Iain James Marshall

Lay perspectives on hypertension and drug adherence: systematic review of qualitative research

Objective To synthesise the findings from individual qualitative studies on patients’ understanding and experiences of hypertension and drug taking; to investigate whether views differ internationally by culture or ethnic group and whether the research could inform interventions to improve adherence. Design Systematic review and narrative synthesis of qualitative research using the 2006 UK Economic and Social Research Council research methods programme guidance. Data sources Medline, Embase, the British Nursing Index, Social Policy and Practice, and PsycInfo from inception to October 2011. Study selection Qualitative interviews or focus groups among people with uncomplicated hypertension (studies principally in people with diabetes, established cardiovascular disease, or pregnancy related hypertension were excluded). Results 59 papers reporting on 53 qualitative studies were included in the synthesis. These studies came from 16 countries (United States, United Kingdom, Brazil, Sweden, Canada, New Zealand, Denmark, Finland, Ghana, Iran, Israel, Netherlands, South Korea, Spain, Tanzania, and Thailand). A large proportion of participants thought hypertension was principally caused by stress and produced symptoms, particularly headache, dizziness, and sweating. Participants widely intentionally reduced or stopped treatment without consulting their doctor. Participants commonly perceived that their blood pressure improved when symptoms abated or when they were not stressed, and that treatment was not needed at these times. Participants disliked treatment and its side effects and feared addiction. These findings were consistent across countries and ethnic groups. Participants also reported various external factors that prevented adherence, including being unable to find time to take the drugs or to see the doctor; having insufficient money to pay for treatment; the cost of appointments and healthy food; a lack of health insurance; and forgetfulness. Conclusions Non-adherence to hypertension treatment often resulted from patients’ understanding of the causes and effects of hypertension; particularly relying on the presence of stress or symptoms to determine if blood pressure was raised. These beliefs were remarkably similar across ethnic and geographical groups; calls for culturally specific education for individual ethnic groups may therefore not be justified. To improve adherence, clinicians and educational interventions must better understand and engage with patients’ ideas about causality, experiences of symptoms, and concerns about drug side effects.

The effects of socioeconomic status on stroke risk and outcomes

The latest evidence on socioeconomic status and stroke shows that stroke not only disproportionately affects low-income and middle-income countries, but also socioeconomically deprived populations within high-income countries. These disparities are reflected not only in risk of stroke but also in short-term and long-term outcomes after stroke. Increased average levels of conventional risk factors (eg, hypertension, hyperlipidaemia, excessive alcohol intake, smoking, obesity, and sedentary lifestyle) in populations with low socioeconomic status account for about half of these effects. In many countries, evidence shows that people with lower socioeconomic status are less likely to receive good-quality acute hospital and rehabilitation care than people with higher socioeconomic status. For clinical practice, better implementation of well established treatments, effective management of risk factors, and equity of access to high-quality acute stroke care and rehabilitation will probably reduce inequality substantially. Overcoming barriers and adapting evidence-based interventions to different countries and health-care settings remains a research priority.

Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

Abstract Objectives Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. Methods We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Results Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%–99% recall) with substantially less effort (we observed a reduction of around 60%–80%) than relying on manual screening alone. Conclusions Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks.

Trends in Risk Factor Prevalence and Management Before First Stroke Data From the South London Stroke Register 1995–2011

Background and Purpose— Vascular risk factors are suboptimally managed internationally. This study investigated time trends in risk factors diagnosed before stroke and their treatment, and factors associated with appropriate medication use. Methods— A total of 4416 patients with a first stroke were registered in the population-based South London Stroke Register from 1995 to 2011. Previously diagnosed risk factors and usual medications were collected from patients’ primary care and hospital records. Trends and associations were assessed using multivariate logistic regression. Results— Seventy-two percent of patients were diagnosed previously with 1 or more risk factors; 30% had diagnosed risk factors that were untreated. Hypercholesterolemia increased significantly during the study period; myocardial infarction and transient ischemic attack prevalences decreased. Antiplatelet prescription increased in atrial fibrillation (AF), myocardial infarction, and transient ischemic attack (AF, 37%–51%, P<0.001; myocardial infarction, 48%–69%, P<0.001; transient ischemic attack, 49%–61%, P=0.015). Anticoagulant prescription for AF showed a nonsignificant increase (12%–23%; P=0.059). Fewer older patients with AF were prescribed anticoagulants (age, >85 versus <65 years; adjusted relative risk, 0.19; 95% confidence interval, 0.08–0.41). Black ethnicity (adjusted relative risk, 1.17; 95% confidence interval, 1.10–1.23) and female sex (adjusted relative risk, 1.09; 95% confidence interval, 1.03–1.15) were associated with increased antihypertensive drug prescription; other medications did not vary by ethnicity or sex. Conclusions— Antiplatelet and cholesterol-lowering treatment prescribing have improved significantly over time; however, only a minority with AF received anticoagulants, and this did not improve significantly. Overall, 30% of strokes occurred in patients with previously diagnosed but untreated risk factors.

Rationale-Augmented Convolutional Neural Networks for Text Classification.

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.

Spá: a web-based viewer for text mining in evidence based medicine

Summarizing the evidence about medical interventions is an immense undertaking, in part because unstructured Portable Document Format (PDF) documents remain the main vehicle for disseminating scientific findings. Clinicians and researchers must therefore manually extract and synthesise information from these PDFs. We introduce Spa1,2 a web-based viewer that enables automated annotation and summarisation of PDFs via machine learning. To illustrate its functionality, we use Spa to semi-automate the assessment of bias in clinical trials. Spa has a modular architecture, therefore the tool may be widely useful in other domains with a PDF-based literature, including law, physics, and biology.

Machine Learning for Identifying Randomized Controlled Trials: an evaluation and practitioner’s guide

Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this work, we evaluate ML models for RCT classification (support vector machines, convolutional neural networks, and ensemble approaches). We trained and optimized support vector machine and convolutional neural network models on the titles and abstracts of the Cochrane Crowd RCT set. We evaluated the models on an external dataset (Clinical Hedges), allowing direct comparison with traditional database search filters. We estimated area under receiver operating characteristics (AUROC) using the Clinical Hedges dataset. We demonstrate that ML approaches better discriminate between RCTs and non‐RCTs than widely used traditional database search filters at all sensitivity levels; our best‐performing model also achieved the best results to date for ML in this task (AUROC 0.987, 95% CI, 0.984‐0.989). We provide practical guidance on the role of ML in (1) systematic reviews (high‐sensitivity strategies) and (2) rapid reviews and clinical question answering (high‐precision strategies) together with recommended probability cutoffs for each use case. Finally, we provide open‐source software to enable these approaches to be used in practice.

Automating Biomedical Evidence Synthesis: RobotReviewer

We present RobotReviewer, an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information. Our goal is for RobotReviewer to automatically extract and synthesise the full-range of structured data needed to inform evidence-based practice.

Trends in the prevalence and management of pre-stroke atrial fibrillation, the South London Stroke Register, 1995-2014

Background Previous studies have found low use of anticoagulation prior to stroke, in people with atrial fibrillation (AF). This study examined data on patients with AF-related stroke from a population-based stroke register, and sought to examine changes in management of AF prior to stroke, and reasons for suboptimal treatment, in those who were known to be at a high risk of stroke. Methods The South London Stroke Register (SLSR) is an ongoing population-based register recording first-in-a-lifetime stroke. Trends in the prevalence of AF, and antithrombotic medication prescribed before the stroke, were investigated from 1995 to 2014. Multivariable logistic regression analyses were conducted to assess the factors associated with appropriate management. Results Of the 5041 patients on the register, 816 (16.2%) were diagnosed with AF before their stroke. AF related stroke increased substantially among Black Carribean and Black African patients, comprising 5% of the overall cohort in 1995–1998, increasing to 25% by 2011–2014 (p<0.001). Anticoagulant prescription in AF patients at high-risk of stroke (CHADS2 score [> = 2]) increased from 9% (1995–1998) to 30% (2011–2014) (p<0.001). Antiplatelet prescription was more commonly prescribed throughout all time periods (43% to 64% of high-risk patients.) Elderly patients (>65) were significantly less likely to be prescribed an anticoagulant, with ethnicity, gender and deprivation showing no association with anticoagulation. Conclusions Most AF-related strokes occurred in people who could have been predicted to be at high risk before their stroke, yet were not prescribed optimal preventative treatment. The elderly,despite being at highest stroke risk, were rarely prescribed anticoagulants.

Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement

Background Stroke, like many long-term conditions, tends to be managed in isolation of its associated risk factors and multimorbidity. With increasing access to clinical and research data there is the potential to combine data from a variety of sources to inform interventions to improve healthcare. A ‘Learning Health System’ (LHS) is an innovative model of care which transforms integrated data into knowledge to improve healthcare. The objective of this study is to develop a process of engaging stakeholders in the use of clinical and research data to co-produce potential solutions, informed by a LHS, to improve long-term care for stroke survivors with multimorbidity. Methods We used a stakeholder engagement study design informed by co-production principles to engage stakeholders, including service users, carers, general practitioners and other health and social care professionals, service managers, commissioners of services, policy makers, third sector representatives and researchers. Over a 10 month period we used a range of methods including stakeholder group meetings, focus groups, nominal group techniques (priority setting and consensus building) and interviews. Qualitative data were recorded, transcribed and analysed thematically. Results 37 participants took part in the study. The concept of how data might drive intervention development was difficult to convey and understand. The engagement process led to four priority areas for needs for data and information being identified by stakeholders: 1) improving continuity of care; 2) improving management of mental health consequences; 3) better access to health and social care; and 4) targeting multiple risk factors. These priorities informed preliminary design interventions. The final choice of intervention was agreed by consensus, informed by consideration of the gap in evidence and local service provision, and availability of robust data. This shaped a co-produced decision support tool to improve secondary prevention after stroke for further development. Conclusions Stakeholder engagement to identify data-driven solutions is feasible but requires resources. While a number of potential interventions were identified, the final choice rested not just on stakeholder priorities but also on data availability. Further work is required to evaluate the impact and implementation of data-driven interventions for long-term stroke survivors.