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Convalescence Long-COVID Study



‘CONVALESCENCE embraces the recovery of a multitude of physiological processes in an individual who has suffered from trauma, disease, or an operation. These various physiological processes recover at different rates and the recovery of many is not manifested by any marked objective or subjective signs. There finally comes a time when the patient is conscious of his return to normal and is again ready for work. This transformation from illness to health is often dramatic and is frequently not accompanied by any sign which the physician can detect. The study of convalescence is concerned with the analysis of these disturbances.’


(From CONVALESCENCE: A STUDY IN THE PHYSIOLOGICAL RECOVERY OF NITROGEN METABOLISM AND LIVER FUNCTION).


Convalescence is a NIHR-UKRI funded study (3 years from March 2021) that will use data from established population cohorts and national anonymised electronic health records to achieve the following aims:

• How can we best define long-COVID?

• What are its risk factors and mechanistic pathways?

• What are the consequences for physical and mental health, and for work, education and social and familial relations?

• Can we enhance diagnosis and management through GP records?

Convalescence is a multi-disciplinary collaboration between many universities and the National Institute for Health and Care Excellence (NICE). There are 4 Research Themes:


1. Long-COVID definition


What?


We will characterise how long-COVID presents in people recovering from COVID-19, looking particularly for whether there are groups of symptoms that appear commonly in some patients.


Why?


Long-COVID is an emerging new condition, which means that we don’t yet know much about what symptoms and diagnoses people have, how frequently they occur, and whether there are groups of symptoms and diagnoses that occur in the same patients. Systematically describing these things will allow clinicians to more effectively diagnose and treat patients with long-COVID, as well as enable further research into the prevalence, risk factors and outcomes of these patients.


How?


We will use structured and free text data within routinely gathered electronic health record data, alongside questionnaires from population cohort members who self-identify with long-COVID, to measure the frequency and clustering of long-COVID symptoms and diagnoses.


2. Long-COVID risk

What?


We will identify predisposing risk factors for long-COVID, addressing sociodemographic factors and pre-pandemic health, along with severity of initial COVID-19 illness.


Why?


Long-COVID likely represents a complex and diverse set of aetiological factors, not all of which may be shared between long-COVID patients or be common determinants of developing COVID-19. Identifying the distinct mechanisms responsible for individuals suffering prolonged symptoms will provide pathways to novel treatments and potentially patient stratification. It may also inform us of the members of society most vulnerable to long-COVID were they to be infected, and thus guide public health and vaccine prioritisation (in the remaining schedule and in future for the administration of boosters).


How?


Utilising rich antecedent data on individuals from before the pandemic, which have been recorded either routinely in EHRs or in decades of bespoke follow-ups in the cohort studies


3. Long-COVID outcomes


What?


The long-COVID Outcome research theme will examine and understand short term and long term physical health, mental health and social outcomes of COVID infection and the subsequent long-COVID period.


Why?


COVID has profound direct and indirect effects on society and human health. This work package will aim to understand these effects on human physical and mental health and the way patients interact with the healthcare system with the aim of designing interventions targeting populations most at need. Conversely, we use diverse data sources in order to identify and characterize factors influencing the recovery from long-COVID.


How?


This will be achieved by harnessing the breadth of national electronic health record data sources linked across care settings and the phenotypic depth of cohort studies.



4. Methods to improve Long-COVID diagnosis and management


What?


The methods to improve Long-COVID diagnosis and management research theme is a collaboration with the National Institute for Health and Care Excellence (NICE) NICE | The National Institute for Health and Care Excellence

On 18th December 2020, NICE published a guideline on managing the long-term effects of COVID-19 (guideline reference NG188) alongside new clinical case definitions. This guideline covers a pathway from identification to treatment, and in addition includes advice on organising services. The guideline is a ‘living guideline’, changing in response to new developments in evidence and the availability of additional data and information.


Why?


Providing rapid evidence through research programs such as Convalescence will enable NICE to update guidelines faster and provide more timely care recommendations. NICE is also interested in factors which affect the implementation of guidance, and how to overcome barriers and effectively implement guidance.


How?


Using primary care records within the OpenSAFELY platform, we will investigate compliance with the NICE guidance on diagnosis and management of the long-term effects of COVID-19. We will also explore whether this differs by demographic, health, socioeconomic and regional characteristics.


These will allow us to:

• Create an evidence-based definition of long-COVID sub-phenotypes

• Identify new and bespoke interventions to support people with long-COVID informed by understanding mechanisms of different long-COVID phenotypes

• Inform interventions at the time of infection, and assist in planning of health care services

• Improve care and support for people with long-COVID


Our research approach includes:

• Analysis of existing data in national anonymised electronic health records and longitudinal studies

• Parallel qualitative interviews of study participants

• Detailed phenotyping of long-COVID cases and controls from longitudinal studies via

- Non-invasive imaging of brain, heart, lungs & muscles

- Remote monitoring of mental and cognitive health

- Wearables to assess physical capability and sleep

• Pilot primary care record pop-up reminders for diagnosis and management in collaboration with NICE

• Participant and public involvement and engagement throughout the research


https://www.ucl.ac.uk/covid-19-longitudinal-health-wellbeing/convalescence-long-covid-study

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