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The Centre for Evidence-Based Medicine (CEBM) at Oxford University develops, promotes and disseminates better evidence for health care.
Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.
The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing's work, especially his paper on "the imitation game", in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.
Pharmacological and electronic cigarette interventions for smoking cessation in adults: component network meta-analyses
Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To conduct component network meta-analyses (cNMAs) to investigate the comparative effectiveness, safety and tolerability of different smoking cessation pharmacotherapies and electronic cigarettes (EC), singly and combined, when helping people to stop smoking tobacco. To investigate:. how the different characteristics of smoking cessation pharmacotherapies and EC interventions (e.g. intervention subtype, dose, length of intervention, whether the intervention is used prequit as well as from quit date or from quit date only) influence efficacy, safety and tolerability; whether identifiable participant characteristics and behavioural support suggest different optimal intervention strategies.
Poor quality medical research causes serious harms by misleading healthcare professionals and policymakers, decreasing trust in science and medicine, and wasting public funds. Here we outline underlying problems including insufficient transparency, dysfunctional incentives, and reporting biases. We make the following recommendations to address these problems: Journals and funders should ensure authors fulfil their obligation to share detailed study protocols, analytical code, and (as far as possible) research data. Funders and journals should incentivise uptake of registered reports and establish funding pathways which integrate evaluation of funding proposals with initial peer review of registered reports. A mandatory national register of interests for all those who are involved in medical research in the UK should be established, with an expectation that individuals maintain the accuracy of their declarations and regularly update them. Funders and institutions should stop using metrics such as citations and journal's impact factor to assess research and researchers and instead evaluate based on quality, reproducibility, and societal value. Employers and non-academic training programmes for health professionals (clinicians hired for patient care, not to do research) should not select based on number of research publications. Promotions based on publication should be restricted to those hired to do research.
The Long-Term Impact of Vaginal Surgical Mesh Devices in UK Primary Care: A Cohort Study in the Clinical Practice Research Datalink
Purpose: Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) may be treated with surgical mesh devices; evidence of their long-term complications is lacking. Patients and Methods: Rates of diagnoses of depression, anxiety or self-harm (composite measure) and sexual dysfunction, and rates of prescriptions for antibiotics and opioids were estimated in women with and without mesh surgery, with a diagnostic SUI/POP code, registered in the Clinical Practice Research Datalink (CPRD) gold database. Results: There were 220,544 women eligible for inclusion; 74% (n = 162,687) had SUI, 37% (n = 82,123) had POP, and 11% (n = 24,266) had both. Women undergoing mesh surgery for SUI or POP had about 1.1 times higher rates of antibiotic use. Women with no previous history of the outcome, who underwent mesh surgery had 2.43 (95% CI 2.19–2.70) and 1.47 (95% CI 1.19–1.81) times higher rates of depression, anxiety, or self-harm, 1.88 (95% CI 1.50–2.36) and 1.64 (95% CI 1.02–2.63) times higher rates of sexual dysfunction and 1.40 (95% CI 1.26–1.56) and 1.23 (95% CI 1.01–1.49) times higher opioid use for SUI and POP, respectively. Women with a history of depression, anxiety and self-harm had 0.3 times lower rates of these outcomes with SUI or POP mesh surgery (HR for SUI 0.70 (95% CI 0.67-0.73), HR for POP 0.72 (95% CI 0.65-0.79)). Women with a history of opioid use who had POP mesh surgery had about 0.09 times lower rates (HR 0.91 (95% CI 0.86–0.96)) of prescriptions. Negative control outcome analyses showed no evidence of an association between asthma consultations and mesh surgery in women with POP, but the rate was 0.09 times lower (HR 0.91 (95% CI 0.87–0.94)) in women with SUI mesh surgery, suggesting that study results are subject to some residual confounding. Conclusion: Mesh surgery was associated with poor mental and sexual health outcomes, alongside increased opioid and antibiotic use, in women with no history of these outcomes and improved mental health, and lower opioid use, in women with a previous history of these outcomes. Although our results suggest an influence of residual confounding, careful con-sideration of the benefits and risk of mesh surgery for women with SUI or POP on an individual basis is required.
Understanding how and why quality circles improve standards of practice, enhance professional development and increase psychological well-being of general practitioners: a realist synthesis
ObjectivesTo understand how and why participation in quality circles (QCs) improves general practitioners’ (GPs) psychological well-being and the quality of their clinical practice. To provide evidence-informed and practical guidance to maintain QCs at local and policy levels.DesignA theory-driven mixed method.SettingPrimary healthcare.MethodWe collected data in four stages to develop and refine the programme theory of QCs: (1) coinquiry with Swiss and European expert stakeholders to develop a preliminary programme theory; (2) realist review with systematic searches in MEDLINE, Embase, PsycINFO and CINHAL (1980–2020) to inform the preliminary programme theory; (3) programme refinement through interviews with participants, facilitators, tutors and managers of QCs and (4) consolidation of theory through interviews with QC experts across Europe and examining existing theories.Sources of dataThe coinquiry comprised 4 interviews and 3 focus groups with 50 European experts. From the literature search, we included 108 papers to develop the literature-based programme theory. In stage 3, we used data from 40 participants gathered in 6 interviews and 2 focus groups to refine the programme theory. In stage 4, five interviewees from different healthcare systems consolidated our programme theory.ResultRequirements for successful QCs are governmental trust in GPs’ abilities to deliver quality improvement, training, access to educational material and performance data, protected time and financial resources. Group dynamics strongly influence success; facilitators should ensure participants exchange knowledge and generate new concepts in a safe environment. Peer interaction promotes professional development and psychological well-being. With repetition, participants gain confidence to put their new concepts into practice.ConclusionWith expert facilitation, clinical review and practice opportunities, QCs can improve the quality of standard practice, enhance professional development and increase psychological well-being in the context of adequate professional and administrative support.PROSPERO registration numberCRD42013004826.
OBJECTIVES: The social ties people have with one another are known to influence behaviour, and how information is accessed and interpreted. It is unclear, however, how the social networks that exist in multi-professional health care workplaces might be used to improve quality in hospitals. This paper develops explanatory theory using realist synthesis to illuminate the details and significance of the social ties between health care workers. Specifically we ask: How, why, for whom, to what extent and in what context, do the social ties of staff within a hospital influence quality of service delivery, including quality improvement? METHODS: From a total of 75 included documents identified through an extensive systematic literature search, data were extracted and analysed to identify emergent explanatory statements. RESULTS: The synthesis found that within the hospital workforce, an individual's place in the social whole can be understood across four identified domains: (1) social group, (2) hierarchy, (3) bridging distance and (4) discourse. Thirty-five context-mechanism-outcome configurations were developed across these domains. CONCLUSIONS: The relative position of individual health care workers within the overall social network in hospitals is associated with influence and agency. As such, power to bring about change is inequitably and socially situated, and subject to specific contexts. The findings of this realist synthesis offer a lens through which to understand social ties in hospitals. The findings can help identify possible strategies for intervention to improve communication and distribution of power, for individual, team and wider multi-professional behavioural change in hospitals.
Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settings.
BACKGROUND: In locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. METHODS: We prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using commercially-available rapid tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a held-out temporal external validation cohort. RESULTS: 426 participants were recruited, of whom 89 (21.0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0.72 to 0.74) and calibration (calibration slopes: 1.01 to 1.05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. CONCLUSIONS: We present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.