Publications by authors named "Harry Hemingway"

196 Publications

'What is the risk to me from COVID-19?': Public involvement in providing mortality risk information for people with 'high-risk' conditions for COVID-19 (OurRisk.CoV).

Clin Med (Lond) 2021 Nov;21(6):e620-e628

University College London, London, UK, research director, Health Data Research UK, London, UK, and director of healthcare informatics, genomics/omics, data science, UCL Hospitals NIHR Biomedical Research Centre, London, UK.

Patients and public have sought mortality risk information throughout the pandemic, but their needs may not be served by current risk prediction tools. Our mixed methods study involved: (1) systematic review of published risk tools for prognosis, (2) provision and patient testing of new mortality risk estimates for people with high-risk conditions and (3) iterative patient and public involvement and engagement with qualitative analysis. Only one of 53 (2%) previously published risk tools involved patients or the public, while 11/53 (21%) had publicly accessible portals, but all for use by clinicians and researchers.Among people with a wide range of underlying conditions, there has been sustained interest and engagement in accessible and tailored, pre- and postpandemic mortality information. Informed by patient feedback, we provide such information in 'five clicks' (https://covid19-phenomics.org/OurRiskCoV.html), as context for decision making and discussions with health professionals and family members. Further development requires curation and regular updating of NHS data and wider patient and public engagement.
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http://dx.doi.org/10.7861/clinmed.2021-0386DOI Listing
November 2021

An informatics consult approach for generating clinical evidence for treatment decisions.

BMC Med Inform Decis Mak 2021 10 12;21(1):281. Epub 2021 Oct 12.

Institute of Health Informatics, University College London, London, UK.

Background: An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis.

Methods: We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems.

Results: We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39-1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49-0.76) and ischaemic stroke (HR = 0.27, CI 0.08-0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results.

Conclusion: We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on 'patients like me'. We identify the key challenges in offering such an Informatics Consult as a service.
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http://dx.doi.org/10.1186/s12911-021-01638-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506488PMC
October 2021

Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure.

JAMIA Open 2021 Jul 4;4(3):ooab001. Epub 2021 Feb 4.

Institute of Health Informatics, University College London, London, UK.

Objective: The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms.

Materials And Methods: Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers.

Results: We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD).

Conclusion: Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.
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http://dx.doi.org/10.1093/jamiaopen/ooab001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423424PMC
July 2021

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records.

Lancet Diabetes Endocrinol 2021 10 2;9(10):681-694. Epub 2021 Sep 2.

Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK.

Background: Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).

Methods: In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.

Findings: We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.

Interpretation: A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.

Funding: The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.
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http://dx.doi.org/10.1016/S2213-8587(21)00207-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440227PMC
October 2021

The genomics of heart failure: design and rationale of the HERMES consortium.

ESC Heart Fail 2021 Sep 3. Epub 2021 Sep 3.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Aims: The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure.

Methods And Results: The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 × 10 under an additive genetic model.

Conclusions: HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.
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http://dx.doi.org/10.1002/ehf2.13517DOI Listing
September 2021

Weight Change and the Onset of Cardiovascular Diseases: Emulating Trials Using Electronic Health Records.

Epidemiology 2021 09;32(5):744-755

From the Institute of Health Informatics, University College London (UCL), London, United Kingdom.

Background: Cross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD.

Methods: We estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45-69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group.

Results: Among those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (-0.2% to 1.7%)] and the weight gain group [RD = 0.7% (-0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = -1.4% (-2.4% to -0.6%)] but not of stroke. When we assumed that chronic disease occurred 1-3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals.

Conclusion: Among individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.
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http://dx.doi.org/10.1097/EDE.0000000000001393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318567PMC
September 2021

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility.

BMC Med 2021 04 6;19(1):85. Epub 2021 Apr 6.

Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK.

Background: Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

Methods: For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

Results: Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

Conclusions: Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.
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http://dx.doi.org/10.1186/s12916-021-01940-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022365PMC
April 2021

COVID-19 infection and attributable mortality in UK care homes: cohort study using active surveillance and electronic records (March-June 2020).

Age Ageing 2021 06;50(4):1019-1028

Institute of Health Informatics, University College London, NW1 2DA, London, UK.

Background: epidemiological data on COVID-19 infection in care homes are scarce. We analysed data from a large provider of long-term care for older people to investigate infection and mortality during the first wave of the pandemic.

Methods: cohort study of 179 UK care homes with 9,339 residents and 11,604 staff. We used manager-reported daily tallies to estimate the incidence of suspected and confirmed infection and mortality in staff and residents. Individual-level electronic health records from 8,713 residents were used to model risk factors for confirmed infection, mortality and estimate attributable mortality.

Results: 2,075/9,339 residents developed COVID-19 symptoms (22.2% [95% confidence interval: 21.4%; 23.1%]), while 951 residents (10.2% [9.6%; 10.8%]) and 585 staff (5.0% [4.7%; 5.5%]) had laboratory-confirmed infections. The incidence of confirmed infection was 152.6 [143.1; 162.6] and 62.3 [57.3; 67.5] per 100,000 person-days in residents and staff, respectively. Sixty-eight percent (121/179) of care homes had at least one COVID-19 infection or COVID-19-related death. Lower staffing ratios and higher occupancy rates were independent risk factors for infection.Out of 607 residents with confirmed infection, 217 died (case fatality rate: 35.7% [31.9%; 39.7%]). Mortality in residents with no direct evidence of infection was twofold higher in care homes with outbreaks versus those without (adjusted hazard ratio: 2.2 [1.8; 2.6]).

Conclusions: findings suggest many deaths occurred in people who were infected with COVID-19, but not tested. Higher occupancy and lower staffing levels were independently associated with risks of infection. Protecting staff and residents from infection requires regular testing for COVID-19 and fundamental changes to staffing and care home occupancy.
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http://dx.doi.org/10.1093/ageing/afab060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989651PMC
June 2021

Temporal trends in heart failure medication prescription in a population-based cohort study.

BMJ Open 2021 03 2;11(3):e043290. Epub 2021 Mar 2.

Institute of Health Informatics, University College London, London, UK.

Objective: We examined temporal heart failure (HF) prescription patterns in a large representative sample of real-world patients in the UK, using electronic health records (EHR).

Methods: From primary and secondary care EHR, we identified 85 732 patients with a HF diagnosis between 2002 and 2015. Almost 50% of patients with HF were women and the median age was 79.1 (IQR 70.2-85.7) years, with age at diagnosis increasing over time.

Results: We found several trends in pharmacological HF management, including increased beta blocker prescriptions over time (29% in 2002-2005 and 54% in 2013-2015), which was not observed for mineralocorticoid receptor-antagonists (MR-antagonists) (18% in 2002-2005 and 18% in 2013-2015); higher prescription rates of loop diuretics in women and elderly patients together with lower prescription rates of angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers, beta blockers or MR-antagonists in these patients; little change in medication prescription rates occurred after 6 months of HF diagnosis and, finally, patients hospitalised for HF who had no recorded follow-up in primary care had considerably lower prescription rates compared with patients with a HF diagnosis in primary care with or without HF hospitalisation.

Conclusion: In the general population, the use of MR-antagonists for HF remained low and did not change throughout 13 years of follow-up. For most patients, few changes were seen in pharmacological management of HF in the 6 months following diagnosis.
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http://dx.doi.org/10.1136/bmjopen-2020-043290DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929882PMC
March 2021

A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems.

JAMIA Open 2020 Dec 5;3(4):545-556. Epub 2020 Dec 5.

Institute of Health Informatics, University College London, London, UK.

Objectives: The UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers.

Materials And Methods: We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models.

Results: We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured.

Discussion And Conclusion: Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms.
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http://dx.doi.org/10.1093/jamiaopen/ooaa047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717266PMC
December 2020

Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic.

Eur J Prev Cardiol 2021 Feb 21. Epub 2021 Feb 21.

Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA.

Aims: Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both 'direct', through infection, and 'indirect', through changes in healthcare.

Methods And Results: We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0).Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60-100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2-3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths.

Conclusion: Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic.
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http://dx.doi.org/10.1093/eurjpc/zwaa155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928969PMC
February 2021

Data-driven identification of ageing-related diseases from electronic health records.

Sci Rep 2021 02 3;11(1):2938. Epub 2021 Feb 3.

Health Data Research UK London, University College London, London, UK.

Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82-83) for Cluster 1, 77 years (IQR 75-77) for Cluster 2, 69 years (IQR 66-71) for Cluster 3 and 57 years (IQR 54-59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.
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http://dx.doi.org/10.1038/s41598-021-82459-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859412PMC
February 2021

Estimated impact of the COVID-19 pandemic on cancer services and excess 1-year mortality in people with cancer and multimorbidity: near real-time data on cancer care, cancer deaths and a population-based cohort study.

BMJ Open 2020 11 17;10(11):e043828. Epub 2020 Nov 17.

Institute of Health Informatics, University College London, London, UK.

Objectives: To estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer.

Methods: We employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England.

Results: Declines in urgent referrals (median=-70.4%) and chemotherapy attendances (median=-41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=-44.5%) and chemotherapy attendances (median=-31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity.

Conclusions: Dramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.
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http://dx.doi.org/10.1136/bmjopen-2020-043828DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674020PMC
November 2020

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

BMJ 2020 10 20;371:m3731. Epub 2020 Oct 20.

Nuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK

Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

Design: Population based cohort study.

Setting And Participants: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

Main Outcome Measures: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

Results: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.

Conclusion: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
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http://dx.doi.org/10.1136/bmj.m3731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574532PMC
October 2020

Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK.

Heart 2020 12 5;106(24):1890-1897. Epub 2020 Oct 5.

The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK

Objective: To monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects.

Methods: Retrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018-2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends.

Results: Across nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%-58.6%) and 52.9% (52.2%-53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown and fell by 31%-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020.

Conclusions: Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.
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http://dx.doi.org/10.1136/heartjnl-2020-317870DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536637PMC
December 2020

Models for mortality require tailoring in the context of the COVID-19 pandemic - Authors' reply.

Lancet 2020 09;396(10255):883-884

Institute of Health Informatics, University College London, London NW1 1DA, UK; Health Data Research UK, London, UK.

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http://dx.doi.org/10.1016/S0140-6736(20)31966-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515579PMC
September 2020

Corrigendum to: "Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis" [J Hepatol (2020) 241-251].

J Hepatol 2020 Dec 18;73(6):1594-1595. Epub 2020 Sep 18.

Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK; Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden. Electronic address:

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http://dx.doi.org/10.1016/j.jhep.2020.08.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055539PMC
December 2020

Invasive versus non-invasive management of older patients with non-ST elevation myocardial infarction (SENIOR-NSTEMI): a cohort study based on routine clinical data.

Lancet 2020 08;396(10251):623-634

National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, London, UK. Electronic address:

Background: Previous trials suggest lower long-term risk of mortality after invasive rather than non-invasive management of patients with non-ST elevation myocardial infarction (NSTEMI), but the trials excluded very elderly patients. We aimed to estimate the effect of invasive versus non-invasive management within 3 days of peak troponin concentration on the survival of patients aged 80 years or older with NSTEMI.

Methods: Routine clinical data for this study were obtained from five collaborating hospitals hosting NIHR Biomedical Research Centres in the UK (all tertiary centres with emergency departments). Eligible patients were 80 years old or older when they underwent troponin measurements and were diagnosed with NSTEMI between 2010 (2008 for University College Hospital) and 2017. Propensity scores (patients' estimated probability of receiving invasive management) based on pretreatment variables were derived using logistic regression; patients with high probabilities of non-invasive or invasive management were excluded. Patients who died within 3 days of peak troponin concentration without receiving invasive management were assigned to the invasive or non-invasive management groups based on their propensity scores, to mitigate immortal time bias. We estimated mortality hazard ratios comparing invasive with non-invasive management, and compared the rate of hospital admissions for heart failure.

Findings: Of the 1976 patients with NSTEMI, 101 died within 3 days of their peak troponin concentration and 375 were excluded because of extreme propensity scores. The remaining 1500 patients had a median age of 86 (IQR 82-89) years of whom (845 [56%] received non-invasive management. During median follow-up of 3·0 (IQR 1·2-4·8) years, 613 (41%) patients died. The adjusted cumulative 5-year mortality was 36% in the invasive management group and 55% in the non-invasive management group (adjusted hazard ratio 0·68, 95% CI 0·55-0·84). Invasive management was associated with lower incidence of hospital admissions for heart failure (adjusted rate ratio compared with non-invasive management 0·67, 95% CI 0·48-0·93).

Interpretation: The survival advantage of invasive compared with non-invasive management appears to extend to patients with NSTEMI who are aged 80 years or older.

Funding: NIHR Imperial Biomedical Research Centre, as part of the NIHR Health Informatics Collaborative.
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http://dx.doi.org/10.1016/S0140-6736(20)30930-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456783PMC
August 2020

Clinical academic research in the time of Corona: A simulation study in England and a call for action.

PLoS One 2020 13;15(8):e0237298. Epub 2020 Aug 13.

Barts NHS Trust, London, United Kingdom.

Objectives: We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research.

Design: A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which affect the ability to conduct research in the COVID-19 climate: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics).

Setting: Clinical academics in primary and secondary care in England.

Participants: Equivalent of 3200 full-time clinical academics in England.

Interventions: Four policy approaches to COVID-19 with differing population infection rates: "Italy model" (6%), "mitigation" (10%), "relaxed mitigation" (40%) and "do-nothing" (80%) scenarios. Low and high strain on the health system (no clinical academics able to do research at 10% and 5% infection rate, respectively.

Main Outcome Measures: Number of full-time clinical academics available to conduct clinical research during the pandemic in England.

Results: In the "Italy model", "mitigation", "relaxed mitigation" and "do-nothing" scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively-with no clinical academics at all for 37 days in the "do-nothing" scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11, 12, 30 and 26 weeks respectively.

Conclusions: Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237298PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425844PMC
August 2020

Children with COVID-19 at a specialist centre: initial experience and outcome.

Lancet Child Adolesc Health 2020 08 23;4(8):e30-e31. Epub 2020 Jun 23.

Digital Research and Informatics Unit, Great Ormond Street Hospital and Great Ormond Street Institute of Child Health and NIHR GOSH Biomedical Research Centre, London WC1N 3JH, UK.

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http://dx.doi.org/10.1016/S2352-4642(20)30204-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308787PMC
August 2020

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

Lancet 2020 05 12;395(10238):1715-1725. Epub 2020 May 12.

Institute of Health Informatics, University College London, London, UK; Health Data Research UK, London, UK.

Background: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease.

Methods: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation.

Findings: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0.

Interpretation: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality.

Funding: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.
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http://dx.doi.org/10.1016/S0140-6736(20)30854-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217641PMC
May 2020

Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis.

J Hepatol 2020 08 2;73(2):241-251. Epub 2020 Apr 2.

Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK; Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden. Electronic address:

Background & Aims: MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases.

Methods: First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures.

Results: We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p <5×10). Four of the variants (rs759359281 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated aminotransferases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and body mass index were causally associated with elevated cT1, whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective.

Conclusion: The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals.

Lay Summary: We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.
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http://dx.doi.org/10.1016/j.jhep.2020.03.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372222PMC
August 2020

Prognostic significance of troponin level in 3121 patients presenting with atrial fibrillation (The NIHR Health Informatics Collaborative TROP-AF study).

J Am Heart Assoc 2020 04 26;9(7):e013684. Epub 2020 Mar 26.

NIHR Imperial Biomedical Research Centre Imperial College London and Imperial College Healthcare NHS Trust London United Kingdom.

Background Patients presenting with atrial fibrillation (AF) often undergo a blood test to measure troponin, but interpretation of the result is impeded by uncertainty about its clinical importance. We investigated the relationship between troponin level, coronary angiography, and all-cause mortality in real-world patients presenting with AF. Methods and Results We used National Institute of Health Research Health Informatics Collaborative data to identify patients admitted between 2010 and 2017 at 5 tertiary centers in the United Kingdom with a primary diagnosis of AF. Peak troponin results were scaled as multiples of the upper limit of normal. A total of 3121 patients were included in the analysis. Over a median follow-up of 1462 (interquartile range, 929-1975) days, there were 586 deaths (18.8%). The adjusted hazard ratio for mortality associated with a positive troponin (value above upper limit of normal) was 1.20 (95% CI, 1.01-1.43; <0.05). Higher troponin levels were associated with higher risk of mortality, reaching a maximum hazard ratio of 2.6 (95% CI, 1.9-3.4) at ≈250 multiples of the upper limit of normal. There was an exponential relationship between higher troponin levels and increased odds of coronary angiography. The mortality risk was 36% lower in patients undergoing coronary angiography than in those who did not (adjusted hazard ratio, 0.61; 95% CI, 0.42-0.89; =0.01). Conclusions Increased troponin was associated with increased risk of mortality in patients presenting with AF. The lower hazard ratio in patients undergoing invasive management raises the possibility that the clinical importance of troponin release in AF may be mediated by coronary artery disease, which may be responsive to revascularization.
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http://dx.doi.org/10.1161/JAHA.119.013684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428631PMC
April 2020

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure.

Nat Commun 2020 01 9;11(1):163. Epub 2020 Jan 9.

Department of Biostatistics, University of Liverpool, Liverpool, UK.

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.
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http://dx.doi.org/10.1038/s41467-019-13690-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952380PMC
January 2020

Improving the odds of drug development success through human genomics: modelling study.

Sci Rep 2019 12 11;9(1):18911. Epub 2019 Dec 11.

Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Administration, Boston, MA, USA.

Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases - the 'disease-ome' - represented as columns; and all protein coding genes - 'the protein-coding genome'- represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.
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http://dx.doi.org/10.1038/s41598-019-54849-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906499PMC
December 2019

Metformin use and cardiovascular outcomes after acute myocardial infarction in patients with type 2 diabetes: a cohort study.

Cardiovasc Diabetol 2019 12 9;18(1):168. Epub 2019 Dec 9.

The Hatter Cardiovascular Institute, University College London, 67 Chenies Mews, London, WC1E 6HX, UK.

Background: The use of metformin after acute myocardial infarction (AMI) has been associated with reduced mortality in people with type 2 diabetes mellitus (T2DM). However, it is not known if it is acutely cardioprotective in patients taking metformin at the time of AMI. We compared patient outcomes according to metformin status at the time of admission for fatal and non-fatal AMI in a large cohort of patients in England.

Methods: This study used linked data from primary care, hospital admissions and death registry from 4.7 million inhabitants in England, as part of the CALIBER resource. The primary endpoint was a composite of acute myocardial infarction requiring hospitalisation, stroke and cardiovascular death. The secondary endpoints were heart failure (HF) hospitalisation and all-cause mortality.

Results: 4,030 patients with T2DM and incident AMI recorded between January 1998 and October 2010 were included. At AMI admission, 63.9% of patients were receiving metformin and 36.1% another oral hypoglycaemic drug. Median follow-up was 343 (IQR: 1-1436) days. Adjusted analyses showed an increased hazard of the composite endpoint in metformin users compared to non-users (HR 1.09 [1.01-1.19]), but not of the secondary endpoints. The higher risk of the composite endpoint in metformin users was only observed in people taking metformin at AMI admission, whereas metformin use post-AMI was associated with a reduction in risk of all-cause mortality (0.76 [0.62-0.93], P = 0.009).

Conclusions: Our study suggests that metformin use at the time of first AMI is associated with increased risk of cardiovascular disease and death in patients with T2DM, while its use post-AMI might be beneficial. Further investigation in well-designed randomised controlled trials is indicated, especially in view of emerging evidence of cardioprotection from sodium-glucose co-transporter-2 (SGLT2) inhibitors.
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http://dx.doi.org/10.1186/s12933-019-0972-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900858PMC
December 2019

Association of troponin level and age with mortality in 250 000 patients: cohort study across five UK acute care centres.

BMJ 2019 11 20;367:l6055. Epub 2019 Nov 20.

NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK

Objective: To determine the relation between age and troponin level and its prognostic implication.

Design: Retrospective cohort study.

Setting: Five cardiovascular centres in the UK National Institute for Health Research Health Informatics Collaborative (UK-NIHR HIC).

Participants: 257 948 consecutive patients undergoing troponin testing for any clinical reason between 2010 and 2017.

Main Outcome Measure: All cause mortality.

Results: 257 948 patients had troponin measured during the study period. Analyses on troponin were performed using the peak troponin level, which was the highest troponin level measured during the patient's hospital stay. Troponin levels were standardised as a multiple of each laboratory's 99th centile of the upper limit of normal (ULN). During a median follow-up of 1198 days (interquartile range 514-1866 days), 55 850 (21.7%) deaths occurred. A positive troponin result (that is, higher than the upper limit of normal) signified a 3.2 higher mortality hazard (95% confidence interval 3.1 to 3.2) over three years. Mortality varied noticeably with age, with a hazard ratio of 10.6 (8.5 to 13.3) in 18-29 year olds and 1.5 (1.4 to 1.6) in those older than 90. A positive troponin result was associated with an approximately 15 percentage points higher absolute three year mortality across all age groups. The excess mortality with a positive troponin result was heavily concentrated in the first few weeks. Results were analysed using multivariable adjusted restricted cubic spline Cox regression. A direct relation was seen between troponin level and mortality in patients without acute coronary syndrome (ACS, n=120 049), whereas an inverted U shaped relation was found in patients with ACS (n=14 468), with a paradoxical decline in mortality at peak troponin levels >70×ULN. In the group with ACS, the inverted U shaped relation persisted after multivariable adjustment in those who were managed invasively; however, a direct positive relation was found between troponin level and mortality in patients managed non-invasively.

Conclusions: A positive troponin result was associated with a clinically important increased mortality, regardless of age, even if the level was only slightly above normal. The excess mortality with a raised troponin was heavily concentrated in the first few weeks.

Study Registration: ClinicalTrials.gov NCT03507309.
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http://dx.doi.org/10.1136/bmj.l6055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865859PMC
November 2019
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