A retrospective cohort study predicting and validating impact of the COVID-19 pandemic in individuals with chronic kidney disease

Abstract

Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe COVID-19, but analyses across CKD stages, and comorbidities are lacking. In prevalent and incident CKD, we investigated comorbidities, baseline risk, COVID-19 incidence, and predicted versus observed one-year excess death. In a national dataset (NHS Digital Trusted Research Environment [NHSD TRE]) for England encompassing 56 million individuals), we conducted a retrospective cohort study (March 2020 to March 2021) for prevalence of comorbidities by incident and prevalent CKD, SARS-CoV-2 infection and mortality. Baseline mortality risk, incidence and outcome of infection by comorbidities, controlling for age, sex and vaccination were assessed. Observed versus predicted one-year mortality at varying population infection rates and pandemic-related relative risks using our published model in pre-pandemic CKD cohorts (NHSD TRE and Clinical Practice Research Datalink [CPRD]) were compared. Among individuals with CKD (prevalent:1,934,585, incident:144,969), comorbidities were common (73.5% and 71.2% with one or more condition[s] in respective data sets, and 13.2% and 11.2% with three or more conditions, in prevalent and incident CKD), and associated with SARS-CoV-2 infection, particularly dialysis/transplantation (odds ratio 2.08, 95% confidence interval 2.04-2.13) and heart failure (1.73, 1.71-1.76), but not cancer (1.01, 1.01-1.04). One-year all-cause mortality varied by age, sex, multi-morbidity and CKD stage. Compared with 34,265 observed excess deaths, in the NHSD-TRE and CPRD databases respectively, we predicted 28,746 and 24,546 deaths (infection rates 10% and relative risks 3.0), and 23,754 and 20,283 deaths (observed infection rates 6.7% and relative risks 3.7). Thus, in this largest, national-level study, individuals with CKD have a high burden of comorbidities and multi-morbidity, and high risk of pre-pandemic and pandemic mortality. Hence, treatment of comorbidities, non-pharmaceutical measures, and vaccination are priorities for people with CKD and management of long-term conditions is important during and beyond the pandemic. Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe COVID-19, but analyses across CKD stages, and comorbidities are lacking. In prevalent and incident CKD, we investigated comorbidities, baseline risk, COVID-19 incidence, and predicted versus observed one-year excess death. In a national dataset (NHS Digital Trusted Research Environment [NHSD TRE]) for England encompassing 56 million individuals), we conducted a retrospective cohort study (March 2020 to March 2021) for prevalence of comorbidities by incident and prevalent CKD, SARS-CoV-2 infection and mortality. Baseline mortality risk, incidence and outcome of infection by comorbidities, controlling for age, sex and vaccination were assessed. Observed versus predicted one-year mortality at varying population infection rates and pandemic-related relative risks using our published model in pre-pandemic CKD cohorts (NHSD TRE and Clinical Practice Research Datalink [CPRD]) were compared. Among individuals with CKD (prevalent:1,934,585, incident:144,969), comorbidities were common (73.5% and 71.2% with one or more condition[s] in respective data sets, and 13.2% and 11.2% with three or more conditions, in prevalent and incident CKD), and associated with SARS-CoV-2 infection, particularly dialysis/transplantation (odds ratio 2.08, 95% confidence interval 2.04-2.13) and heart failure (1.73, 1.71-1.76), but not cancer (1.01, 1.01-1.04). One-year all-cause mortality varied by age, sex, multi-morbidity and CKD stage. Compared with 34,265 observed excess deaths, in the NHSD-TRE and CPRD databases respectively, we predicted 28,746 and 24,546 deaths (infection rates 10% and relative risks 3.0), and 23,754 and 20,283 deaths (observed infection rates 6.7% and relative risks 3.7). Thus, in this largest, national-level study, individuals with CKD have a high burden of comorbidities and multi-morbidity, and high risk of pre-pandemic and pandemic mortality. Hence, treatment of comorbidities, non-pharmaceutical measures, and vaccination are priorities for people with CKD and management of long-term conditions is important during and beyond the pandemic. Chronic kidney disease (CKD) carries major global disease burden, as a risk factor for morbidity and mortality, and as the end syndrome of underlying risk factors and diseases,1Cockwell P. Fisher L.-A. The global burden of chronic kidney disease.Lancet. 2020; 395: 662-664Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar,2Bikbov B. Purcell C.A. Levey A.S. et al.Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.Lancet. 2020; 395: 709-733Abstract Full Text Full Text PDF PubMed Scopus (2300) Google Scholar such as cancers3Stengel B. Chronic kidney disease and cancer: a troubling connection.J Nephrol. 2010; 23: 253PubMed Google Scholar and cardiovascular disease (CVD).4Gansevoort R.T. Correa-Rotter R. Hemmelgarn B.R. et al.Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention.Lancet. 2013; 382: 339-352Abstract Full Text Full Text PDF PubMed Scopus (1387) Google Scholar During the coronavirus disease 2019 (COVID-19) pandemic, CKD has been associated with poor prognosis.5Henry B.M. Lippi G. Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection.Int Urol Nephrol. 2020; 52: 1193-1194Crossref PubMed Scopus (359) Google Scholar,6Extance A. Covid-19 and long term conditions: what if you have cancer, diabetes, or chronic kidney disease?.BMJ. 2020; 368: m1174Crossref PubMed Scopus (53) Google Scholar Despite clinical and public health importance, CKD research to date in all stages, multimorbidity, or the general population7Saran R. Robinson B. Abbott K.C. et al.US renal data system 2017 annual data report: epidemiology of kidney disease in the United States.Am J Kidney Dis. 2018; 71: A7Abstract Full Text Full Text PDF PubMed Scopus (640) Google Scholar using national-level data has been limited.The pandemic has had both direct (through infection) and indirect (through changes in health services, economic upheaval, and behavioural factors8Yao H. Chen J.-H. Xu Y.-F. Patients with mental health disorders in the COVID-19 epidemic.Lancet Psychiatry. 2020; 7: e21Abstract Full Text Full Text PDF PubMed Scopus (915) Google Scholar,9Pereira-Sanchez V. Adiukwu F. El Hayek S. et al.COVID-19 effect on mental health: patients and workforce.Lancet Psychiatry. 2020; 7: e29-e30Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar) impacts. The direct impact in individuals with CKD and other underlying conditions is related to baseline risk, influenced by age, sex, multimorbidity, and other sociodemographic factors.10Anderson R.M. Hollingsworth T.D. Baggaley R.F. et al.COVID-19 spread in the UK: the end of the beginning?.Lancet. 2020; 396: 587-590Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar However, previous studies of COVID-19 in CKD have been small scale (12–1099 cases5Henry B.M. Lippi G. Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection.Int Urol Nephrol. 2020; 52: 1193-1194Crossref PubMed Scopus (359) Google Scholar), have mostly focused on end-stage CKD, and have ignored major comorbidities (either most common in CKD or related to risk of COVID-19 mortality). Few risk stratification tools are used in clinical practice for individuals with CKD or prediction of CKD, and those that include CKD usually do not consider different CKD stages. Better characterization of baseline risk in people with CKD may inform individual and population approaches to CKD prevention and treatment and integrated management of chronic diseases. CKD, already known to increase baseline risk of mortality, is associated with increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, disease severity, hospital11Mafham M.M. Spata E. Goldacre R. et al.COVID-19 pandemic and admission rates for and management of acute coronary syndromes in England.Lancet. 2020; 396: 381-389Abstract Full Text Full Text PDF PubMed Scopus (428) Google Scholar and intensive care admission,12Ghaem-Maghami S. Kehoe S. Burton M.K. CEU ID: 11123.Crisis. 2020; 10: 12Google Scholar and mortality. The role of other risk factors and underlying conditions in risk of COVID-19 in people with CKD requires more detailed investigation.13Reilev M. Kristensen K.B. Pottegaard A. et al.Characteristics and predictors of hospitalization and death in the first 9,519 cases with a positive RT-PCR test for SARS-CoV-2 in Denmark: a nationwide cohort.Int J Epidemiol. 2020; 49: 1468-1481Crossref PubMed Scopus (179) Google Scholar, 14Lai A.G. Pasea L. Banerjee A. et al.Estimating excess mortality in people with cancer and multimorbidity in the COVID-19 emergency.BMJ Open. 2020; 10e043828Google Scholar, 15Banerjee A. Pasea L. Harris S. et al.Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.Lancet. 2020; 395: 1715-1725Abstract Full Text Full Text PDF PubMed Scopus (322) Google Scholar There are clinical practice tools for risk stratification of COVID-19 patients in the community and hospitals, but inclusion of CKD is as a binary variable, and so the spectrum of risk faced by individuals with CKD has not been fully considered. Such analyses are important in risk communication to patients, public and health professionals, as well as policies to suppress infection rate (IR), such as social distancing and physical isolation. Meanwhile, more nuanced investigation of the risk associated with CKD may inform clinical care, COVID-19 vaccination strategies, as well as public health approaches to CKD after the pandemic.16NHS Digital. Shielded Patient List.https://digital.nhs.uk/coronavirus/shielded-patient-listDate accessed: April 7, 2022Google Scholar, 17NHS England. Who is at high risk from coronavirus (COVID-19)?.https://www.nhs.uk/conditions/coronavirus-covid-19/people-at-higher-risk/who-is-at-high-risk-from-coronavirus/#:∼:text=People%20who%20are%20still%20at,sickle%20cell%20diseaseDate accessed: April 7, 2022Google Scholar, 18NHS England. Implementing phase 3 of the NHS response to the COVID-19 pandemic.https://www.england.nhs.uk/publication/implementing-phase-3-of-the-nhs-response-to-the-covid-19-pandemic/Date accessed: April 7, 2022Google Scholar, 19NHS England. Implementing personalised stratified follow-up pathways.https://www.england.nhs.uk/publication/implementing-personalised-stratified-follow-up-pathways/Date accessed: April 7, 2022Google Scholar Using national, population-based electronic health records (EHRs), in individuals with prevalent and incident CKD, we investigated the following: (i) underlying conditions; (ii) mortality risk; (iii) incidence of SARS-CoV-2 infection, and (iv) prediction and validation of pandemic-related excess deaths. We conducted a retrospective, population-based cohort study using NHS Digital Trusted Research Environment for England (NHSD TRE)20Wood A. Denholm R. Hollings S. et al.Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource.BMJ. 2021; 373: n826Crossref PubMed Scopus (56) Google Scholar: a national database developed for pandemic-related research, linking primary care,21NHS Digital for England. General Practice Extraction Service (GPES) data for pandemic planning and research: a guide for analysts and users of the data. Updated June 8, 2022. Accessed April 7, 2022. https://digital.nhs.uk/coronavirus/gpes-data-for-pandemic-planning-and-research/guide-for-analysts-and-users-of-the-dataGoogle Scholar Hospital Episode Statistics Admitted Patient Care, COVID-19 trajectories,22Thygesen J.H. Tomlinson C. Hollings S. et al.on behalf of the Longitudinal Health and Wellbeing COVID-19 National Core Study and the CVD-COVID-UK/COVID-IMPACT ConsortiumCOVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records..Lancet Digit Health. 2022; 4: e542-e557Abstract Full Text Full Text PDF PubMed Scopus (19) Google Scholar COVID-19 vaccination, and mortality information from the Office for National Statistics Civil Registration of Deaths (Supplementary Figure S1). To investigate multimorbidity, baseline risk, incidence, and mortality, in individuals with CKD (aged ≥18 years), we defined “prevalent CKD” as ≥6 months before the onset of pandemic (March 1, 2020) without history of COVID-19, and “incident CKD” as new onset from March 1, 2020, to March 1, 2021, without history of COVID-19 before developing CKD. To predict 1-year COVID-19–related excess deaths based on prepandemic mortality risk, prevalent CKD at January 1, 2019, was defined using similar criteria. To show applicability of our methods to less complete, less up-to-date data sets, we also used Clinical Practice Research Datalink (CPRD) Gold data (as in our previous research15Banerjee A. Pasea L. Harris S. et al.Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.Lancet. 2020; 395: 1715-1725Abstract Full Text Full Text PDF PubMed Scopus (322) Google Scholar) to define prevalent CKD at April 6, 2014, by either diagnosed CKD or 2 estimated glomerular filtration rate measures (by Modification of Diet in Renal Disease-4 algorithm23David-Neto E. Triboni A.H.K. Ramos F. et al.Evaluation of MDRD 4, CKD-EPI, BIS-1, and modified Cockcroft–Gault equations to estimate glomerular filtration rate in the elderly renal-transplanted recipients.Clin Transplant. 2016; 30: 1558-1563Crossref PubMed Scopus (17) Google Scholar) ≥6 months before index date. Having an underlying condition, for all cohorts, was defined as having ≥6 months’ history of the condition: (i) before index date for prevalent CKD and (ii) before incidence date for incident CKD. Number of underlying conditions, where stated, was based on 6 conditions: chronic obstructive pulmonary disease, asthma, CVD, cancer, diabetes, and chronic liver disease. COVID-19 mortality was defined as mortality within 28 days of a positive test result. For SARS-CoV-2 incidence rate in prevalent CKD, disease-free time was estimated from earliest date before death or first-dose vaccination. Incident CKD was defined as SARS-CoV-2 positive ≥14 days after developing CKD. Disease-free time was measured from date of incident CKD. Crude incidence rate did not account for vaccination or other factors. Definitions of underlying conditions were derived from Health Data Research UK–Clinical diseAse research using LInked Bespoke studies and Electronic health Records (CALIBER), a comprehensive platform with validated definitions of underlying conditions.24Denaxas S. Gonzalez-Izquierdo A. Direk K. et al.UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER.J Am Med Informatics Assoc. 2019; 26: 1545-1559Crossref PubMed Scopus (98) Google Scholar Phenotyping was performed in primary care (General Practice Extraction Service [GPES] Data for Pandemic Planning and Research [GDPPR]) using Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) concepts and in secondary care (Hospital Episode Statistics Admitted Patient Care) using International Classification of Diseases, Tenth Revision (ICD-10), codes. For CKD phenotyping (including CKD stages, dialysis, and transplant), we extracted SNOMED CT concepts systematically using off-line NHS Digital SNOMED CT Browser (Supplementary Table S1). CVD was defined as a composite of stroke (nonspecified, ischemic, hemorrhagic, transient ischemic attack, or subarachnoid hemorrhagic), heart failure, arrhythmias, acute myocardial infarction, cardiomyopathy, atrial fibrillation, deep vein thrombosis, isolated calf vein thrombosis, and pulmonary embolism.25Banerjee A. Chen S. Pasea L. et al.Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic.Eur J Prev Cardiol. 2021; 28: 1599-1609Crossref PubMed Scopus (69) Google Scholar Obesity was defined as body mass index >40 kg/m2. Diabetes included all types of diabetes. Implementation of phenotypes is publicly available (https://github.com/BHFDSC/CCU003_03/tree/main/phenotypes). We estimated prevalence of underlying conditions in prevalent and incident CKD, stratifying by age, gender, CKD stage, or dialysis/transplantation. We compared prevalence of underlying conditions in infected versus noninfected for (i) all CKD patients and (ii) nonsurvival group, using odds ratio (Wald method) and Mantel-Haenszel χ2 test with 95% confidence intervals. With SARS-CoV-2 infection as exposure and 1-year all-cause mortality as outcome, we estimated adjusted relative risk (RR), stratified by underlying conditions, for both prevalent and incident CKD, using generalized linear model with Poisson distribution (log link) after adjusting for the following: (i) age and (ii) age and other potential cofounders by exact matching based on ≥1 vaccination dose, age groups (5-year intervals), and sex, assessing matching quality using distributional plots. To estimate overall effect of having an underlying condition, analyses were repeated with generalized linear model for each condition, reporting respective RRs (with “SARS-CoV-2 positive” as another potential confounder in exact matching). We estimated crude incidence rate of SARS-CoV-2 infection per 10,000 person-week, stratified by underlying conditions for incident and prevalent CKD. By Kaplan-Meier analyses, we estimated prepandemic baseline risk of 1-year all-cause mortality for prevalent CKD in NHSD TRE (2019) and CPRD cohorts (2014). We validated our recent model14Lai A.G. Pasea L. Banerjee A. et al.Estimating excess mortality in people with cancer and multimorbidity in the COVID-19 emergency.BMJ Open. 2020; 10e043828Google Scholar,15Banerjee A. Pasea L. Harris S. et al.Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.Lancet. 2020; 395: 1715-1725Abstract Full Text Full Text PDF PubMed Scopus (322) Google Scholar (to predict COVID-19–related excess death) using our risk estimates and applying 1-year population IR of 10%, and overall RR of mortality (set at 3) based on previous reports.15Banerjee A. Pasea L. Harris S. et al.Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.Lancet. 2020; 395: 1715-1725Abstract Full Text Full Text PDF PubMed Scopus (322) Google Scholar,26Office for National Statistics. Deaths registered weekly in England and Wales, provisional. Accessed June 28, 2022. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwalesGoogle Scholar We predicted total excess deaths by: (i) age groups and number of underlying conditions and (ii) underlying conditions, using assumed and observed IR and RR. The analysis was performed according to a prespecified analysis plan published on GitHub (https://github.com/BHFDSC/CCU003_01), including implementations and phenotypes. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. AD, MAM, and AB had full access to all the data in the study; and AB had final responsibility for the decision to submit for publication.

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