19 Mortality Risk in Down Syndrome: Results From a Cohort Study Of 8 Million Adults

Background: At the start of the coronavirus disease 2019 (COVID-19) pandemic, many national health organizations emphasized nonpharmacologic interventions, such as quarantining or physical distancing. In the United Kingdom, strict self-isolation (“shielding”) was advised for those deemed to be clinically extremely vulnerable on the basis of the presence of selected medical conditions or at the discretion of their general practitioners. Down syndrome features on neither the U.K. shielding list nor the U.S. Centers for Disease Control and Prevention list of groups at “increased risk.” However, it is associated with immune dysfunction, congenital heart disease, and pulmonary pathology and, given its prevalence, may be a relevant albeit unconfirmed risk factor for severe COVID-1