Ontological representation, classification and data-driven computing of phenotypes

Background The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term ‘phenotype’ has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case ‘phenotype pipeline’ (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

Current status of opioid epidemic in the United Kingdom and strategies for treatment optimisation in chronic pain

The increase in opioid prescriptions in the United States has been accompanied by an increase in misuse as well as overdose and toxicity related morbidity and mortality. However, the extent of the increased opioid use, including misuse in the United Kingdom, currently remains less debated. Recent studies in the United Kingdom have shown a rise in opioid use and attributed deaths, particularly in areas with higher deprivation. There are also large variations amongst the devolved nations; Scotland has the highest drug-related deaths and year-on-year increase within Europe. Better clinical guidelines that can enable person-centred management of chronic pain, medicines optimisation, and early diagnosis and treatment of opioid use disorder are crucial to addressing opioid-related morbidity and mortality in the United Kingdom.

Axitinib plus immune checkpoint inhibitor: evidence- and expert-based consensus recommendation for treatment optimisation and management of related adverse events

With the recent approval of the combinations of axitinib with the immune checkpoint inhibitor (ICI) pembrolizumab or avelumab for first-line treatment of advanced renal cell carcinoma, guidance on how to distinguish between immune-related adverse events (AEs) caused by ICI versus axitinib-related AEs is necessary to optimise therapy with axitinib–ICI combinations. The recommendations here are based on (1) systematic review of published evidence, (2) discussion among experts in the field and (3) a survey to obtain expert consensus on specific measures for therapy management with the combinations axitinib/avelumab and axitinib/pembrolizumab. The experts identified areas of AEs requiring unique management during treatment with axitinib–ICI combinations that were not covered by current recommendations. Diarrhoea, hepatic toxicity, fatigue and cardiovascular AEs were found to be applicable to such specialised management. Triage between immune-suppressive and supportive measures is a key component in therapy management. Clinical monitoring and experience with both classes of agents are necessary to manage this novel therapeutic approach. We focused on AEs with an overlap between axitinib and ICI therapy. Our recommendations address AE management of axitinib–ICI combinations with the aim to improve the safety of these therapies.

Protocol for a mixed methods process evaluation of the Smoking Treatment Optimisation in Pharmacies (STOP) trial

IntroductionAssessing the fidelity of complex behavioural interventions and examining the contextual reasons why such interventions succeed, or fail are important activities but challenging and rarely reported. The Smoking Treatment Optimisation in Pharmacies (STOP) trial is a cluster randomised trial evaluating the effectiveness of a complex intervention to optimise the National Health Service (NHS) Stop Smoking Service delivered in community pharmacies. This complex intervention comprises a training package for pharmacy staff involving motivational interviewing and communication skills aimed at increasing smoking cessation knowledge and proactive client engagement. We report on a process evaluation which was planned alongside the trial to offer findings that will assist in the interpretation of the main trial results and help inform potential implementation in community pharmacy settings on a wider scale.Methods and analysisQuantitative data on recruitment and retention process of pharmacies, pharmacy staff and service users has been collected during the trial along with data on dose and fidelity of the intervention delivery from participating intervention arm pharmacies to identify potential implementation issues. Simulated client data on behaviour change skills and display of intervention materials from both control and intervention pharmacies is being assessed. These data will be combined with qualitative data; including adviser-smoker consultation recordings that provide a snapshot of behaviour skills delivery by stop smoking advisers and semi-structured interviews with pharmacy staff and services users from the intervention arm.DiscussionPublished protocols for process evaluations of complex health interventions are still rare despite increasing funding for this work to facilitate understanding of trial outcomes from an implementation perspective. This mixed methods protocol will contribute to the developing literature around the conduct of process evaluation and the value they add to health services research.Trial registration numberISRCTN16351033.Strengths and limitations of this studyA planned mixed methods process evaluation that draws together data from different sources to help explain the trial results and establish the feasibility of scaling this complex intervention up in community pharmacy settings.A strength is the use of a previously tested mystery shopping method to assess fidelity of skills performance at the pharmacy counterThe process evaluation relies on willing pharmacy staff and service users involved in the trial to collect some of the data, which may introduce bias.This paper also provides a detailed example of how to use the MRC framework for process evaluation of complex interventions to design an extensive process evaluation within trial settings.