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There’s nothing like feeling safe in one of your most vulnerable moments. Mr. Seni was so warm, professional and calm every step of the way. I would do my next delivery with him and recommend that anyone considering a consultant-led delivery does it with him. 

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Healix

Cigna

Aviva Health

Aetna

Vitality Health

AXA PPP Healthcare

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Cardiovascular Disease

Professor Amitava Banerjee was incredibly impressive. He was very professional and very personable at the same time during every interaction. While obviously being extremely knowledgable, which in itself was very reassuring, he was a very calm, genuine and personable; both a great doctor and a great person. He was always very attentive, never rushed and focused on the patient as an individual. During a very stressful period his knowledge, experience and manner were invaluable. We wish more were like him. 

Heart rate variability (HRV) has long been measured by doctors. But now - using just a shop-bought fitness tracker - adults could check it themselves to see how they respond to hidden stress triggers.

How checking your pulse every day could beat stress and boost health

Identifying clinically relevant subtypes in complex conditions such as CKD could inform targeted management, healthcare resource utilization and future clinical trials, and our methods using machine learning in electronic health records are transferable to other disease areas.

Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals

Long COVID has been defined by World Health Organization as “continuation or development
of new symptoms 3 months after the initial SARS-CoV-2 infection, with these symptoms
lasting for at least 2 months with no other explanation”. Cardiovascular disease is
implicated as a risk factor, concomitant condition and consequence of Long COVID.
As well as heterogeneity in definition, presentation and likely underlying pathophysiology
of Long COVID, disparities by social determinants of health, extensively studied and
described in cardiovascular disease, have been observed in three ways.

Disparities by social determinants of health: links between Long COVID and cardiovascular disease.

University College London researchers used machine learning — a type of artificial intelligence — to pinpoint five types of heart failure, with a goal of predicting the prognosis for them.

AI identified these 5 types of heart failure in new study: 'Interesting to differentiate'

To determine the prevalence of organ impairment in long COVID patients at 6 and 12 months after initial symptoms and to explore links to clinical presentation.Prospective cohort study.Individuals.In individuals recovered from acute COVID-19, we assessed ...

Multi-organ impairment and long COVID: a 1-year prospective, longitudinal cohort study

Background Heart failure (HF), type 2 diabetes (T2D) and chronic kidney disease (CKD) commonly coexist. We studied characteristics, prognosis and healthcare utilisation of individuals with two of these conditions.

Methods We performed a retrospective, population-based linked electronic health records study from 1998 to 2020 in England to identify individuals diagnosed with two of: HF, T2D or CKD. We described cohort characteristics at time of second diagnosis and estimated risk of developing the third condition and mortality using Kaplan-Meier and Cox regression models. We also estimated rates of healthcare utilisation in primary care and hospital settings in follow-up.

Findings We identified cohorts of 64 226 with CKD and HF, 82 431 with CKD and T2D, and 13 872 with HF and T2D. Compared with CKD and T2D, those with CKD and HF and HF and T2D had more severe risk factor profile. At 5 years, incidence of the third condition and all-cause mortality occurred in 37% (95% CI: 35.9%, 38.1%%) and 31.3% (30.4%, 32.3%) in HF+T2D, 8.7% (8.4%, 9.0%) and 51.6% (51.1%, 52.1%) in HF+CKD, and 6.8% (6.6%, 7.0%) and 17.9% (17.6%, 18.2%) in CKD+T2D, respectively. In each of the three multimorbid groups, the order of the first two diagnoses was also associated with prognosis. In multivariable analyses, we identified risk factors for developing the third condition and mortality, such as age, sex, medical history and the order of disease diagnosis. Inpatient and outpatient healthcare utilisation rates were highest in CKD and HF, and lowest in CKD and T2D.

Interpretation HF, CKD and T2D carry significant mortality and healthcare burden in combination. Compared with other disease pairs, individuals with CKD and HF had the most severe risk factor profile, prognosis and healthcare utilisation. Service planning, policy and prevention must take into account and monitor data across conditions.

All data relevant to the study are included in the article or uploaded as supplemental information. Due to CPRD licence restrictions, no further data sharing is available.

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

AbstractAims. The risk and burden of cardiovascular disease (CVD) are higher in homeless than in housed individuals but population-based analyses are lacking. T

Prevalence, incidence, and outcomes across cardiovascular diseases in homeless individuals using national linked electronic health records

Background Long COVID is associated with multiple symptoms and impairment in multiple organs. Cross-sectional studies have reported cardiac impairment to varying degrees by varying methodologies. Using cardiac MR (CMR), we investigated a 12-month trajectory of abnormalities in Long COVID.

Objectives To investigate cardiac abnormalities 1-year post-SARS-CoV-2 infection.

Methods 534 individuals with Long COVID underwent CMR (T1/T2 mapping, cardiac mass, volumes, function and strain) and multiorgan MRI at 6 months (IQR 4.3–7.3) since first post-COVID-19 symptoms. 330 were rescanned at 12.6 (IQR 11.4–14.2) months if abnormal baseline findings were reported. Symptoms, questionnaires and blood samples were collected at both time points. CMR abnormalities were defined as ≥1 of low left or right ventricular ejection fraction (LVEF), high left or right ventricular end diastolic volume, low 3D left ventricular global longitudinal strain (GLS), or elevated native T1 in ≥3 cardiac segments. Significant change over time was reported by comparison with 92 healthy controls.

Results Technical success of multiorgan and CMR assessment in non-acute settings was 99.1% and 99.6% at baseline, and 98.3% and 98.8% at follow-up. Of individuals with Long COVID, 102/534 (19%) had CMR abnormalities at baseline; 71/102 had complete paired data at 12 months. Of those, 58% presented with ongoing CMR abnormalities at 12 months. High sensitivity cardiac troponin I and B-type natriuretic peptide were not predictive of CMR findings, symptoms or clinical outcomes. At baseline, low LVEF was associated with persistent CMR abnormality, abnormal GLS associated with low quality of life and abnormal T1 in at least three segments was associated with better clinical outcomes at 12 months.

Conclusion CMR abnormalities (left entricular or right ventricular dysfunction/dilatation and/or abnormal T1mapping), occurred in one in five individuals with Long COVID at 6 months, persisting in over half of those at 12 months. Cardiac-related blood biomarkers could not identify CMR abnormalities in Long COVID.

Trial registration number [NCT04369807][1].

All data relevant to the study are included in the article or uploaded as online supplemental information.

 [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT04369807&atom=%2Fopenhrt%2F10%2F1%2Fe002241.atom

Cardiac abnormalities in Long COVID 1-year post-SARS-CoV-2 infection

Experts believe higher deaths from heart complaints and diabetes mean the indirect effects of the pandemic will be greater than Covid itself

Rising non-Covid excess deaths reveal the disastrous legacy of the pandemic

The present study highlights increasing emergency cardiovascular admissions during the pandemic, in the context of a substantial and sustained reduction in elective admissions and procedures. This is likely to increase further the demands on cardiovascular services over the coming years.

Effects of the COVID-19 pandemic on secondary care for cardiovascular disease in the UK: an electronic health record analysis across three countries

Our analyses support coordinated use of large-scale, linked electronic health records to identify and validate disease subtypes with relevance for clinical risk prediction, patient selection for trials, and future genetic research.

Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study

Modelling of the effect of Covid-19 on those with underlying health risks may have missed a large proportion of the population, warn researchers. My team led research which helped to trigger the first UK lockdown in the first wave of the COVID-19 pandemic

UK not doing enough and coronavirus could kill 70,000 without tougher measures, scientists warn

To use national, pre- and post-pandemic electronic health records (EHR) to
develop and validate a scenario-based model incorporating baseline mortality
risk, infection rate (IR) and relative risk (RR) of death for prediction of
excess deaths.An EHR-based, ...

Using national electronic health records for pandemic preparedness:
validation of a parsimonious model for predicting excess deaths among those with
COVID-19–a data-driven retrospective cohort study

About 20% of UK population vulnerable but not included in government’s shielding advice

Millions with health conditions at risk from Covid-19 'if forced back to work'

Cardiovascular disease is both a risk factor and potential outcome of the direct, indirect and long-term effects of COVID-19. A recent analysis in &gt;150,000 survivors of COVID-19 demonstrates an increased 1-year risk of numerous cardiovascular diseases. Preventing and managing this new disease burden presents challenges to health systems and requires a learning health system approach.

Long COVID and cardiovascular disease: a learning health system approach - Nature Reviews Cardiology

Researchers are especially concerned about the impact of long COVID on people's general quality of life - notably among healthcare workers, whose rate of absence has increased as a result of their symptoms.

Most long COVID patients have organ damage a year later, study finds

Obesity is a known risk factor for severe COVID-19 disease. Here, the authors use linked Census, electronic health records, and mortality data for 12.6 million adults in England and show that the association between COVID-19 mortality and body mass index is stronger in ethnic minority groups.

A population-based cohort study of obesity, ethnicity and COVID-19 mortality in 12.6 million adults in England - Nature Communications

People say their lives have been ruined by long Covid, amid fears the number of sufferers is rising.

Long Covid: 'I have to choose between walking and talking'

A recent study has found that organ damage persists in long Covid patients, even in those initially not severely affected, raising serious concerns about the long-term health impacts of the virus

'Silent' organ damage found in over half of long Covid patients, study warns

Experts have dismissed anti-vax conspiracy theories linking the Covid jabs with high-profile athletes suffering cardiac emergencies.

What IS the truth about Covid vaccines and myocarditis?

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.

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 Medicine

In the first wave of the COVID pandemic, we provided 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.

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

Background Despite simpler regimens than vitamin K antagonists (VKAs) for stroke prevention in atrial fibrillation (AF), adherence (taking drugs as prescribed) and persistence (continuation of drugs) to direct oral anticoagulants are suboptimal, yet understudied in electronic health records (EHRs).

Objective We investigated (1) time trends at individual and system levels, and (2) the risk factors for and associations between adherence and persistence.

Methods In UK primary care EHR (The Health Information Network 2011–2016), we investigated adherence and persistence at 1 year for oral anticoagulants (OACs) in adults with incident AF. Baseline characteristics were analysed by OAC and adherence/persistence status. Risk factors for non-adherence and non-persistence were assessed using Cox and logistic regression. Patterns of adherence and persistence were analysed.

Results Among 36 652 individuals with incident AF, cardiovascular comorbidities (median CHA2DS2VASc[Congestive heart failure, Hypertension, Age≥75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category] 3) and polypharmacy (median number of drugs 6) were common. Adherence was 55.2% (95% CI 54.6 to 55.7), 51.2% (95% CI 50.6 to 51.8), 66.5% (95% CI 63.7 to 69.2), 63.1% (95% CI 61.8 to 64.4) and 64.7% (95% CI 63.2 to 66.1) for all OACs, VKA, dabigatran, rivaroxaban and apixaban. One-year persistence was 65.9% (95% CI 65.4 to 66.5), 63.4% (95% CI 62.8 to 64.0), 61.4% (95% CI 58.3 to 64.2), 72.3% (95% CI 70.9 to 73.7) and 78.7% (95% CI 77.1 to 80.1) for all OACs, VKA, dabigatran, rivaroxaban and apixaban. Risk of non-adherence and non-persistence increased over time at individual and system levels. Increasing comorbidity was associated with reduced risk of non-adherence and non-persistence across all OACs. Overall rates of ‘primary non-adherence’ (stopping after first prescription), ‘non-adherent non-persistence’ and ‘persistent adherence’ were 3.5%, 26.5% and 40.2%, differing across OACs.

Conclusions Adherence and persistence to OACs are low at 1 year with heterogeneity across drugs and over time at individual and system levels. Better understanding of contributory factors will inform interventions to improve adherence and persistence across OACs in individuals and populations.

Adherence and persistence to direct oral anticoagulants in atrial fibrillation: a population-based study

Liver disease on cT1 MRI is associated with a high risk of CVD events, CVD-related hospitalization, and all-cause mortality.

The association between liver disease on cT1 and CVD is independent of liver function tests, fibrosis and metabolic risk.

Risk of CVD events is increased even in the early stages of chronic liver disease.

Liver disease is a significant risk factor for cardiovascular outcomes - A UK Biobank study

AbstractAims. To evaluate population-based electronic health record (EHR) definitions of atrial fibrillation (AF) and valvular heart disease (VHD) subtypes, tim

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis

Univeristy College London Hospital

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