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Predicting and Validating Risk of Pre-Pandemic and Excess Mortality in Individuals With Chronic Kidney Disease

21 Pages Posted: 24 Nov 2021

See all articles by Muhammad Dashtban

Muhammad Dashtban

University College London - Institute of Health Informatics

Mehrdad A. Mizani

University College London - Institute of Health Informatics

Spiros Denaxas

University College London - Institute of Health Informatics

Dorothea Nitsch

London School of Hygiene & Tropical Medicine

Jennifer Quint

Imperial College London - National Heart and Lung Institute

Richard Corbett

Imperial College London

Jil Billy Mamza

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs

Tamsin Morris

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs

Mamas Mamas

Keele University

Deborah A. Lawlor

Bristol NIHR Biomedical Research Centre

Kamlesh Khunti

University of Leicester - Leicester Diabetes Centre

CVD-COVID consortium

Cathie Sudlow

Health Data Research UK - BHF Data Science Centre

Harry Hemingway

University College London - Institute of Health Informatics

Amitava Banerjee

University College London - Institute of Health Informatics

More...

Abstract

Background: Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe coronavirus (COVID-19) infection, but analyses of risk across different CKD stages, multimorbidity and demographic factors in population-based data are lacking. In people with CKD, we investigated comorbidities, and 1-year pre- and post-COVID-19 mortality.

Methods: In linked primary and secondary care electronic health records (CPRD data) for 3,862,012 individuals aged ≥ 30 years registered with a practice between 1997 and 2017, we identified individuals with CKD. We analysed prevalence of common comorbidities across incident and prevalent CKD, CKD stages, ethnic groups, sex, and age, using validated, openly available phenotypes. Using our published model, we estimated 1-year mortality at baseline and during the COVID-19 pandemic at different levels of population infection rate (IR) and relative risk (RR) of mortality associated with the pandemic. We used CPRD data for model development and contemporary English population during pandemic (NHS Digital Trusted Research Environment (TRE) for England: n=54 million (CKD: 2.3 million) from 1 March 2020 until 1 March 2021) for validation.

Findings: We identified 294,381 (mean age 72.5 years; and female: 59%) and 55,691 (mean 74.8 years and female: 62%) individuals with incident and prevalent CKD, respectively. Co- and multi-morbidity were more common in prevalent than incident CKD. Among individuals with incident CKD, 64% had ≥1 other moderate- or high-risk condition for COVID-19 mortality. In incident CKD, the pre-pandemic 1-year mortality increased with age (e.g., 1.4% and 12.5% with 1 comorbidity, and <50 years and >80 years respectively), stage of CKD (e.g., 2.7% and 28.8% with 2 comorbidities, and stage 1 and stage 5 CKD, respectively) and number of underlying conditions (e.g. 0.8% and 6.4% in <50 years, and 8.1% and 20.1% in >80 years for 0 and 3+ conditions, respectively). At IR 10%, we predicted 31003 and 46505 (at RR 2 and 3) excess deaths over 1 year in individuals with CKD. Observed excess deaths, IR and RR were 46,473, 6.55 and 4.65, respectively . Our validation results indicate the potential of predicting direct impact of a pandemic using pre-pandemic, large-scale EHR data.

Interpretation: Individuals with CKD have high burden of comorbidities and multimorbidity, high risk of pre-pandemic mortality and predictable high risk of mortality during the pandemic, signalling prioritisation for treatment of underlying disease, non-pharmaceutical measures, and vaccination.

Funding Information: This study was funded by AstraZeneca UK Ltd. The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded codevelopment (with NHS Digital) of the trusted research environment, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK data and connectivity component of the UK governments’ chief scientific adviser’s national core studies programme to coordinate national covid-19 priority research. Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians.

Declaration of Interests: DN is Director of Informatics Research for the UK Kidney Association and on the steering group for two Glaxo-SmithKline-funded studies in Sub Saharan Africa, unrelated to this research. All other authors declare no competing interests.JQ has received grants from Asthma UK, AstraZeneca, British Lung Foundation, Bayer, Boehringer Ingelheim, Chiesi, GSK, IQVIA, MRC
and The Health Foundation, and personal fees for advisory board participation or speaking fees from AstraZeneca, Bayer, Boehringer Ingelheim and GlaxoSmithKline. JBM and TM are employed by AstraZeneca UK Ltd, a biopharmaceutical company. DAL has received funding from Wellcome, the European Research Council (ERC Advanced grant and a Horizon 2020 grant), US National Institute of Health, Diabetes UK, Roche Diagnostics and Medtronic Ltd for research unrelated to that presented here. KK is director of the University of Leicester Centre for Black Minority Ethnic Health, trustee of the South Asian Health Foundation, and chair of the ethnicity subgroup of the UK Scientific Advisory Group for Emergencies (SAGE), he has acted as a consultant, speaker or received grants for investigator-initiated studies for AstraZeneca, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly and Merck Sharp & Dohme, Boehringer Ingelheim, Bayer, Berlin-Chemie/Menarini Group, Janssen and Napp. AB is supported by research funding from the National Institute for Health Research (NIHR), British Medical Association, AstraZeneca, and UK Research and Innovation, and Trustee of the South Asian Health Foundation. HH is a National Institute for Health Research (NIHR) Senior Investigator. FA and HH are funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. HH work is supported by: 1. Health Data Research UK (grant No. LOND1), which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust. AB and HH are part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA; it is chaired, by DE Grobbee and SD Anker, partnering with 20
academic and industry partners and ESC. Other authors report no conflicts of interest.

Ethics Approval Statement: Approval for the study was granted by the Independent Scientific Advisory Committee (20_074R) of the Medicines and Healthcare products Regulatory Agency in the UK in accordance with the Declaration of Helsinki. The North East-Newcastle and North Tyneside 2
research ethics committee provided ethical approval for the CVD-COVID-UK/COVID-IMPACT research programme (REC No 20/NE/0161).

Keywords: chronic kidney disease, multimorbidity, excess mortality, coronavirus, risk prediction

Suggested Citation

Dashtban, Muhammad and Mizani, Mehrdad A. and Denaxas, Spiros and Nitsch, Dorothea and Quint, Jennifer and Corbett, Richard and Mamza, Jil Billy and Morris, Tamsin and Mamas, Mamas and Lawlor, Deborah A. and Khunti, Kamlesh and consortium, CVD-COVID and Sudlow, Cathie and Hemingway, Harry and Banerjee, Amitava, Predicting and Validating Risk of Pre-Pandemic and Excess Mortality in Individuals With Chronic Kidney Disease. Available at SSRN: https://ssrn.com/abstract=3970707 or http://dx.doi.org/10.2139/ssrn.3970707

Muhammad Dashtban

University College London - Institute of Health Informatics ( email )

United Kingdom

Mehrdad A. Mizani

University College London - Institute of Health Informatics ( email )

United Kingdom

Spiros Denaxas

University College London - Institute of Health Informatics ( email )

United Kingdom

Dorothea Nitsch

London School of Hygiene & Tropical Medicine ( email )

Keppel Street
London, WC1E 7HT
United Kingdom

Jennifer Quint

Imperial College London - National Heart and Lung Institute ( email )

Guy Scadding Building, Cale Street
London, SW3 6LY
United Kingdom

Richard Corbett

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, SW7 2AZ
United Kingdom

Jil Billy Mamza

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs ( email )

Cambridge
United Kingdom

Tamsin Morris

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs ( email )

Cambridge
United Kingdom

Mamas Mamas

Keele University ( email )

Deborah A. Lawlor

Bristol NIHR Biomedical Research Centre ( email )

Kamlesh Khunti

University of Leicester - Leicester Diabetes Centre ( email )

Leicester
United Kingdom

Cathie Sudlow

Health Data Research UK - BHF Data Science Centre ( email )

London
United Kingdom

Harry Hemingway

University College London - Institute of Health Informatics ( email )

United Kingdom

Amitava Banerjee (Contact Author)

University College London - Institute of Health Informatics

United Kingdom

No contact information is available for CVD-COVID Consortium

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