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van Staa T, Sharma A, Palin V, Fahmi A, Cant H, Zhong X, Jury F, Gold N, Welfare W, Ashcroft D, Tsang JY, Elliott RA, Sutton C, Armitage C, Couch P, Moulton G, Tempest E, Buchan IE. Knowledge support for optimising antibiotic prescribing for common infections in general practices: evaluation of the effectiveness of periodic feedback, decision support during consultations and peer comparisons in a cluster randomised trial (BRIT2) - study protocol. BMJ Open 2023; 13:e076296. [PMID: 37607793 PMCID: PMC10445367 DOI: 10.1136/bmjopen-2023-076296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/12/2023] [Indexed: 08/24/2023] Open
Abstract
INTRODUCTION This project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation of quality improvement activities by the clinicians. The main research question is, can a knowledge support system (KSS) intervention within an LHS implementation improve antibiotic prescribing without increasing the risk of complications? METHODS AND ANALYSIS A pragmatic cluster randomised controlled trial will be conducted, with randomisation of at least 112 general practices in North-West England. General practices participating in the trial will be randomised to the following interventions: periodic practice-level and individual prescriber feedback using dashboards; or the same dashboards plus a KSS. Data from large databases of healthcare records are used to characterise heterogeneity in antibiotic uses, and to calculate risk scores for clinical outcomes and for the effectiveness of different treatment strategies. The results provide the baseline content for the dashboards and KSS. The KSS comprises a display within the electronic health record used during the consultation; the prescriber (general practitioner or allied health professional) will answer standard questions about the patient's presentation and will then be presented with information (eg, patient's risk of complications from the infection) to guide decision making. The KSS can generate information sheets for patients, conveyed by the clinicians during consultations. The primary outcome is the practice-level rate of antibiotic prescribing (per 1000 patients) with secondary safety outcomes. The data from practices participating in the trial and the dashboard infrastructure will be held within regional shared care record systems of the National Health Service in the UK. ETHICS AND DISSEMINATION Approved by National Health Service Ethics Committee IRAS 290050. The research results will be published in peer-reviewed journals and also disseminated to participating clinical staff and policy and guideline developers. TRIAL REGISTRATION NUMBER ISRCTN16230629.
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Affiliation(s)
- Tjeerd van Staa
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | | | - Victoria Palin
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Ali Fahmi
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Harriet Cant
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Xiaomin Zhong
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Francine Jury
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Natalie Gold
- Faculty of Philosophy, University of Oxford, Oxford, UK
| | | | - Darren Ashcroft
- NIHR School for Primary Care Research, Centre for Primary Care and Health Services Research, The University of Manchester, Manchester, UK
| | - Jung Yin Tsang
- Centre for Primary Care and Health Services Research, University of Manchester, Manchester, UK
| | - Rachel Ann Elliott
- Manchester Centre for Health Economics, The University of Manchester, Manchester, UK
| | - Christopher Sutton
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, UK
| | - Chris Armitage
- Manchester Centre for Health Psychology, University of Manchester, Manchester, UK
| | - Philip Couch
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Georgina Moulton
- Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Edward Tempest
- Centre for Health Informatics, The University of Manchester, Manchester, UK
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Choi WM, Yip TCF, Wong GLH, Kim WR, Yee LJ, Brooks-Rooney C, Curteis T, Cant H, Chen CH, Chen CY, Huang YH, Jin YJ, Jun DW, Kim JW, Park NH, Peng CY, Shin HP, Shin JW, Yang YH, Lim YS. Hepatocellular carcinoma risk in patients with chronic hepatitis B receiving tenofovir- vs. entecavir-based regimens: Individual patient data meta-analysis. J Hepatol 2023; 78:534-542. [PMID: 36572349 DOI: 10.1016/j.jhep.2022.12.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND & AIMS The comparative risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) receiving tenofovir disoproxil fumarate (TDF) vs. entecavir (ETV) remains controversial. In this individual patient data (IPD) meta-analysis, we aimed to compare HCC risk between the two drugs and identify subgroups who may benefit more from one treatment than the other. METHODS Published meta-analyses, electronic databases and congress proceedings were searched to identify eligible studies through January 2021. We compared HCC risk between the two drugs using a multivariable Cox proportional hazards model with anonymised IPD from treatment-naïve patients with CHB receiving TDF or ETV for ≥1 year. Treatment effect consistency was explored in propensity score matching (PSM), weighting (PSW) and subgroup analyses for age, sex, hepatitis B e-antigen (HBeAg) positivity, cirrhosis and diabetes status. RESULTS We included 11 studies from Korea, Taiwan and Hong Kong involving 42,939 patients receiving TDF (n = 6,979) or ETV (n = 35,960) monotherapy. Patients receiving TDF had significantly lower HCC risk (adjusted hazard ratio [HR] 0.77; 95% CI 0.61-0.98; p = 0.03). Lower HCC risk with TDF was consistently observed in PSM (HR 0.73; 95% CI 0.59-0.88; p <0.01) and PSW (HR 0.83; 95% CI 0.67-1.03; p = 0.10) analyses and in all subgroups, with statistical significance in the ≥50 years of age (HR 0.76; 95% CI 0.58-1.00; p <0.05), male (HR 0.74; 95% CI 0.58-0.96; p = 0.02), HBeAg-positive (HR 0.69; 95% CI 0.49-0.97; p = 0.03) and non-diabetic (HR 0.79; 95% CI 0.63-1.00; p <0.05) subgroups. CONCLUSION TDF was associated with significantly lower HCC risk than ETV in patients with CHB, particularly those with HBeAg positivity. Longer follow-up may be needed to better define incidence differences between the treatments in various subgroups. IMPACT AND IMPLICATIONS Previous aggregate data meta-analyses have reported inconsistent conclusions on the relative effectiveness of tenofovir disoproxil fumarate and entecavir in reducing hepatocellular carcinoma risk in patients with chronic hepatitis B (CHB). This individual patient data meta-analysis on 11 studies involving 42,939 patients from Korea, Taiwan and Hong Kong suggested that tenofovir disoproxil fumarate-treated patients have a significantly lower hepatocellular carcinoma risk than entecavir-treated patients, which was observed in all subgroups of clinical interest and by different analytical methodologies. These findings should be taken into account by healthcare providers when determining the optimal course of treatment for patients with CHB and may be considered in ensuring that treatment guidelines for CHB remain pertinent.
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Affiliation(s)
- Won-Mook Choi
- Department of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Terry Cheuk-Fung Yip
- CUHK Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Grace Lai-Hung Wong
- CUHK Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - W Ray Kim
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California, USA
| | | | | | | | | | - Chien-Hung Chen
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung, Taiwan
| | - Chi-Yi Chen
- Division of Hepatogastroenterology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi, Taiwan
| | - Yi-Hsiang Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Young-Joo Jin
- Digestive Disease Center, Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon, Republic of Korea
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jin-Wook Kim
- Department of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Neung Hwa Park
- Department of Internal Medicine, University of Ulsan College of Medicine, Ulsan University Hospital, 877 Bangeojinsunhwando-ro, Dong-gu, Ulsan, 44033, Republic of Korea; Biomedical Research Center, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan, Republic of Korea
| | - Cheng-Yuan Peng
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan; School of Medicine, China Medical University, Taichung, Taiwan
| | - Hyun Phil Shin
- Department of Gastroenterology and Hepatology, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Republic of Korea
| | - Jung Woo Shin
- Department of Internal Medicine, University of Ulsan College of Medicine, Ulsan University Hospital, 877 Bangeojinsunhwando-ro, Dong-gu, Ulsan, 44033, Republic of Korea
| | - Yao-Hsu Yang
- Department of Traditional Chinese Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan; Health Information and Epidemiology Laboratory, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Young-Suk Lim
- Department of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Fahmi A, Wong D, Walker L, Buchan I, Pirmohamed M, Sharma A, Cant H, Ashcroft DM, van Staa TP. Combinations of medicines in patients with polypharmacy aged 65-100 in primary care: Large variability in risks of adverse drug related and emergency hospital admissions. PLoS One 2023; 18:e0281466. [PMID: 36753492 PMCID: PMC9907844 DOI: 10.1371/journal.pone.0281466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Polypharmacy can be a consequence of overprescribing that is prevalent in older adults with multimorbidity. Polypharmacy can cause adverse reactions and result in hospital admission. This study predicted risks of adverse drug reaction (ADR)-related and emergency hospital admissions by medicine classes. METHODS We used electronic health record data from general practices of Clinical Practice Research Datalink (CPRD GOLD) and Aurum. Older patients who received at least five medicines were included. Medicines were classified using the British National Formulary sections. Hospital admission cases were propensity-matched to controls by age, sex, and propensity for specific diseases. The matched data were used to develop and validate random forest (RF) models to predict the risk of ADR-related and emergency hospital admissions. Shapley Additive eXplanation (SHAP) values were calculated to explain the predictions. RESULTS In total, 89,235 cases with polypharmacy and hospitalised with an ADR-related admission were matched to 443,497 controls. There were over 112,000 different combinations of the 50 medicine classes most implicated in ADR-related hospital admission in the RF models, with the most important medicine classes being loop diuretics, domperidone and/or metoclopramide, medicines for iron-deficiency anaemias and for hypoplastic/haemolytic/renal anaemias, and sulfonamides and/or trimethoprim. The RF models strongly predicted risks of ADR-related and emergency hospital admission. The observed Odds Ratio in the highest RF decile was 7.16 (95% CI 6.65-7.72) in the validation dataset. The C-statistics for ADR-related hospital admissions were 0.58 for age and sex and 0.66 for RF probabilities. CONCLUSIONS Polypharmacy involves a very large number of different combinations of medicines, with substantial differences in risks of ADR-related and emergency hospital admissions. Although the medicines may not be causally related to increased risks, RF model predictions may be useful in prioritising medication reviews. Simple tools based on few medicine classes may not be effective in identifying high risk patients.
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Affiliation(s)
- Ali Fahmi
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
- * E-mail:
| | - David Wong
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Lauren Walker
- Institute of Population Health, NIHR Applied Research Collaboration North West Coast, University of Liverpool, Liverpool, United Kingdom
| | - Iain Buchan
- Institute of Population Health, NIHR Applied Research Collaboration North West Coast, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology (ISMIB) University of Liverpool, Liverpool, United Kingdom
| | - Anita Sharma
- Chadderton South Health Centre, Eaves Lane, Chadderton, United Kingdom
| | - Harriet Cant
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Darren M. Ashcroft
- Centre for Pharmacoepidemiology and Drug Safety, NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Tjeerd Pieter van Staa
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
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