Harmonization of epidemiology of acute kidney injury and acute kidney disease produces comparable findings across four geographic populations
Authors: Simon Sawhney, Samira Bell, Corri Black, Christian Fynbo Christiansen, Uffe Heide-Jørgensen, Simon Kok Jensen, Paul E. Ronksley, Zhi Tan, Marcello Tonelli, Heather Walker, Matthew T. James
Journal: Kidney International (2022)

There is substantial variability in the reported incidence and outcomes of acute kidney injury (AKI). The extent to which this is attributable to differences in source populations versus methodological differences between studies is uncertain. We used 4 population-based datasets from Canada, Denmark, and the United Kingdom to measure the annual incidence and prognosis of AKI and acute kidney disease (AKD), using a homogenous analytical approach that incorporated KDIGO creatinine-based definitions and subsets of the AKI/AKD criteria. The cohorts included 7 million adults ≥18 years of age between 2011 and 2014; median age 59-68 years, 51.9-54.4% female sex. Age- and sex-standardised incidence rates for AKI or AKD were similar between regions and years; range 134.3-162.4 events/10,000 person years. Among patients who met either KDIGO 48-hour or 7-day AKI creatinine criteria, the standardised 1-year mortality was similar (30.4%-38.5%) across the cohorts, which was comparable to standardised 1-year mortality among patients who met AKI/AKD criteria using a baseline creatinine within 8-90 days prior (32.0%-37.4%). Standardised 1-year mortality was lower (21.0%-25.5% across cohorts) among patients with AKI/AKD ascertained using a baseline creatinine >90 days prior. These findings illustrate that the incidence and prognosis of AKI and AKD based on KDIGO criteria are consistent across 3 high-income countries when capture of laboratory tests is complete, creatinine-based definitions are implemented consistently within but not beyond a 90-day period, and adjustment is made for population age and sex. These approaches should be consistently applied to improve the generalizability and comparability of AKI research and clinical reporting. There is substantial variability in the reported incidence and outcomes of acute kidney injury (AKI). The extent to which this is attributable to differences in source populations versus methodological differences between studies is uncertain. We used 4 population-based datasets from Canada, Denmark, and the United Kingdom to measure the annual incidence and prognosis of AKI and acute kidney disease (AKD), using a homogenous analytical approach that incorporated KDIGO creatinine-based definitions and subsets of the AKI/AKD criteria. The cohorts included 7 million adults ≥18 years of age between 2011 and 2014; median age 59-68 years, 51.9-54.4% female sex. Age- and sex-standardised incidence rates for AKI or AKD were similar between regions and years; range 134.3-162.4 events/10,000 person years. Among patients who met either KDIGO 48-hour or 7-day AKI creatinine criteria, the standardised 1-year mortality was similar (30.4%-38.5%) across the cohorts, which was comparable to standardised 1-year mortality among patients who met AKI/AKD criteria using a baseline creatinine within 8-90 days prior (32.0%-37.4%). Standardised 1-year mortality was lower (21.0%-25.5% across cohorts) among patients with AKI/AKD ascertained using a baseline creatinine >90 days prior. These findings illustrate that the incidence and prognosis of AKI and AKD based on KDIGO criteria are consistent across 3 high-income countries when capture of laboratory tests is complete, creatinine-based definitions are implemented consistently within but not beyond a 90-day period, and adjustment is made for population age and sex. These approaches should be consistently applied to improve the generalizability and comparability of AKI research and clinical reporting. Over the past decade, significant progress has been made in the understanding of the epidemiology of acute kidney injury (AKI), including its incidence and outcomes.1Susantitaphong P. Cruz D.N. Cerda J. et al.World incidence of AKI: a meta-analysis.Clin J Am Soc Nephrol. 2013; 8: 1482-1493Crossref PubMed Scopus (862) Google Scholar, 2See E.J. Jayasinghe K. Glassford N. et al.Long-term risk of adverse outcomes after acute kidney injury: a systematic review and meta-analysis of cohort studies using consensus definitions of exposure.Kidney Int. 2019; 95: 160-172Abstract Full Text Full Text PDF PubMed Scopus (201) Google Scholar, 3Hoste E.A.J. Kellum J.A. Selby N.M. et al.Global epidemiology and outcomes of acute kidney injury.Nat Rev Nephrol. 2018; 14: 607-625Crossref PubMed Scopus (479) Google Scholar, 4Levey A.S. James M.T. Acute kidney injury.Ann Intern Med. 2017; 167: ITC66-ITC80Crossref PubMed Scopus (150) Google Scholar An important facilitator of this process has been the uptake of consensus criteria for defining AKI, most recently using the Kidney Disease: Improving Global Outcomes (KDIGO) definitions.5Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work GroupKDIGO clinical practice guideline for acute kidney injury.Kidney Int Suppl. 2012; 2: 1-138Abstract Full Text Full Text PDF Scopus (2088) Google Scholar The KDIGO criteria have increased care provider awareness, research, and public appreciation of AKI as a global health problem. Consensus reports of the Acute Dialysis Quality Initiative (ADQI) have further led to recognition of the importance of acute kidney disease (AKD), based on changes in kidney function developing over 90 days, to bridge the conceptual models of AKI and chronic kidney disease (CKD).6Chawla L.S. Bellomo R. Bihorac A. et al.Acute kidney disease and renal recovery: consensus report of the acute disease quality initiative (ADQI) 16 workgroup.Nat Rev Nephrol. 2017; 13: 241-257Crossref PubMed Scopus (735) Google Scholar,7James M.T. Levey A.S. Tonelli M. et al.Incidence and prognosis of acute kidney diseases and disorders using an integrated approach to laboratory measurements in a universal health care system.JAMA Netw Open. 2019; 2e191795Crossref Scopus (57) Google Scholar Despite this progress, uncertainty remains about fundamental elements of AKI and AKD epidemiology. Several challenges to applying the KDIGO AKI and AKD criteria for epidemiologic research, and in clinical applications such as e-alert systems, are well recognized.8James M.T. Hobson C.E. Darmon M. et al.Applications for detection of acute kidney injury using electronic medical records and clinical information systems: workgroup statements from the 15th ADQI Consensus Conference.Can J Kidney Health Dis. 2016; 3: 1-9Crossref PubMed Scopus (48) Google Scholar,9Guthrie G. Guthrie B. Walker H. et al.Developing an AKI consensus definition for database research: findings from a scoping review and expert opinion using a Delphi process.Am J Kidney Dis. 2022; 79: 488-496Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar These include the following: the selection of timeframes and measures for establishing an individual's baseline kidney function; the question of whether small absolute changes in creatinine level (0.3 mg/dl) over 48 hours should be considered equivalent to larger relative changes (1.5-fold) over 7 days; and the implications of including changes in kidney function detected over 90 days, as represented by the AKD criteria. Meta-analysis of studies in which the KDIGO definition was applied demonstrated very high heterogeneity in estimates of the burden of AKI, with marked differences in incidence ranging from <10% to >40% across hospital-based cohorts,1Susantitaphong P. Cruz D.N. Cerda J. et al.World incidence of AKI: a meta-analysis.Clin J Am Soc Nephrol. 2013; 8: 1482-1493Crossref PubMed Scopus (862) Google Scholar and mortality rates for AKI based on the KDIGO definition that vary more than 3-fold across studies.1Susantitaphong P. Cruz D.N. Cerda J. et al.World incidence of AKI: a meta-analysis.Clin J Am Soc Nephrol. 2013; 8: 1482-1493Crossref PubMed Scopus (862) Google Scholar,2See E.J. Jayasinghe K. Glassford N. et al.Long-term risk of adverse outcomes after acute kidney injury: a systematic review and meta-analysis of cohort studies using consensus definitions of exposure.Kidney Int. 2019; 95: 160-172Abstract Full Text Full Text PDF PubMed Scopus (201) Google Scholar What remains unclear is the extent to which the variability in reported incidence and outcome of AKI relates to differences in the source populations of cohorts versus methodological differences among studies, including the application of KDIGO definitions. These uncertainties currently undermine the ability to make generalizable conclusions about the current clinical burden of AKI/AKD and to develop standardized recommendations about how AKI/AKD identification should be operationalized in research and clinical practice. A recent KDIGO controversies conference acknowledged these issues, calling for further work to reconcile these uncertainties and enhance our understanding of the epidemiology of AKD, including AKI.10Lameire N.H. Levin A. Kellum J.A. et al.Harmonizing acute and chronic kidney disease definition and classification: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference.Kidney Int. 2021; 100: 516-526Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar In this study, we used whole-population data covering 7 million residents in Canada, Denmark, and the UK to measure the incidence and prognosis of AKI and AKD using a homogenous analytical approach incorporating KDIGO definitions. We sought to determine whether harmonized analytical approaches would produce comparable findings for AKI incidence and mortality across 4 independent cohorts from high-income countries with universal access to care and complete population ascertainment of kidney function measurements. Complete population community and hospital laboratory data were extracted from a 5-year period (2010–2014) from 4 regions with a combined population of 7 million inhabitants: Alberta (Canada), northern Denmark, Grampian (UK), and Tayside (UK). These populations, served by socialized health systems, were selected for their ability to provide integrated data on isotope-dilution mass spectrometry (IDMS) calibrated creatinine measurements for all residents within their source population, irrespective of clinical setting (hospital inpatient, outpatient specialty, community). Waivers of consent were provided by research ethics boards for use of health data for each region. Use of Alberta data was approved by the Conjoint Health Research Ethics Board (CHREB) of the University of Calgary (ID# REB20-0970)—including waiver of consent for use of previously collected health data in accordance with Alberta Health Information Act. Use of data from northern Denmark (the north and central regions in Denmark) was reported to the Danish Data Protection Agency through registration at Aarhus University (record number 2016-051-000001/812). According to Danish legislation, no ethical approval was required. For Tayside, data provision and linkage were carried out by the University of Dundee Health Informatics Centre (HIC, https://www.dundee.ac.uk/hic), with analysis of anonymized data performed in an ISO27001 and Scottish Government accredited secure safe haven. HIC standard operating procedures have been reviewed and approved by the National Health Service East of Scotland Research Ethics Service, and consent for this study was obtained from the National Health Service Fife Caldicott Guardian. Use of Grampian unconsented, pseudonymized, routinely collected health data were provided by North West Research Ethics Committee (19/NW/0552), Grampian Caldicott Guardian, and National Health Service Research and Development. All cohorts have been used in previous epidemiologic research on kidney disease, with additional details provided in previous publications.11Hemmelgarn B.R. Clement F. Manns B.J. et al.Overview of the Alberta Kidney Disease Network.BMC Nephrol. 2009; 10: 30Crossref PubMed Scopus (184) Google Scholar, 12Vestergaard S.V. Christiansen C.F. Thomsen R.W. et al.Identification of patients with CKD in medical databases: a comparison of different algorithms.Clin J Am Soc Nephrol. 2021; 16: 543-551Crossref PubMed Scopus (18) Google Scholar, 13Graversen H.V. Norgaard M. Nitsch D. Christiansen C.F. Preadmission kidney function and risk of acute kidney injury in patients hospitalized with acute pyelonephritis: a Danish population-based cohort study.PLoS One. 2021; 16e0247687Crossref Scopus (2) Google Scholar, 14Slagelse C. Gammelager H. Iversen L.H. et al.Acute kidney injury and 1-year mortality after colorectal cancer surgery: a population-based cohort study.BMJ Open. 2019; 9e024817Google Scholar, 15Logan R. Davey P. De Souza N. et al.Assessing the accuracy of ICD-10 coding for measuring rates of and mortality from acute kidney injury and the impact of electronic alerts: an observational cohort study.Clin Kidney J. 2019; 13: 1083-1090Crossref PubMed Google Scholar, 16Bell S. James M.T. Farmer C.K.T. et al.Development and external validation of an acute kidney injury risk score for use in the general population.Clin Kidney J. 2020; 13: 402-412Crossref PubMed Google Scholar, 17Sawhney S. Robinson H.A. van der Veer S.N. et al.Acute kidney injury in the UK: a replication cohort study of the variation across three regional populations.BMJ Open. 2018; 8e019435Crossref PubMed Scopus (20) Google Scholar, 18Sawhney S. Tan Z. Black C. et al.Validation of risk prediction models to inform clinical decisions after acute kidney injury.Am J Kidney Dis. 2021; 78: 28-37Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar, 19Mclean A. Nath M. Sawhney S. Population epidemiology of hyperkalemia: cardiac and kidney long-term health outcomes.Am J Kidney Dis. 2022; 79: 527-538Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar Datasets for each region were prepared using a common analytical protocol and statistical code for both data preparation and analysis (available in the Supplementary Stata Analysis Code). The code was applied for analysis within each region. To avoid privacy risks associated with movement of individual-level patient data between regions, the analytical code was designed for each center to produce output files of aggregated data only, which were then sent to the coordinating center (University of Aberdeen) for pooling and final reporting. All creatinine results for each individual within each underlying population were extracted for analyses. Creatinine values that were recorded as missing or as a non-value (e.g., "sample inadequate," "sample error"), or were outside the limits for detection of the analyser, were excluded. All adult (age ≥ 18 years) residents within each population region with at least one creatinine test during 2009–2014 were included. Creatinine tests taken after initiation of long-term kidney replacement therapy (dialysis or transplant) for established kidney failure were excluded, as established by kidney replacement therapy registry data for each site. Shared statistical code was used to determine the subsets of patients that had AKI/AKD, based on KDIGO criteria (the Aberdeen definition). This code has been validated previously and used in AKI studies and is provided in the Supplementary Stata Analysis Code.17Sawhney S. Robinson H.A. van der Veer S.N. et al.Acute kidney injury in the UK: a replication cohort study of the variation across three regional populations.BMJ Open. 2018; 8e019435Crossref PubMed Scopus (20) Google Scholar,20Sawhney S. Marks A. Fluck N. et al.Intermediate and long-term outcomes of survivors of acute kidney injury episodes: a large population-based cohort study.Am J Kidney Dis. 2017; 69: 18-28Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar Four definition subsets (Box 1) of AKI/AKD were evaluated, alone and in combination. Subsets (1) and (2) comprise patients whose fulfillment of KDIGO AKI criteria could be ascertained directly from existing blood tests within the timeframes specified by the KDIGO AKI definition; subsets (3) and (4) comprise patients who fulfilled KDIGO criteria for AKD without observed AKI, or with presumed AKI, based on ascertainment using longer periods of time from the baseline creatinine measurement.10Lameire N.H. Levin A. Kellum J.A. et al.Harmonizing acute and chronic kidney disease definition and classification: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference.Kidney Int. 2021; 100: 516-526Abstract Full Text Full Text PDF PubMed Scopus (80) Google ScholarBox 1Definitions of AKI and AKD subsetsTabled 1Subsets 1 and 2: direct ascertainment of AKI according to KDIGO creatinine criteria:1.50% relative rise in creatinine from the lowest level within preceding 7 d2.0.3 mg/dl (26 micromol/l) absolute rise in creatinine from the lowest level within preceding 48 hSubsets 3 and 4: AKD according to KDIGO criteria without observed AKI, or with AKI that is presumed based on longer periods of time since baseline creatinine measurement:3.50% rise in creatinine from median creatinine value in preceding 8–90 d4.50% rise in creatinine from median creatinine value in preceding 91–365 d if no tests within preceding 8–90 dAKD, acute kidney disease; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes. Open table in a new tab Tabled 1Subsets 1 and 2: direct ascertainment of AKI according to KDIGO creatinine criteria:1.50% relative rise in creatinine from the lowest level within preceding 7 d2.0.3 mg/dl (26 micromol/l) absolute rise in creatinine from the lowest level within preceding 48 hSubsets 3 and 4: AKD according to KDIGO criteria without observed AKI, or with AKI that is presumed based on longer periods of time since baseline creatinine measurement:3.50% rise in creatinine from median creatinine value in preceding 8–90 d4.50% rise in creatinine from median creatinine value in preceding 91–365 d if no tests within preceding 8–90 dAKD, acute kidney disease; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes. Open table in a new tab AKD, acute kidney disease; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes. AKI/AKD were further categorized based on the following considerations: (i) the highest KDIGO AKI stage obtained over 90 days from AKI onset; (ii) kidney recovery (to within 50% of baseline by 90 days from onset); and (iii) presence of prior AKI within the previous year. Additional variables collected included age, sex, and baseline level of kidney function. Baseline kidney function was derived from the reference creatinine measurement that served as a baseline for that AKI/AKD episode, and was based on the estimated glomerular filtration rate (eGFR), determined using the Chronic Kidney Disease–Epidemiology Collaboration (CKD-EPI) equation, excluding the coefficient for Black race.21Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (16605) Google Scholar AKI/AKD associated with hospitalization was defined by a creatinine test from a hospital location within 7 days of onset of an AKI/AKD event. The incidence of AKI/AKD was determined for each cohort overall, and according to AKI/AKD subset definitions. Mortality from any cause, at 30 days and 1 year after onset of AKI/AKD, was also reported. All cohorts included linkages to national or regional vital statistics repositories to ensure comprehensive ascertainment of vital status in their regions. Results were reported for the years 2011, 2012, 2013, and 2014. Data from 2009 and 2010 were used for estimation of baseline kidney function and assessment of prior AKI. Incidence rates were standardized for age and sex to a UK population from 2012. Mid-year population denominators were obtained from population census reports for each region stratified by age and sex to calculate age- and sex-standardized incidence rates (per 10,000 person-years [py]).22National Records of ScotlandMid-year population estimates.https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/population/population-estimates/mid-year-population-estimatesDate accessed: October 10, 2021Google Scholar, 23Alberta Office of Statistics and Information. Population statistics.https://www.alberta.ca/population-statistics.aspxDate accessed: October 10, 2021Google Scholar, 24Statistics DenmarkStatBank Denmark.https://www.statbank.dk/statbank5a/default.asp?w=1600Date accessed: October 10, 2021Google Scholar Characteristics were reported by region and year, including population size, total number of adults with at least one blood test in the period, overall number of blood tests, mean/median number of blood tests, and mean/median value of creatinine results. Further characteristics of AKI/AKD included the following: number of people with at least one AKI/AKD episode overall and within definition subsets; AKI/AKD by stage; AKI/AKD associated with hospitalization, recovery, and prior AKI; and the proportion of people with AKI/AKD who progressed from stage 1 to stage 2 or 3. Annual AKI/AKD incidence rates were based on the first presentation of each person within a given year, expressed as the number of people first presenting with AKI/AKD per 10,000 py. Rates were reported stratified by age (in 10-year groups) and sex. For comparisons by sex, we reported those aged ≥40 versus <40 years separately, in recognition of the potential for artefactually increased rates of AKI/AKD due to physiological changes during pregnancy. Mortality was reported within each region at 30 days, 1 year, and by cumulative incidence plots for AKI/AKD overall and within each definition subset. To assess the implications of including each subset within the overall definition, both incidence and mortality were also assessed in groups created by sequentially removing AKI/AKD subcriteria from the definition (i.e., we assessed the number and outcomes of those people who could be identified by only one definition subset and would no longer be included among cases of AKI/AKD if that definition subset were to be removed). The purpose of this process was to assess the impact of including incrementally longer lookback periods beyond 7 days (to 90 days, or 1 year) for ascertaining baseline creatinine measurements when laboratory capture is complete across community and hospital settings. To additionally account for age and sex differences among AKI/AKD cohorts, mortalities were standardized for age and sex compared to the Grampian AKI/AKD population. All analyses were performed in Stata/SE 16 (StataCorp, release 16). Table 1 describes the characteristics of the study cohorts by geographic region and year. Of 7 million inhabitants included across all regions, 5.2–5.5 million adults were included annually. In each year, the number of people with at least one creatinine test increased from approximately 2.2 million in 2011 to 2.5 million in 2014, amounting to approximately 40% of the population from each region. The median creatinine level was similar among regions (range: 0.83–0.88 mg/dl). For 3 of the 4 geographic cohorts, the median creatinine level was stable across the years; however, an isolated increase was observed in the Tayside cohort in 2011, following a change in creatinine assay for the region during that year. The median age of people with a creatinine test in Alberta was lower (59 years) than that in northern Denmark, Grampian, and Tayside (65–68 years). Sex distributions were similar (range: 51.9%–54.4% female).Table 1Population characteristics of people with versus without blood-test monitoringAlberta, CanadaDenmarkGrampian, UKTayside, UK2011201220132014201120122013201420112012201320142011201220132014Population3,789,0303,874,5483,981,0114,083,6481,842,5281,847,7381,853,6781,860,807569,580573,400579,200584,220410,250411,740412,160413,800Population age ≥18 yr2,945,5563,013,6273,098,1983,179,4671,436,1041,445,4421,456,6221,468,340459,439463,114468,594473,815331,515333,457334,164336,098Total number of blood tests3,001,9833,155,3883,302,6433,506,8291,890,6161,977,4432,066,7212,121,404515,709534,608541,527563,180444,012451,672464,562475,969People monitored with ≥1 test in year1,277,5031,324,2611,381,2731,466,432599,266622,352638,402659,094171,534177,688183,154187,057140,720143,527146,215147,757People monitored as % of population43.443.944.646.141.743.143.844.937.338.439.139.542.443.043.844.0Mean no. tests per monitored person2.32.42.42.43.23.23.23.23.03.03.03.03.23.13.23.2Mean age of people tested, yr58.858.958.958.862.262.562.662.562.062.061.761.965.565.665.665.9Median age of people tested, yr59.559.759.759.765.065.065.065.065.065.065.065.068.068.068.068.0% of people tested, female53.453.553.353.152.052.051.951.954.254.053.854.054.454.454.354.2Median creatinine level, mg/dl0.880.870.870.870.850.850.870.870.840.830.830.830.880.830.810.81 Open table in a new tab Table 2 and Figure 1 describe the incidence of AKI/AKD. The majority of events were first episodes during the study period; 53.2% occurred in female patients, and 75.4%–79.7% were associated with hospitalization. Crude incidence rates varied among regions: crude incidence rates of AKI/AKD ranged from 114.5 (Alberta) to 174.0 (Tayside) per 10,000 py. After standardizing for age and sex, the standardized rates were similar across regions and years, ranging from 134.3 to 162.4 per 10,000 py (Figure 1). Figure 2 shows the age-stratified annual incidence of AKI/AKD, illustrating a consistent pattern of doubling of incidence with every 10-year increase in age across all regions and years, so that 8% of those aged >80 years had at least one AKI/AKD episode in a given year. AKI/AKD rates also had a consistent pattern across regions by sex, with greater age-standardized AKI incidence rates among male patients than among female patients aged >40 years, and substantially lower AKI/AKD incidence among male patients than among female patients aged <40 years (Figures 3 and 4).Table 2Population rate and annual incidence of AKI and AKD overall and within definition subsetsAlberta, CanadaDenmarkGrampian, UKTayside, UK2011201220132014201120122013201420112012201320142011201220132014AKI/AKD rates Total number of episodes36,57840,18042,29844,37721,30422,21622,39021,79664066619650567776239551857215727 Incident episodes33,72637,01439,03140,94919,50120,30320,54919,94159556190609962615769514853755314 Crude incidence, per 10,000 per yr114.5122.8126.0128.8135.8140.5141.1135.8129.6133.7130.2132.1174.0154.4160.8158.1 (95% CI)(113.2–115.7)(121.6–124.1)(124.7–127.2)(127.5–130.0)(133.9–137.7)(148.5–142.3)(139.2–143.0)(133.9–137.7)(126.3–132.9)(130.4–137.0)(126.9–133.5)(129.2–135.7)(169.6–178.6)(150.2–158.7)(156.6–165.2)(153.9–162.4) Age–sex standardized incidence, per 10,000 py149.1157.9160.3162.4138.1141.6141.5134.3137.0140.1135.8137.3162.3142.3147.9143.9 (95% CI)(147.5–150.7)(156.3–159.5)(158.7–161.9)(160.8–164.0)(136.2–140.0)(139.7–143.5)(139.6–143.4)(132.5–136.2)(133.6–140.4)(136.7–143.5)(132.5–139.1)(134.0–140.7)(158.3–166.4)(138.6–146.3)(144.0–151.8)(140.1–147.8)AKI/AKD subset definitions, % Meets criterion of changes within 7 d24.524.323.723.426.325.825.025.429.128.528.627.628.831.429.328.248 h25.323.824.024.530.629.229.930.431.429.428.729.128.632.728.630.38–90 d39.339.138.338.040.941.841.842.439.240.040.140.437.739.740.940.391–365 d25.526.927.927.821.922.222.020.423.226.325.925.424.220.724.422.7 Exclusively only meets criterion of changes within 7-d criterion9.59.69.29.18.17.97.87.89.99.29.88.411.011.010.09.848 h12.711.711.812.414.913.814.815.114.012.112.212.513.215.212.214.08–90 d29.929.829.429.229.030.030.030.627.227.128.228.226.526.629.028.791–365 d22.924.325.325.119.019.419.417.719.922.822.221.821.417.421.119.4AKI/AKD phenotype, % Stage 169.269.570.471.265.165.666.867.468.270.069.270.669.667.768.368.9 Stage 218.618.517.817.419.719.519.018.319.318.819.018.918.519.119.618.7 Stage 312.212.011.811.515.114.814.214.212.411.311.810.511.913.212.112.4 AKI/AKD in hospital77.375.875.777.577.575.475.977.379.778.277.275.6nananana Progression from stage 1 to 2/314.613.813.512.918.117.517.016.315.314.214.012.614.315.214.414.4 Without recovery to within 1.5× baseline33.834.935.336.032.632.831.232.130.729.629.930.331.930.028.731.2 Prior AKI/AKD episode in the past year8.89.08.89.39.39.29.59.89.27.67.87.68.48.47.28.6AKD, acute kidney disease; AKI, acute kidney injury; CI, confidence interval; na, not available; py, person-year. Open table in a new tab Figure 2Population incidence rates of acute kidney injury (AKI) and acute kidney disease (AKD) per 10,000 person-years within age groups (Denmark; Tayside, UK; Alberta, Canada; Grampian, UK).View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 3Age- and sex-standardized rates of acute kidney injury (AKI) and acute kidney disease (AKD) for male and female people aged ≥40 years (Denmark; Tayside, UK; Alberta, Canada; Grampian, UK).View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 4Age- and sex-standardized rates of acute kidney injury (AKI) and acute kidney disease (AKD) for male and female people aged <40 years (Denmark; Tayside, UK; Alberta, Canada; Grampian, UK).View Large Image Figure ViewerDownload Hi-res image Download (PPT) AKD, acute kidney disease; AKI, acute kidney injury; CI, confidence interval; na, not