Cargando…
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179175/ https://www.ncbi.nlm.nih.gov/pubmed/35690576 http://dx.doi.org/10.1016/S2589-7500(22)00091-7 |
_version_ | 1784723210640556032 |
---|---|
author | Thygesen, Johan H Tomlinson, Christopher Hollings, Sam Mizani, Mehrdad A Handy, Alex Akbari, Ashley Banerjee, Amitava Cooper, Jennifer Lai, Alvina G Li, Kezhi Mateen, Bilal A Sattar, Naveed Sofat, Reecha Torralbo, Ana Wu, Honghan Wood, Angela Sterne, Jonathan A C Pagel, Christina Whiteley, William N Sudlow, Cathie Hemingway, Harry Denaxas, Spiros |
author_facet | Thygesen, Johan H Tomlinson, Christopher Hollings, Sam Mizani, Mehrdad A Handy, Alex Akbari, Ashley Banerjee, Amitava Cooper, Jennifer Lai, Alvina G Li, Kezhi Mateen, Bilal A Sattar, Naveed Sofat, Reecha Torralbo, Ana Wu, Honghan Wood, Angela Sterne, Jonathan A C Pagel, Christina Whiteley, William N Sudlow, Cathie Hemingway, Harry Denaxas, Spiros |
author_sort | Thygesen, Johan H |
collection | PubMed |
description | BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK. |
format | Online Article Text |
id | pubmed-9179175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91791752022-06-10 COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records Thygesen, Johan H Tomlinson, Christopher Hollings, Sam Mizani, Mehrdad A Handy, Alex Akbari, Ashley Banerjee, Amitava Cooper, Jennifer Lai, Alvina G Li, Kezhi Mateen, Bilal A Sattar, Naveed Sofat, Reecha Torralbo, Ana Wu, Honghan Wood, Angela Sterne, Jonathan A C Pagel, Christina Whiteley, William N Sudlow, Cathie Hemingway, Harry Denaxas, Spiros Lancet Digit Health Articles BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK. The Author(s). Published by Elsevier Ltd. 2022-07 2022-06-09 /pmc/articles/PMC9179175/ /pubmed/35690576 http://dx.doi.org/10.1016/S2589-7500(22)00091-7 Text en © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Articles Thygesen, Johan H Tomlinson, Christopher Hollings, Sam Mizani, Mehrdad A Handy, Alex Akbari, Ashley Banerjee, Amitava Cooper, Jennifer Lai, Alvina G Li, Kezhi Mateen, Bilal A Sattar, Naveed Sofat, Reecha Torralbo, Ana Wu, Honghan Wood, Angela Sterne, Jonathan A C Pagel, Christina Whiteley, William N Sudlow, Cathie Hemingway, Harry Denaxas, Spiros COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records |
title | COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records |
title_full | COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records |
title_fullStr | COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records |
title_full_unstemmed | COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records |
title_short | COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records |
title_sort | covid-19 trajectories among 57 million adults in england: a cohort study using electronic health records |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179175/ https://www.ncbi.nlm.nih.gov/pubmed/35690576 http://dx.doi.org/10.1016/S2589-7500(22)00091-7 |
work_keys_str_mv | AT thygesenjohanh covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT tomlinsonchristopher covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT hollingssam covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT mizanimehrdada covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT handyalex covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT akbariashley covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT banerjeeamitava covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT cooperjennifer covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT laialvinag covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT likezhi covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT mateenbilala covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT sattarnaveed covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT sofatreecha covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT torralboana covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT wuhonghan covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT woodangela covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT sternejonathanac covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT pagelchristina covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT whiteleywilliamn covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT sudlowcathie covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT hemingwayharry covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT denaxasspiros covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords AT covid19trajectoriesamong57millionadultsinenglandacohortstudyusingelectronichealthrecords |