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      Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches

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          Abstract

          Background

          Chronic kidney disease (CKD) has become a major public health problem worldwide and has caused a huge social and economic burden, especially in developing countries. No previous study has used machine learning (ML) methods combined with longitudinal data to predict the risk of CKD development in 2 years amongst the elderly in China.

          Methods

          This study was based on the panel data of 925 elderly individuals in the 2012 baseline survey and 2014 follow-up survey of the Healthy Aging and Biomarkers Cohort Study (HABCS) database. Six ML models, logistic regression (LR), lasso regression, random forests (RF), gradient-boosted decision tree (GBDT), support vector machine (SVM), and deep neural network (DNN), were developed to predict the probability of CKD amongst the elderly in 2 years (the year of 2014). The decision curve analysis (DCA) provided a range of threshold probability of the outcome and the net benefit of each ML model.

          Results

          Amongst the 925 elderly in the HABCS 2014 survey, 289 (18.8%) had CKD. Compared with the other models, LR, lasso regression, RF, GBDT, and DNN had no statistical significance of the area under the receiver operating curve (AUC) value (>0.7), and SVM exhibited the lowest predictive performance (AUC = 0.633, p-value = 0.057). DNN had the highest positive predictive value (PPV) (0.328), whereas LR had the lowest (0.287). DCA results indicated that within the threshold ranges of ~0–0.03 and 0.37–0.40, the net benefit of GBDT was the largest. Within the threshold ranges of ~0.03–0.10 and 0.26–0.30, the net benefit of RF was the largest. Age was the most important predictor variable in the RF and GBDT models. Blood urea nitrogen, serum albumin, uric acid, body mass index (BMI), marital status, activities of daily living (ADL)/instrumental activities of daily living (IADL) and gender were crucial in predicting CKD in the elderly.

          Conclusion

          The ML model could successfully capture the linear and nonlinear relationships of risk factors for CKD in the elderly. The decision support system based on the predictive model in this research can help medical staff detect and intervene in the health of the elderly early.

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          Most cited references60

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          Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living

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            Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

            Summary Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI. Funding Bill & Melinda Gates Foundation.
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              Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.

              End-stage renal disease substantially increases the risks of death, cardiovascular disease, and use of specialized health care, but the effects of less severe kidney dysfunction on these outcomes are less well defined. We estimated the longitudinal glomerular filtration rate (GFR) among 1,120,295 adults within a large, integrated system of health care delivery in whom serum creatinine had been measured between 1996 and 2000 and who had not undergone dialysis or kidney transplantation. We examined the multivariable association between the estimated GFR and the risks of death, cardiovascular events, and hospitalization. The median follow-up was 2.84 years, the mean age was 52 years, and 55 percent of the group were women. After adjustment, the risk of death increased as the GFR decreased below 60 ml per minute per 1.73 m2 of body-surface area: the adjusted hazard ratio for death was 1.2 with an estimated GFR of 45 to 59 ml per minute per 1.73 m2 (95 percent confidence interval, 1.1 to 1.2), 1.8 with an estimated GFR of 30 to 44 ml per minute per 1.73 m2 (95 percent confidence interval, 1.7 to 1.9), 3.2 with an estimated GFR of 15 to 29 ml per minute per 1.73 m2 (95 percent confidence interval, 3.1 to 3.4), and 5.9 with an estimated GFR of less than 15 ml per minute per 1.73 m2 (95 percent confidence interval, 5.4 to 6.5). The adjusted hazard ratio for cardiovascular events also increased inversely with the estimated GFR: 1.4 (95 percent confidence interval, 1.4 to 1.5), 2.0 (95 percent confidence interval, 1.9 to 2.1), 2.8 (95 percent confidence interval, 2.6 to 2.9), and 3.4 (95 percent confidence interval, 3.1 to 3.8), respectively. The adjusted risk of hospitalization with a reduced estimated GFR followed a similar pattern. An independent, graded association was observed between a reduced estimated GFR and the risk of death, cardiovascular events, and hospitalization in a large, community-based population. These findings highlight the clinical and public health importance of chronic renal insufficiency. Copyright 2004 Massachusetts Medical Society
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                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                21 October 2022
                2022
                : 10
                : 998549
                Affiliations
                [1] 1Department of Health Management and Policy, School of Public Health, Capital Medical University , Beijing, China
                [2] 2Department of Systems, Populations, and Leadership, University of Michigan School of Nursing , Ann Arbor, MI, United States
                [3] 3Thomas E. Starzl Transplantation Institute, University of Pittsburgh Medical Center , Pittsburgh, PA, United States
                [4] 4Department of Biostatistics, University of Michigan School of Public Health , Ann Arbor, MI, United States
                [5] 5Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [6] 6Research Center for Rural Health Services, Hubei Province Key Research Institute of Humanities and Social Sciences , Wuhan, China
                Author notes

                Edited by: Roy Rillera Marzo, Management and Science University, Malaysia

                Reviewed by: Parismita Sarma, Gauhati University, India; Aditya Gupta, Amazon (United States), United States

                *Correspondence: Nina Wu wunina@ 123456ccmu.edu.cn

                This article was submitted to Aging and Public Health, a section of the journal Frontiers in Public Health

                Article
                10.3389/fpubh.2022.998549
                9634246
                a3b65b48-3bed-4cb2-9170-9d3b5b62c5f6
                Copyright © 2022 Su, Zhang, He, Chen and Wu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 July 2022
                : 20 September 2022
                Page count
                Figures: 3, Tables: 3, Equations: 6, References: 60, Pages: 13, Words: 8296
                Categories
                Public Health
                Original Research

                prediction,chronic kidney disease,elderly,machine learning,longevity areas,china

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