Study: COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. Image Credit: whiteMocca / Shutterstock

Population-level big data reveals COVID-19 phenotypes

A recent study published in Digital Health The Lancet The journal assessed trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in England using electronic health records (EHRs).

Study: COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. Image Credit: whiteMocca/Shutterstock

Background

SARS-CoV-2 pandemic mitigation measures are based on updatable estimates of the trajectories, progression and onset of the disease CoV 2019 (COVID-19).

In the present study, the authors searched PubMed for articles using the search terms SARS-CoV-2 or COVID-19, electronic health records or EHR, and severity, without language constraints, since the inception of the database. of data through October 14, 2021. They found that several surveys assessed elements that influence the severity of SARS-CoV-2 infection and predicted outcomes for hospitalized COVID-19 patients.

However, most studies have focused on isolated aspects of the health system, such as primary/secondary care only, have been conducted in sub-populations, for example, patients admitted to hospital, have had small sample sizes and frequently used dichotomized outcomes such as hospital admission or mortality that do not reflect the full spectrum of disease. Additionally, the team found no surveys that comprehensively assess the severity of SARS-CoV-2 infection throughout pandemic waves and based on patient trajectories and vaccination status.

About the study

In the current cohort study, scientists used an extensible framework to describe and validate 10 characteristics of SARS-CoV-2 infection from nationwide connected EHRs to recognize and characterize trajectories of COVID- 19. They tapped eight interconnected National Health Service (NHS) data sources for people alive on January 23, 2020 in England.

These databases linked data on laboratory testing for COVID-19, primary care, hospitalization (intensive care unit (ICU) admission and ventilator support), and registered deaths. Information on SARS-CoV-2 vaccination, testing, primary/secondary care data, and death records were collected through November 30, 2021.

The team described 10 COVID-19 phenotypes that indicate clinically significant phases of disease severity and fall into five categories: SARS-CoV-2-positive test, hospitalization, primary care diagnosis, ventilation strategy consisting of four phenotypes and mortality comprising three phenotypes. They created patient trajectories showing the duration and frequency of transitions between phenotypes. COVID-19 vaccination status and pandemic waves were used to stratify the analyses.

Of note, the researchers aimed to create an updatable framework capable of reconstructing an individual’s COVID-19 trajectories across various states of severity, providing crucial insights into the effectiveness of booster vaccine doses, the effect of new viral variants, post-exposure prophylaxis and new treatment options using data linkage.

Results

The results of the study illustrated that in the current cohort of 57,032,174 people, 13,990,423 incidences of COVID-19 were found in 7,244,925 people, resulting in a rate of SARS-CoV infection -2 by 12.7% over the research period. Among 7,244,925 subjects, 460,737 people or 64% were hospitalized and 158,020 people or 2.2% died. Of the 460,737 people hospitalized, 48,847, or 10.6%, required intensive care admission, 69,090, or 15%, received non-invasive ventilation and 25,928, or 5.6% , invasive ventilation.

Mortality was higher in the initial wave of COVID-19 with 23,485, or 30.4%, of 77,202 SARS-CoV-2 patients than in the second pandemic wave with 44,220 (23.1%) of 191,528 people infected with the virus out of 384,135 hospitalized but not hospitalized patients. requiring ventilation. Patients admitted to intensive care, on the other hand, had no difference in mortality.

​​​​​​Framework describing the ten COVID-19 phenotypes and severity categories, produced using seven linked data sources to assess the difference between COVID-19 waves and vaccination statusFramework describing the ten COVID-19 phenotypes and severity categories, produced using seven linked data sources to assess the difference between COVID-19 waves and vaccination status

During the first pandemic wave, COVID-19 patients who required ventilatory support outside the ICU had the highest mortality rate, with 2,569 or 50.7% of 5,063 patients dying. Additionally, 15,486 i.e. 9.8% of the 158,020 COVID-19 related deaths occurred within 28 days of the initial COVID-19 event without a diagnosis of SARS-CoV infection. -2 on the death certificate. Additionally, 10,884, i.e. 6.9%, of the 158,020 deaths were recognized from death data, without any previous COVID-19 phenotypic information. The authors noted that the second wave of the COVID-19 pandemic had longer patient trajectories than the first wave.

COVID-19 trajectory networks, The size of the circles represents the number of individuals with this event compared to the total study population.  The numbers on the arrows show the proportion of individuals who switched to each phenotype (compared to the number of individuals in this COVID-19 wave).  Numbers in brackets indicate the median number of days between events for all individuals with this transition.  Median days between individuals who were unaffected (i.e. no recorded COVID-19 phenotype) and other severity phenotypes are not shown as they were not directly comparable across waves, in because of the difference in duration of the two periods.  Thick arrows represent transitions that occurred in 0.1% or more of individuals.  The thin black arrows represent transitions that occurred in 0.01% or more individuals.  All transitions occurring in less than 0.01% of individuals are not shown.  All included individuals were alive and had no prior recorded COVID-19 events prior to the start date of the specified waves.

COVID-19 trajectory networks, The size of the circles represents the number of individuals with this event compared to the total study population. The numbers on the arrows show the proportion of individuals who switched to each phenotype (compared to the number of individuals in this COVID-19 wave). Numbers in brackets indicate the median number of days between events for all individuals with this transition. Median days between individuals who were unaffected (i.e. no recorded COVID-19 phenotype) and other severity phenotypes are not shown as they were not directly comparable across waves, in because of the difference in duration of the two periods. Thick arrows represent transitions that occurred in 0.1% or more of individuals. The thin black arrows represent transitions that occurred in 0.01% or more individuals. All transitions occurring in less than 0.01% of individuals are not shown. All included individuals were alive and had no prior recorded COVID-19 events prior to the start date of the specified waves.

conclusion

According to the authors, this was the first research that uses national data to provide a comprehensive overview of COVID-19 throughout pandemic waves, with a focus on vaccination, patient trajectories and severity. The team documented the frequency of comorbidities, key demographic variables, the implications of the two main waves of SARS-CoV-2 in England, and the influence of vaccination on the severity of COVID-19 in over 57 million people registered with a GP and living in England using information from nationally linked EHRs.

Investigators also identified and defined patient trajectory pathways, which describe key transition routes for COVID-19 patients through the healthcare system. They also provide COVID-19 phenotyping methods that are reproducible and describe clinically significant phases of disease severity, such as primary care diagnosis, positive tests, hospitalization, death, and ventilator support.

Overall, current analyzes reveal the vast range of COVID-19 trajectories, as seen by disparities in survival, incidence, and clinical course. Using multiple EHR sources, the team created a modular analytical framework that could track the influence of the SARS-CoV-2 pandemic and establish evidence of clinical and policy importance.

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