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Abstract Background Heart failure is a major global health challenge incurring a high rate of mortality, morbidity and hospitalisation. Effective medicines management at the time of hospital discharge into the community could reduce poor outcomes for people with heart failure. Within the Improving the Safety and Continuity Of Medicines management at Transitions of care (ISCOMAT) programme, the Medicines at Transitions Intervention (MaTI) was co-designed to improve such transitions, with a cluster randomised controlled trial to test effectiveness. The MaTI includes a patient toolkit and transfer of discharge medicines information to community pharmacy. This paper aims to determine the degree to which the intervention was delivered, and identify barriers and facilitators experienced by staff for the successful implementation of the intervention. Methods The study was conducted in six purposively selected intervention sites. A mixed-methods design was employed using hospital staff interviews, structured and unstructured ward observations, and routine trial data about adherence to the MaTI. A parallel mixed analysis was applied. Qualitative data were analysed thematically using the Framework method. Data were synthesised, triangulated and mapped to the Consolidated Framework for Implementation Research (CFIR). Results With limited routines of communication between ward staff and community pharmacy, hospital staff found implementing community pharmacy-related steps of the intervention challenging. Staff time was depleted by attempts to bridge system barriers, sometimes leading to steps not being delivered. Whilst the introduction of the patient toolkit was often completed and valued as important patient education and a helpful way to explain medicines, the medicines discharge log within it was not, as this was seen as a duplication of existing systems. Within the CFIR the most applicable constructs were identified as ‘intervention complexity’ and ‘cosmopolitanism’ based on how well hospitals were networked with community pharmacies, and the availability of hospital resources to facilitate this. Conclusion The MaTI was generally successfully implemented, particularly the introduction of the toolkit. However, implementation involving community pharmacy was more challenging and more effective communication systems are needed to support wider implementation. Trial registration 11/04/2018 ISRCTN66212970. https://www.isrctn.com/ISRCTN66212970 .

Background: Cardiovascular disease (CVD) causes a quarter of all deaths in the UK,1 and the NHS Long Term Plan aims for earlier detection and treatment of cardiovascular, renal and metabolic risk factors.2 We trained, tested and prospectively implemented a machine learning algorithm in primary care electronic health record (EHR) data to identify individuals at higher risk of incident cardio-renal-metabolic diseases and cardiovascular death.3, 4 Methods: We used UK primary care EHR data from 2,081,139 individuals aged ≥30 years (2 January 1998, 30 November 2018), randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities (OPTIMISE). We calculated the cumulative incidence rate for ten cardio-renal-metabolic diseases and death, and excluded individuals for the analysis of each disease who had a preceding diagnosis of that disease. Fine and Gray's models with competing risk of death were fit for each outcome between higher and lower predicted risk.We implemented OPTIMISE in a pilot interventional non-randomised single arm study across four primary care sites. Consenting individuals aged ≥30 years at higher predicted risk received community-based cardio-renal-metabolic phenotyping and assessment for guideline-adherence of current treatment. Results: In the testing dataset (n = 416,228), individuals at higher predicted risk had higher long-term risk of heart failure (HR 12.54, 95% CI 12.08–13.01), aortic stenosis (9.98, 9.16–10.87), AF (HR 8·75, 95% CI 8·44–9·06), stroke/TIA (8.07, 7.80–8.34), chronic kidney disease (CKD) (6.85, 6.70–7.00), peripheral vascular disease (6.62, 6.28–6.98), valvular heart disease (6.49, 6.14–6.85), MI (5.02, 4.82–5.22), diabetes (2.05, 2.00–2.10) and COPD (2.02, 2.00–2.05) (Figure 1). They were also at higher risk of death (10.45, 10.23–10.68), accounting for 74% of cardiovascular deaths (8,582/11,676).Of 82 higher risk patients in the pilot clinical implementation (mean age 71.6 years (SD 7.5), 50% women), 78.0% had hypertension and 37.8% had type 2 diabetes (Table 1). Of higher risk patients with hypertension, 58.5% (31/53) of those aged 140 mmHg, and 54.5% (6/11) of those aged ≥80 years had a SBP >150 mmHg. Of those with type 2 diabetes and co-existent CVD, only 23.1% (3/13) were on SGLT2 inhibitor therapy. Of higher risk patients on statin therapy, 37.0% (20/54) had LDL-cholesterol >1.8 mmol/L, and 23.1% (3/13) of patients with previous CVD had an LDL-cholesterol >2.0 mmol/L.Furthermore, 19.5% (16/82) of the higher risk cohort had undiagnosed moderate or high risk CKD. Those with unrecognised CKD were often not on a statin (41.7%; 5/12), ACEi/ARB therapy with co-existent hypertension (61.5%; 8/13), or SGLT2 inhibitor with co-existent diabetes (83.3%; 5/6). Almost half of the cohort (49%) were found to be obese, and 17% (14/82) were eligible for GLP-1 RA therapy. Conclusions: The machine learning OPTIMISE algorithm can identify people at higher risk of cardio-renal-metabolic diseases and death in UK primary care EHR data. On prospective evaluation higher risk individuals have unrecorded and undertreated cardio-renal-metabolic diseases, which are actionable targets for integrated multi-disciplinary preventative care.

BackgroundThe occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making.Methods and findingsThis nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only-as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI.ConclusionsIn this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.

Abstract Metformin is an antihyperglycemic used to treat type 2 diabetes mellitus (T2DM). Patients with T2DM are at increased risk of cardiovascular disease. We explored the association between metformin use and cardiovascular magnetic resonance (CMR) derived stress myocardial blood flow (MBF), myocardial perfusion reserve (MPR) and major adverse cardiovascular events (MACE; all cause death, MI, stroke, heart failure hospitalisation and coronary revascularisation) in patients with T2DM. Multi-centre study of patients with T2DM, and healthy controls, underwent quantitative myocardial perfusion CMR using an artificial intelligence supported process. Multivariable regression analysis, and cox proportional hazard models of propensity score weighted patients quantified associations between metformin use, MBF, MPR, all cause death and MACE. Analysis included 572 patients with T2DM (68% prescribed metformin) with median follow-up 851 days (IQR 935 − 765). Metformin use was associated with an increase of MPR of 0.12 [0.08–0.40], p = 0.004. There were 82 MACE events (14.3%) including 25 (4.4%) deaths of which 16 were in those not prescribed metformin (8.7%), compared to 9 in patients prescribed metformin (2.3%): adjusted hazard ratio 0.24 (95% CI 0.08–0.70, p = 0.009). MACE events were similar between groups. This multicentre, inverse probability weighting propensity score analysis study showed that in patients with T2DM, metformin use is associated with higher MPR and improved all cause survival.

Background A growing population of patients with chronic kidney disease (CKD) presents with non–ST‐segment–elevation myocardial infarction, although little is known about their longer‐term mortality. Methods and Results Using the MINAP (Myocardial Ischaemia National Audit Project) registry, linked to Office for National Statistics mortality data, we analyzed 363 559 UK patients with non–ST‐segment–elevation myocardial infarction, with or without CKD. Cox regression models were fitted, adjusting for baseline demographics. Compared with patients without CKD, patients with CKD were less frequently prescribed P2Y12 inhibitors (89% versus 86%, P