Temporal trends in acute kidney injury-related mortality across 43 countries, 1996–2021, with projections up to 2050: a global time series analysis and modelling study

Hyeon Seok Hwang; Soeun Kim; Soo-Young Yoon; Hayeon Lee; Hyeon Jin Kim; Selin Woo; Lee Smith; Louis Jacob; Jinseok Lee; Dong Keon Yon

doi:10.23876/j.krcp.25.224

Kidney Res Clin Pract > Epub ahead of print

Hwang, Kim, Yoon, Lee, Kim, Woo, Smith, Jacob, Lee, and Yon: Temporal trends in acute kidney injury-related mortality across 43 countries, 1996–2021, with projections up to 2050: a global time series analysis and modelling study

Original Article

Kidney Research and Clinical Practice 2025;j.krcp.25.224.

Published online: December 4, 2025

DOI: https://doi.org/10.23876/j.krcp.25.224

Temporal trends in acute kidney injury-related mortality across 43 countries, 1996–2021, with projections up to 2050: a global time series analysis and modelling study

Hyeon Seok Hwang^1,^2,^*

, Soeun Kim^2,^3,^*

, Soo-Young Yoon¹

, Hayeon Lee²

, Hyeon Jin Kim^2,³

, Selin Woo²

, Lee Smith^4,⁵

, Louis Jacob^6,^7,⁸

, Jinseok Lee⁹

, Dong Keon Yon^2,^3,¹⁰

¹Division of Nephrology, Department of Internal Medicine, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

²Center for Digital Health, Medical Science Research Institute, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

³Department of Precision Medicine, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

⁴Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK

⁵Department of Public Health, Faculty of Medicine, Biruni University, Istanbul, Türkiye

⁶Research and Development Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, ISCIII, Barcelona, Spain

⁷Department of Physical Medicine and Rehabilitation, Lariboisière-Fernand Widal Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), Université Paris Cité, Paris, France

⁸Epidemiology of Ageing and Neurodegenerative Diseases (EpiAgeing), U1153, Inserm, Université Paris Cité, Paris, France

⁹Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea

¹⁰Department of Pediatrics, Kyung Hee University Medical Center, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

Correspondence: Dong Keon Yon Department of Pediatrics, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, South Korea E-mail: yonkkang@gmail.com

Hyeon Seok Hwang Division of Nephrology, Department of Internal Medicine, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
Tel: 82-2-958-8162 Fax: 82-2-968-1848 E-mail: hwanghsne@gmail.com

^*Hyeon Seok Hwang and Soeun Kim contributed equally to this study as co-first authors.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial and No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted non-commercial use, distribution of the material without any modifications, and reproduction in any medium, provided the original works properly cited.

Abstract

Background

Acute kidney injury (AKI) is a major global public health concern. However, a major challenge in addressing the AKI burden is the lack of global data on AKI-related mortality and its predictions, leaving significant limitations in understanding its trends over time. Therefore, we aimed to estimate AKI-related mortality rate trends and forecast future deaths.

Methods

We evaluated the temporal trends in age-standardized AKI-related mortality from 1996 to 2021 across 43 countries using the World Health Organization Mortality Database, with future projections through to 2050. Temporal trends were assessed based on age-standardized mortality rates, and future projections up to 2050 were calculated using a predictive model that considered attributable risk factors from the Global Burden of Disease Study 2021.

Results

Age-standardized AKI-related mortality rate per 1,000,000 people remained stable from 1996 to 2021 (10.47 [95% confidence interval (CI), 8.84–12.11] to 9.94 [95% CI, 8.32–11.57]). Although age-standardized mortality rates were lower in high-income countries (HICs) compared to low- and middle-income countries (LMICs), HICs exhibited a modest but statistically significant increasing trend (from 5.83 per 1,000,000 people [95% CI, 4.21–7.46] to 7.30 [95% CI, 5.66–8.95]), whereas LMICs showed a declining trend (from 19.66 [95% CI, 16.78–22.53] to 15.33 [95% CI, 12.37–18.29]). Projections indicate that mortality will rise to 11.36 per 1,000,000 population (95% CI, 10.65–12.07) by 2050, primarily attributable to population aging.

Conclusion

This global time-series modeling study highlights rising AKI-related mortality in HICs and/or aging populations. These findings underscore the need for targeted interventions to mitigate future AKI-related deaths.

Keywords: Acute kidney injury, Epidemiology, Global disease burden, Mortality, Projections and predictions

Introduction

Acute kidney injury (AKI) is a common disorder characterized by a rapid decline in glomerular filtration. AKI not only increases the risk of chronic kidney disease and the need for dialysis but also leads to a wide range of non-renal organ complications (i.e., hyperkalemia, severe acidosis, fluid overload, encephalopathy, and pericarditis) [2]. Thus, AKI frequently worsens clinical outcomes in critically ill patients, significantly escalating the risk of short-term mortality to rates often exceeding 50% [2]. Furthermore, the development of AKI increases mortality risk even after recovery from an acute critical phase, and this risk is notably heightened when accompanied by underlying comorbidities [3]. Hence, AKI is estimated to contribute to approximately 1.7 million global deaths annually and is an increasingly urgent concern owing to its profound burden on healthcare systems [4].

The incidence of AKI has increased worldwide over the past few years. The aging population, the limited and inequitable access to renal care, and the escalating prevalence of chronic kidney disease have further exacerbated this trend [5]. In response, professional nephrology societies undertook initiatives to reduce the global AKI-related health burden, and the International Society of Nephrology’s “0by25” is a global campaign aiming to eliminate all preventable deaths from AKI by the year 2025 [6]. However, a major challenge in addressing the AKI burden is the lack of reliable and comprehensive global data on AKI-related mortality. Significant gaps remain in our understanding of the temporal changes in AKI-related mortality and regional variations in their impact. Differences in AKI manifestations suggest that mortality trends may demonstrate significant disparities between high-income countries (HICs) and low-income countries [7].

Therefore, we aimed to systematically evaluate mortality rates associated with AKI worldwide. The study had the following four main objectives: 1) to determine the temporal trends in AKI-related mortality using the World Health Organization (WHO) Mortality Database, 2) to analyze the relationship between AKI-related mortality and socioeconomic and demographic indicators, 3) to employ mathematical modeling to forecast AKI-related mortality trends, and 4) to perform decomposition analysis to identify the factors contributing to variations in AKI mortality trend [8].

Methods

The present study used the WHO Mortality Database to explore the temporal trends and patterns in the incidence of AKI-related mortality from 1996 to 2021 across 43 countries [9]. Additionally, we investigated various factors related to AKI-related mortality and projected future trends up to 2050 [10]. This study complied with the Guidelines for Accurate and Transparent Health Estimate Reporting, and the study protocol was approved by the Institutional Review Board of Kyung Hee University (KHSIRB-25-128).

Data sources and processing

The main data source was the WHO Mortality Database, which compiles annual mortality statistics by cause of death, age, and sex from reports made by WHO member states via their civil registration systems [9,11]. From 1996 to 2021, we extracted the total number of AKI-related deaths for all relevant age categories, sexes, and countries. To be included in the study, a country had to have at least 80% of its data reported during the observation period (between 1996 and 2021); 43 countries satisfied this requirement (Supplementary Table 1, available online). The WHO received data from each nation that had not been corrected for incompleteness; the mortality values were presented as absolute numbers, up to and including zero [9]. For years in which certain nations did not report, we did not use any imputation methods [11]. Therefore, missing data were retained in their original form without interpolation or estimation. The International Classification of Diseases, 10th Revision (ICD-10) code was used to classify the causes of death. We utilized ICD-10 codes (N17; Supplementary Table 2, available online) to identify AKI-related deaths [12]. Potential misclassification resulting from cross-country variation in coding practices could not be assessed due to the absence of relevant metadata. For estimating the mortality rates, all population data for each nation were taken from the United Nations (UN) dataset.

To conduct a comprehensive analysis of the global mortality rates associated with AKI, we classified countries based on two key variables: income level (HICs and low- and middle-income countries [LMICs]) and the Human Development Index (HDI), which includes the categories of very high HDI, high HDI, and medium HDI. The classification of income was determined using the World Bank’s Gross National Income per capita criteria, with a threshold set at $13,846 for each country (Supplementary Table 3, available online) [13]. The HDI, developed by the UN, ranges from 0 to 1 and evaluates human development based on variables such as health, education, and living standards [14]. In this study, countries were categorized according to their HDI values: those with an HDI of 0.9 or higher were classified as very high HDI, those with values between 0.89 and 0.8 as high HDI, and those with an HDI of 0.8 or lower as medium HDI (Supplementary Table 4, available online). This stratification allows the identification of distinct characteristics across countries according to their developmental or economic profiles.

Temporal trends analysis via locally estimated scatter plot smoother curve

Age-standardized AKI-related mortality rates were calculated to account for variations in population distributions over time and between nations (Supplementary Methods, available online). These rates were analyzed for six age groups: under 20 years, 20 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 64 years, and 65 years and older, using the WHO World Standard Population as a reference [9].

A smoothed curve describing global trends in AKI-related mortality over time was generated for 43 countries using a locally estimated scatter plot smoother (LOESS) for trend analysis (Supplementary Table 1 and Supplementary Methods, available online) [9]. LOESS was selected for its ability to accommodate incomplete and irregularly spaced data, making it appropriate for analyzing longitudinal mortality trends with occasional missingness. The LOESS curve serves as a smoothing technique that connects data points while accounting for fluctuations, thereby facilitating the identification of overall trends in the data [15]. To calculate the smoothing parameter, the software’s default optimization process was employed (Supplementary Methods, available online) [9]. The death rates per 1 million individuals were presented alongside their 95% confidence intervals (CIs), and a two-sided p-value of <0.05 was utilized to evaluate statistical significance. The LOESS curve was fitted and illustrated using SAS (version 9.4, SAS Institute) and Python software (version 3.11.4, Python Software Foundation), respectively [16].

Association analysis of socioeconomic indicators of acute kidney injury-related mortality

The relationship between AKI-related mortality and several socioeconomic variables was examined using four indices: HDI, Socio-demographic Index (SDI), Sustainable Development Goals (SDGs) 3, and the reverse Gini coefficient (Supplementary Methods). The SDI from the Global Burden of Disease Study (GBD) reflects developmental status with components such as income, education, and birth rates [17,18]. HDI and SDI values range from 0 to 1, with values approaching 1 indicating high development [14]. The index used in the SDGs, ranging from 0 to 100, focuses on Goal 3 related to improving health and well-being [19]. It addresses critical issues needing improvement in developing countries, such as maternal and child mortality and infectious diseases. It also encompasses concerns that require attention in middle-income countries and HICs, including road traffic fatalities, universal health coverage, and health impacts from environmental pollution [20]. The Gini coefficient, as reported by the World Bank, is a tool used to evaluate income inequality in economies. The Gini coefficient was reversed and presented to facilitate the interpretation of the direction of increase in relation to other variables. Lower reverse Gini coefficient values, ranging from 0 to 100, indicate considerable income inequality [21].

Scatter plots revealing the average AKI-related mortality rates for each nation were created using the most recent indices provided by each institution, and linear regression was used to evaluate relationships. β coefficients, representing the slopes estimated from the linear regression models, and p-values from the analysis conducted using Python software are displayed.

Estimate projections

To forecast AKI-related mortality trends up to 2050, this study projected future changes based on the LOESS-calculated AKI-related mortality rates from 1996 to 2021. This approach aimed to provide a comprehensive analysis of long-term trends and the potential future burden of AKI-related mortality, utilizing mean body mass index (BMI), systolic blood pressure (SBP), and fasting plasma glucose (FPG) as predictive covariates, sourced from the GBD 2021 estimate [22]. Population attributable fractions and summary exposure values (SEVs) were calculated for each risk factor, with SEVs ranging from 0 (no risk) to 1 (maximum risk) to quantify excess risk exposure. Scalars, defined as Scalar=1/1-PAF, were employed to adjust mortality rates not attributable to GBD risk factors, enabling more precise estimations (Supplementary Methods, available online) [23]. Logit-transformed prevalence rates served as the dependent variable in a regression model, where mean BMI, SBP, and FPG were included as fixed effects, and location-age-sex was applied to account for population-level heterogeneity. The regression model was as follows:

Logit (AKI mortaltiy rate)= α+β1× mean BMI+β2*mean SBP+β3×mean FPG +ϵ

Adjustments were implemented to align the projected values with the GBD 2021 mortality estimates, ensuring consistency with existing data. Validation testing was conducted using data from 1990 to 2010 to predict mortality rates for 2010–2021, with observed and predicted values compared using the root mean squared error to evaluate model performance [23]. Subgroup-specific RMSE values, reflecting the model’s predictive accuracy across HDI levels and sex groups, are presented in Supplementary Table 5 (available online).

Decomposition analysis in acute kidney injury-related mortality

Three factors were examined using a decomposition analysis: population growth, population aging, and epidemiological changes. This analysis focused on the effects of these factors on trends in AKI-related mortality rates between 1996 and 2021, as well as between 1996 and 2050 [24]. Epidemiological changes refer to variations in AKI-related mortality rates adjusted for population size and age. The analysis was conducted by Das Gupta [25] and incorporated demographic statistics, age structure information, and the frequency of deaths from AKI [11]. Furthermore, we performed an analysis based on the classification of very high human development, high human development, and medium human development to explore the nuanced influence of HDI. Positive and negative results indicated increases or decreases in overall mortality, respectively, owing to the contributions of the analyzed factors. Detailed explanations are provided in the (Supplementary Methods, available online).

Role of the funding source

The study funders had no role in the study design, data collection, analysis, interpretation, or report writing. The corresponding author had full access to all the data in the study and was finally responsible for the decision to submit it for publication.

Results

Age-standardized AKI-related mortality rates were available for 43 countries from the WHO Mortality Database, covering 1996 to 2021. The LOESS-smoothed rate for temporal age-standardized AKI-related mortality was 10.47 deaths per 1,000,000 people (95% CI, 8.84–12.11) in 1996 and 9.94 deaths per 1,000,000 people (95% CI, 8.32–11.57) in 2021 (Table 1). Fig. 1 illustrates an overview of the global trends in age-standardized AKI-related mortality, showing that the mortality rate did not exhibit notable changes during the study period.

Trends in age-standardized AKI-related mortality rates were individually estimated for informal comparisons between countries with different socioeconomic statuses. Of the 42 countries, 28 HICs (66.7%) and 14 LMICs (33.3%) were included in the analysis. The age-standardized AKI-related mortality rate was lower in HICs than in LMICs. The LOESS-smoothed age-standardized mortality rate was decreased in LMICs from 19.66 deaths per 1,000,000 people (95% CI, 16.78–22.53) in 1996 to 15.33 deaths per 1,000,000 people (95% CI, 12.37–18.29) in 2021. However, there was a gradual increase in age-standardized AKI-related mortality in HIC from 5.83 deaths per 1,000,000 people (95% CI, 4.21–7.46) to 7.3 deaths per 1,000,000 people (95% CI, 5.66–8.95) in 2021 (Table 1). We also classified the countries into 33 very high HDI, eight high HDI, and two medium HDI countries (Fig. 2). Similarly, the trends in age-standardized AKI-related mortality rates consistently decreased in high and medium-HDI countries, whereas they gradually increased in very high HDI countries.

To examine the relationship between socioeconomic status and age-standardized AKI-related mortality rates, we analyzed their associations. The HDI (β = -47.058, p < 0.001), SDI (β = -38.404, p < 0.001), and SDGs (β = –0.402, p < 0.001) were negatively associated with age-standardized AKI-related mortality rates, while the reverse Gini coefficient (β = -0.529, p < 0.001) also demonstrated a negative association, indicating that lower income inequality is associated with reduced age-standardized AKI-related mortality rates (Fig. 3).

The LOESS-smoothed age-standardized mortality rates for global AKI are illustrated separately for males and females (Table 1). Male individuals exhibited a slightly higher rate of age-standardized AKI-related mortality than female individuals, with no significant differences observed in the trend patterns of age-standardized AKI-related mortality rates.

In all age categories under 50 years, the LOESS-smoothed estimate of the decrease in AKI-related mortality rate from 1996 to 2021, regardless of sex (Fig. 4), and this pattern was consistent across almost all income and HDI statuses (Supplementary Figs. 1–3, available online). Individuals aged 50–64 years revealed no remarkable global changes in AKI-related mortality rate. In the population aged 65 years or older, an upward trend in AKI-related mortality rate was observed among females, increasing from 74.48 deaths per 1,000,000 people (95% CI, 60.18–88.78) in 1996 to 85.58 deaths per 1,000,000 people (95% CI, 71.42–99.74) in 2021. Increasing AKI-related mortality trends in individuals aged >65 years were also found in HICs and countries with a very high HDI, while they had lower AKI-related mortality rates than other countries.

Global trends in age-standardized AKI-related mortality rates were projected up to 2050 (Fig. 5). The age-standardized AKI-related mortality rate is expected to show a gradual incline from 9.94 per 1,000,000 people (95% CI, 8.32–11.57) in 2021 to 10.71 (95% CI, 10.00–11.75) in 2030, 11.04 (95% CI, 10.33–11.75) in 2040, and 11.36 (95% CI, 10.65–12.07) in 2050. Changes in the number of age-standardized AKI-related mortality were most pronounced in very high HDI countries than in high or medium-HDI countries (Supplementary Table 6, available online).

To quantify the drivers of changes in the number of AKI-related deaths, we estimated the relative contributions of three factors: aging, epidemiological changes, and population (Fig. 6). The increase in AKI-related deaths between 1996 and 2021 was primarily driven by aging (6,956 deaths) and population growth (3,708 deaths), and its role is anticipated to become even more significant during 1996–2050 (Supplementary Tables 7–9, available online). The greatest increase in mortality was observed in countries with very high HDI compared to those with high- or medium-HDI countries.

Discussion

This study evaluated the temporal AKI-related mortality rates and their trends from 1996 to 2021 across 43 countries using the WHO Mortality Database. Our findings showed that age-standardized AKI-related mortality rates remained relatively stable globally; however, significant variations were noted across different socioeconomic status and age categories. Countries with a higher socioeconomic status exhibited upward trends in age-standardized AKI-related mortality rates, whereas countries with a lower socioeconomic status showed a decline. Mortality rates were lower and decreased in younger populations. Conversely, older populations showed higher rates, with increasing trends. In future projections, age-standardized AKI-related mortality is anticipated to gradually increase, with population aging predominantly driving this increase.

Compelling data from several studies indicate that the incidence of AKI has increased across all socioeconomic levels, with some reports demonstrating a three- to four-fold increase over the past 10 to 15 years [26,27]. Interestingly, despite the sharp increase in AKI incidence, our study found that the global age-standardized AKI-related mortality rate remained stable and consistently lower at approximately 10 deaths per million annually compared to the global AKI incidence of 2,000 to 3,000 cases per million per year [5,28,29]. These findings suggest that cumulative efforts to improve AKI management may lead to favorable results in terms of the AKI-related mortality rate, considering the trends in AKI incidence. Nonetheless, the lack of a significant decline in AKI-related mortality remains a concern, underscoring the need to strengthen ongoing efforts to reduce mortality rates further.

We found that LMICs and high and medium-HDI countries exhibited significantly higher age-standardized AKI-related mortality rates than countries classified as high income or with very high HDI. Furthermore, all unfavorable socioeconomic indices, including the HDI, SDI, SDGs, and the reverse Gini coefficient, were significantly associated with higher mortality rates. These findings highlight the greater burden of AKI in countries with lower socioeconomic status, which is consistent with previous results [30,31]. Encouragingly, we observed significant declines in AKI-related mortality trends, with steeper declines in countries with lower income or HDI levels. Improved healthcare infrastructure, enhanced medical access, and advancements in kidney care delivery have contributed to the reduction of AKI-related mortality rates in these countries [32]. Furthermore, global initiatives prioritized for these regions, such as the movement to achieve zero preventable deaths by 2025, may further facilitate mortality reduction [6,33].

While high-socioeconomic countries found lower age-standardized AKI-related mortality rates than countries with low socioeconomic levels, we observed a gradual increase in mortality trends. The characteristics and etiology of AKI differ significantly between the two regions [7,34]. In LMICs, AKI often emerges from a single infectious disease or hypovolemia and affects young and otherwise healthy populations. However, in HICs, AKI predominantly occurs in the intensive care unit and is often associated with multiple comorbidities and organ failure. Therefore, the reduction of AKI-related mortality remains challenging owing to the medical complexity and could further increase with increasing comorbidities. We suggest that the strategy to reduce AKI-related mortality rates in HICs should be different and involve a multifaceted framework that integrates kidney care, management of comorbid conditions, and critical care.

Our study found significantly different trends in AKI-related mortality across the age categories. Patients over 65 years of age showed an increasing trend in AKI-related mortality, whereas this trend decreased among younger populations. These patterns are particularly prominent in countries with very high HDI levels. Increasing AKI-related mortality among older adults could pose a significant challenge in high-socioeconomic countries, where complicated manifestations of AKI are prevalent. Conversely, the declining trend in younger populations may reflect improvements in early AKI detection, greater clinical awareness, and the implementation of standardized acute care protocols in intensive care settings. These advancements, combined with a relatively lower comorbidity burden, may have contributed to reduced AKI-related mortality in this group. These findings underscore the need for age- and region-specific strategies.

Over the last quarter century, rapid global population growth and an accelerated pace of aging have reshaped health outcomes worldwide [35]. Consistent with these trends, our decomposition analysis of the past period revealed that aging and population growth were primarily responsible for changes in mortality. Our projections of age-standardized AKI-related mortality indicated a gradual increase in the mortality rate over the next 25 years, with the largest increase anticipated in countries with very high HDI. Furthermore, population aging is expected to be a major contributor to the projected increase in AKI-related mortality over the coming decades. These findings underscore the importance of implementing targeted public health strategies in high-HDI settings, where aging populations are expanding rapidly. Health systems should prioritize the early identification of high-risk older adults, integrate AKI risk assessments into routine inpatient and perioperative care, and strengthen monitoring of nephrotoxic medication use in older populations. Therefore, our findings highlight the need for AKI prevention and management strategies that effectively address the needs of aging and high-income populations.

This study provides a comprehensive global analysis of AKI-related mortality trends and future projections using standardized mortality data from 43 countries. While the analytical methods are established, their use in AKI-specific mortality forecasting remains limited. Through the inclusion of key metabolic risk factors and socioeconomic indicators, our study offers new insights into cross-national disparities, potential inflection points, and long-term trajectories in AKI-related mortality. Therefore, our study addresses a previously underexplored aspect of AKI-related mortality that has not been captured in GBD-based assessments and contributes to the broader understanding of global kidney health. However, this study had several limitations. First, the WHO Mortality Database relies on data reported from member countries, which may result in incompleteness or inconsistencies due to variation in national death registration systems and ICD coding practices. These differences can lead to underreporting or misclassification of AKI-related mortality. To reduce the impact of reporting bias, we used age-standardized mortality rates, LOESS smoothing, and multicountry comparisons over a 25-year period. While these methods are useful to minimize the limitations, some residual bias may persist. Second, AKI-related mortality rates may have been underestimated in LMICs or medium-HDI countries because of limited access to medical resources and insufficient nationwide data collection systems [30]. Therefore, global efforts should prioritize the collection of reliable AKI-related data in these regions. Third, the use of ICD-10 codes to define AKI may underestimate the true burden, primarily affecting elderly or multimorbid patients. Prior validation studies have shown that the sensitivity of ICD-based definitions is moderate (35%–76%), whereas specificity is consistently high (91%–98%), and the positive predictive value ranges widely from 48% to 94% [36–40]. Considering these limitations, our study focused on assessing temporal trends and relative differences across countries rather than precise estimates of absolute mortality. Nonetheless, the potential for underestimation in specific populations should be considered when interpreting our findings. Fourth, the WHO Mortality Database lacks detailed information on the causes of AKI-related mortality, making it difficult to categorize AKI accurately and reflect its characteristics across countries. Finally, the lack of available data in the WHO database also makes it challenging to identify risk factors associated with AKI-related mortality.

In conclusion, our study of age-standardized AKI-related mortality using the WHO Mortality Database found that countries with higher socioeconomic statuses exhibited increasing trends in AKI-related mortality, whereas countries with lower socioeconomic statuses showed a decline over time. Mortality trends have also increased among older populations. Age-standardized AKI-related mortality is projected to increase gradually, and higher socioeconomic countries and populations aging are forecasted to be the key elements shaping future trends. Our study provides valuable insights for the development of strategies to relieve the global burden of AKI-related mortality.

Supplementary Materials

Supplementary data are available at Kidney Research and Clinical Practice online (https://doi.org/10.23876/j.krcp.25.224).

j-krcp-25-224-Supplementary-Methods.pdf

j-krcp-25-224-Supplementary-Table-1.pdf

j-krcp-25-224-Supplementary-Table-2.pdf

j-krcp-25-224-Supplementary-Table-3.pdf

j-krcp-25-224-Supplementary-Table-4.pdf

j-krcp-25-224-Supplementary-Table-5.pdf

j-krcp-25-224-Supplementary-Table-6.pdf

j-krcp-25-224-Supplementary-Table-7.pdf

j-krcp-25-224-Supplementary-Table-8.pdf

j-krcp-25-224-Supplementary-Table-9.pdf

j-krcp-25-224-Supplementary-Fig-1.pdf

j-krcp-25-224-Supplementary-Fig-2.pdf

j-krcp-25-224-Supplementary-Fig-3.pdf

Notes

Conflicts of interest

All authors have no conflicts of interest to declare.

Funding

This research was supported by the Patient-Centered Clinical Research Coordinating Center (PACEN) funded by the Ministry of Health & Welfare (RS-2024-00399169) and the Ministry of Science and ICT (MSIT), Republic of Korea, under the Information Technology Research Center (ITRC) support program (IITP-2024-RS-2024-00438239), supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP). This work was supported by the Institute of IITP grant funded by the Korea government (MSIT) (RS-2024-00509257, Global AI Frontier Lab). The funding agencies played no role in the study design, data collection, analysis, interpretation, or writing of the report.

Data sharing statement

The World Health Organization (WHO) Mortality Database is a global collaborative dataset of mortality rates reported by the WHO member countries. Study protocol and statistical code are available from Dong Keon Yon (yonkkang@gmail.com). The dataset is available from the WHO through a data use agreement.

Authors’ contributions

Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization: HSH, SK, DKY, HL, HJK, SW, LS, LJ, JL

Funding acquisition: DKY

Software: SK, DKY

Writing–original draft: HSH, SK, DKY

Writing–review & editing: SYY, HL, HJK, SW, LS, LJ, JL, DKY

All authors read and approved the final manuscript.

Figure 1.

Age-standardized AKI-related mortality rate (per 1,000,000 people) for the global, HICs, and LMICs population among 43 countries for the years 1990–2021.

The y-axis indicates age-standardized AKI-related mortality rates, with a consistent scale used across all three panels. The locally weighted scatterplot smoother mortality rates with 95% confidence intervals are shown in blue. HICs included 28 countries, Australia, Belgium, Chile, Croatia, Czech Republic, Denmark, Finland, Germany, Hungary, Israel, Japan, Kuwait, Lithuania, Luxembourg, Netherlands, Norway, Panama, Poland, Republic of Korea, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States of America, and Uruguay. LMICs included 14 countries, including Argentina, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Malaysia, Mexico, Nicaragua, Paraguay, Peru, Serbia, South Africa, and Thailand.

AKI, acute kidney injury; HIC, high-income country; LMIC, low- and middle-income country.

Figure 2.

Age-standardized AKI-related mortality rate (per 1,000,000 people) across the globe and HDI among 43 countries.

The y-axis indicates age-standardized AKI-related mortality rates, with a consistent scale used across all three panels. The locally weighted scatterplot smoother mortality rates with 95% confidence intervals are shown in blue. Very high human development group, including the 33 countries: Argentina, Australia, Belgium, Chile, Costa Rica, Croatia, Czech Republic, Denmark, Finland, Germany, Hungary, Israel, Japan, Kuwait, Lithuania, Luxembourg, Malaysia, Netherlands, Norway, Panama, Poland, Republic of Korea, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Thailand, United Kingdom, United States of America, and Uruguay. High human development group, including the eight countries: Brazil, Colombia, Dominican Republic, Ecuador, Mexico, Paraguay, Peru, and South Africa. And the medium human development group, which includes the 2 countries: Nicaragua and Venezuela.

AKI, acute kidney injury; HDI, human development index.

Figure 3.

Association between age-standardized AKI-related mortality rate (per 1,000,000 people) and Human Development Index, Socio-demographic Index, Sustainable Development Goals, and reverse Gini coefficient.

All four panels share a common y-axis, which represents the age-standardized mortality rate attributable to AKI (per 1,000,000 people). “Sustainable Development Goal” in this context refers to Goal 3: Good Health and Well-Being. The reverse Gini coefficient is a modified measure of income distribution, where higher values indicate greater equality.

AKI, acute kidney injury. Country codes are provided in Supplementary Table 1 (available online).

Figure 4.

LOESS-smoothed AKI-related mortality rate (per 1,000,000 people) by sex and age group among 43 countries from 1990 to 2021.

AKI, acute kidney injury; LOESS, locally weighted scatterplot smoother.

Figure 5.

Projections in age-standardized AKI-related mortality rate (per 1,000,000 people) from 1990 to 2050.

The dashed line represents the predicted value for forecasted mortality, whereas the shaded area represents the 95% confidence interval. In this predictive model, we incorporated covariates such as mean body mass index, systolic blood pressure, and fasting plasma glucose.

AKI, acute kidney injury.

Figure 6.

Changes in the number of AKI-related deaths (per 1,000,000) associated with aging, epidemiological change, and population from 1996 to 2021 and 1996 to 2050 by sex and HDI. Panel (A) illustrates the observed changes from 1996 to 2021, while panel (B) presents projected changes from 1996 to 2050. Each bar represents the contribution of the three components, and the black dot indicates the net change (combined effect). HDI levels are categorized as global, very high, high, and medium. Sex-specific results are shown for the overall, male, and female populations.

AKI, acute kidney injury; HDI, Human Development Index.

Table 1.

LOESS-smoothed age-standardized AKI-related mortality rate (per 1,000,000 people) across variables in 43 countries from 1990 to 2021

Variable	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008
Global	10.47	10.27	10.08	9.91	9.75	9.60	9.47	9.34	9.42	9.63	9.84	10.08	10.31
Global	(8.84–12.11)	(8.86–11.69)	(8.87–11.28)	(8.89–10.94)	(8.86–10.63)	(8.81–10.40)	(8.71–10.23)	(8.55–10.12)	(8.64–10.20)	(8.88–10.38)	(9.06–10.62)	(9.31–10.86)	(9.53–11.08)
Sex
Male	12.65	12.33	12.01	11.73	11.46	11.20	10.96	10.73	10.75	10.94	11.13	11.39	11.63
Male	(10.69–14.61)	(10.64–14.02)	(10.57–13.45)	(10.50–12.96)	(10.40–12.52)	(10.25–12.15)	(10.06–11.87)	(9.79–11.67)	(9.82–11.68)	(10.04–11.84)	(10.20–12.06)	(10.46–12.32)	(10.70–12.56)
Female	8.72	8.62	8.52	8.45	8.38	8.32	8.28	8.23	8.36	8.59	8.81	9.04	9.24
Female	(7.27–10.17)	(7.37–9.87)	(7.45–9.59)	(7.54–9.36)	(7.60–9.17)	(7.62–9.03)	(7.60–8.95)	(7.53–8.93)	(7.67–9.05)	(7.92–9.25)	(8.12–9.50)	(8.35–9.72)	(8.56–9.93)
Income
HICs	5.83	5.84	5.86	5.89	5.92	5.95	6.02	6.08	6.30	6.61	6.93	7.30	7.65
HICs	(4.21–7.46)	(4.48–7.21)	(4.72–6.99)	(4.95–6.83)	(5.11–6.73)	(5.20–6.70)	(5.30–6.74)	(5.33–6.84)	(5.55–7.05)	(5.89–7.34)	(6.18–7.68)	(6.55–8.05)	(6.90–8.40)
LMICs	19.66	19.06	18.46	17.92	17.37	16.86	16.41	15.96	15.83	15.84	15.85	15.99	16.19
LMICs	(16.78–22.53)	(16.58–21.54)	(16.35–20.58)	(16.11–19.72)	(15.82–18.93)	(15.46–18.26)	(15.07–17.75)	(14.56–17.35)	(14.45–17.22)	(14.50–17.18)	(14.47–17.23)	(14.62–17.37)	(14.82–17.57)
HDI
Very high	6.51	6.59	6.67	6.74	6.82	6.93	7.03	7.14	7.34	7.53	7.73	7.98	8.23
Very high	(5.04–7.97)	(5.30–7.88)	(5.54–7.79)	(5.77–7.72)	(5.98–7.67)	(6.18–7.68)	(6.34–7.72)	(6.46–7.81)	(6.69–7.98)	(6.88–8.19)	(7.04–8.42)	(7.31–8.65)	(7.54–8.91)
High	20.96	20.58	20.20	19.83	19.51	19.19	18.86	18.68	18.49	18.31	18.37	18.43	18.49
High	(17.86–24.05)	(17.84–23.32)	(17.79–22.62)	(17.71–21.94)	(17.64–21.37)	(17.52–20.85)	(17.32–20.40)	(17.22–20.14)	(17.04–19.95)	(16.79–19.82)	(16.91–19.83)	(16.97–19.89)	(16.98–20.01)
Medium	14.49	14.13	13.77	13.41	13.07	12.73	12.39	12.05	11.70	11.36	11.00	10.64	10.39
Medium	(9.60–19.39)	(9.64–18.62)	(9.67–17.87)	(9.67–17.15)	(9.66–16.48)	(9.62–15.84)	(9.53–15.25)	(9.39–14.70)	(9.19–14.21)	(8.93–13.79)	(8.58–13.42)	(8.17–13.12)	(7.95–12.83)
Age group (yr)
<20	0.79	0.78	0.77	0.76	0.74	0.73	0.72	0.71	0.69	0.68	0.67	0.65	0.64
<20	(0.65–0.93)	(0.65–0.91)	(0.65–0.89)	(0.65–0.87)	(0.65–0.84)	(0.64–0.82)	(0.64–0.80)	(0.63–0.78)	(0.62–0.76)	(0.61–0.75)	(0.60–0.73)	(0.59–0.72)	(0.57–0.71)
20–29	0.87	0.85	0.83	0.81	0.78	0.76	0.74	0.72	0.70	0.68	0.66	0.64	0.62
20–29	(0.72–1.02)	(0.71–0.98)	(0.70–0.95)	(0.69–0.92)	(0.68–0.89)	(0.67–0.86)	(0.65–0.83)	(0.64–0.80)	(0.63–0.78)	(0.61–0.75)	(0.59–0.73)	(0.57–0.71)	(0.55–0.69)
30–39	2.33	2.29	2.25	2.20	2.16	2.12	2.08	2.03	1.99	1.95	1.90	1.86	1.82
30–39	(1.88–2.78)	(1.87–2.70)	(1.87–2.63)	(1.86–2.55)	(1.84–2.48)	(1.83–2.41)	(1.81–2.34)	(1.79–2.28)	(1.76–2.22)	(1.73–2.17)	(1.69–2.12)	(1.65–2.07)	(1.60–2.04)
40–49	4.53	4.49	4.44	4.39	4.35	4.30	4.26	4.21	4.16	4.11	4.07	4.02	3.98
40–49	(3.75–5.32)	(3.76–5.21)	(3.78–5.10)	(3.79–5.00)	(3.80–4.90)	(3.80–4.81)	(3.79–4.72)	(3.78–4.64)	(3.76–4.56)	(3.73–4.49)	(3.70–4.44)	(3.65–4.39)	(3.60–4.36)
50–64	12.79	12.75	12.71	12.67	12.64	12.61	12.58	12.56	12.53	12.50	12.48	12.46	12.44
50–64	(10.93–14.64)	(11.04–14.45)	(11.15–14.28)	(11.24–14.11)	(11.33–13.94)	(11.42–13.81)	(11.49–13.68)	(11.55–13.57)	(11.59–13.47)	(11.61–13.40)	(11.60–13.35)	(11.59–13.33)	(11.55–13.34)
65	88.07	86.60	85.13	84.00	82.87	81.88	80.98	80.09	81.22	83.65	86.08	88.85	91.44
65	(72.76–103.37)	(73.40–99.79)	(73.87–96.38)	(74.39–93.60)	(74.59–91.14)	(74.46–89.30)	(73.91–88.05)	(72.74–87.43)	(73.94–88.51)	(76.60–90.70)	(78.82–93.34)	(81.60–96.11)	(84.19–98.69)

Variable	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Global	10.45	10.59	10.64	10.62	10.61	10.52	10.43	10.34	10.25	10.17	10.09	10.02	9.94
Global	(9.70–11.20)	(9.82–11.37)	(9.87–11.41)	(9.87–11.38)	(9.83–11.39)	(9.78–11.26)	(9.69–11.17)	(9.54–11.14)	(9.35–11.15)	(9.13–11.22)	(8.88–11.31)	(8.61–11.43)	(8.32–11.57)
Sex
Male	11.79	11.95	12.01	12.01	12.00	11.91	11.82	11.74	11.65	11.57	11.50	11.43	11.36
Male	(10.89–12.69)	(11.03–12.88)	(11.09–12.94)	(11.10–12.91)	(11.06–12.93)	(11.02–12.80)	(10.94–12.71)	(10.78–12.69)	(10.57–12.73)	(10.32–12.83)	(10.04–12.95)	(9.74–13.12)	(9.42–13.31)
Female	9.37	9.50	9.53	9.50	9.47	9.37	9.27	9.17	9.07	8.98	8.89	8.80	8.71
Female	(8.70–10.04)	(8.81–10.18)	(8.84–10.21)	(8.83–10.16)	(8.78–10.16)	(8.72–10.03)	(8.62–9.93)	(8.46–9.88)	(8.27–9.87)	(8.05–9.91)	(7.81–9.97)	(7.55–10.05)	(7.28–10.15)
Income
HICs	8.00	8.11	8.08	7.88	7.68	7.50	7.39	7.28	7.26	7.27	7.27	7.29	7.30
HICs	(7.18–8.82)	(7.29–8.92)	(7.26–8.90)	(7.13–8.63)	(6.93–8.44)	(6.74–8.25)	(6.67–8.10)	(6.53–8.03)	(6.45–8.07)	(6.32–8.22)	(6.13–8.41)	(5.90–8.67)	(5.66–8.95)
LMICs	16.37	16.54	16.62	16.64	16.66	16.61	16.44	16.27	16.09	15.91	15.73	15.53	15.33
LMICs	(15.03–17.70)	(15.16–17.91)	(15.24–18.00)	(15.30–17.97)	(15.28–18.03)	(15.23–18.00)	(15.11–17.77)	(14.86–17.67)	(14.52–17.67)	(14.07–17.75)	(13.56–17.89)	(12.98–18.07)	(12.37–18.29)
HDI
Very high	8.47	8.54	8.61	8.67	8.68	8.69	8.65	8.61	8.58	8.55	8.52	8.50	8.47
Very high	(7.74–9.20)	(7.86–9.22)	(7.94–9.27)	(7.99–9.36)	(8.02–9.34)	(8.02–9.36)	(7.97–9.33)	(7.87–9.36)	(7.74–9.42)	(7.58–9.52)	(7.41–9.64)	(7.21–9.78)	(7.01–9.93)
High	18.54	18.59	18.64	18.45	18.27	18.08	17.87	17.66	17.45	17.20	16.96	16.71	16.47
High	(17.08–20.00)	(17.13–20.05)	(17.13–20.15)	(17.01–19.90)	(16.83–19.70)	(16.59–19.58)	(16.28–19.46)	(15.90–19.42)	(15.47–19.43)	(14.95–19.45)	(14.40–19.52)	(13.82–19.60)	(13.23–19.71)
Medium	10.14	9.89	9.76	9.64	9.51	9.39	9.27	9.15	9.02	8.90	8.77	8.64	-
Medium	(7.68–12.59)	(7.36–12.42)	(7.17–12.35)	(6.91–12.36)	(6.58–12.44)	(6.19–12.58)	(5.76–12.77)	(5.29–13.00)	(4.78–13.26)	(4.24–13.55)	(3.68–13.86)	(3.10–14.19)
Age group (yr)
<20	0.62	0.61	0.59	0.58	0.56	0.54	0.52	0.51	0.49	0.47	0.45	0.43	0.41
<20	(0.56–0.69)	(0.54–0.67)	(0.53–0.66)	(0.51–0.64)	(0.49–0.63)	(0.46–0.62)	(0.44–0.61)	(0.41–0.60)	(0.39–0.59)	(0.36–0.58)	(0.33–0.57)	(0.30–0.56)	(0.27–0.56)
20–29	0.60	0.59	0.57	0.56	0.54	0.52	0.51	0.49	0.48	0.46	0.44	0.43	0.41
20–29	(0.54–0.67)	(0.52–0.66)	(0.50–0.64)	(0.48–0.63)	(0.46–0.62)	(0.44–0.61)	(0.42–0.60)	(0.40–0.59)	(0.37–0.58)	(0.35–0.58)	(0.32–0.57)	(0.29–0.57)	(0.26–0.56)
30–39	1.78	1.74	1.70	1.66	1.61	1.57	1.53	1.48	1.44	1.39	1.34	1.30	1.25
30–39	(1.57–1.99)	(1.53–1.95)	(1.49–1.91)	(1.44–1.88)	(1.38–1.85)	(1.32–1.82)	(1.26–1.80)	(1.19–1.78)	(1.11–1.76)	(1.04–1.74)	(0.96–1.73)	(0.88–1.72)	(0.80–1.71)
40–49	3.94	3.91	3.87	3.82	3.78	3.74	3.69	3.64	3.59	3.54	3.49	3.43	3.38
40–49	(3.58–4.31)	(3.54–4.27)	(3.50–4.24)	(3.44–4.21)	(3.38–4.18)	(3.30–4.17)	(3.22–4.16)	(3.13–4.15)	(3.03–4.15)	(2.92–4.15)	(2.81–4.16)	(2.70–4.17)	(2.59–4.18)
50–64	12.44	12.44	12.45	12.43	12.41	12.39	12.37	12.34	12.32	12.28	12.24	12.20	12.16
50–64	(11.58–13.31)	(11.58–13.31)	(11.57–13.32)	(11.53–13.33)	(11.46–13.36)	(11.37–13.41)	(11.26–13.48)	(11.13–13.55)	(10.99–13.64)	(10.83–13.73)	(10.65–13.83)	(10.47–13.93)	(10.28–14.04)
65	93.25	95.05	95.86	95.96	96.06	95.46	94.94	94.43	93.96	93.56	93.16	92.85	92.53
65	(86.22–100.27)	(87.82–102.29)	(88.62–103.10)	(88.92–103.00)	(88.77–103.35)	(88.53–102.39)	(88.02–101.87)	(86.97–101.88)	(85.54–102.39)	(83.79–103.33)	(81.80–104.53)	(79.66–106.04)	(77.38–107.69)

Data are expressed as rate (95% confidence interval).

AKI, acute kidney injury; HDI, Human Development Index; HIC, high-income country; LMIC, low- and middle-income country; LOESS, locally weighted scatterplot smoother.

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