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      Impact of Arterial Calcification on Cardiovascular and Renal Outcomes in Kidney Transplant Patients

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          Abstract

          Introduction

          Coronary artery calcification score (CACS) and abdominal aortic calcification score (AACS) are both well-established markers of vascular stiffness, and previous studies have shown that a higher CACS is a risk factor for chronic kidney disease (CKD) progression. However, the impact of pretransplant CACS and AACS on cardiovascular and renal outcomes in kidney transplant patients has not been established.

          Methods

          We included 944 kidney transplant recipients from the KoreaN cohort study for Outcome in patients With Kidney Transplantation (KNOW-KT) cohort and categorized them into three groups (low, medium, and high) according to baseline CACS (0, 0 < and ≤100, >100) and AACS (0, 1–4, >4). The low (0), medium (0 < and ≤ 100), and high (>100) CACS groups each consisted of 462, 213, and 225 patients, respectively. Similarly, the low (0), medium (1–4), and high (>4) AACS groups included 638, 159, and 147 patients, respectively. The primary outcome was the occurrence of cardiovascular events. The secondary outcomes were all-cause mortality and composite kidney outcomes, which comprised of >50% decline in the estimated glomerular filtration rate and graft loss. Cox regression analysis was used to investigate the association between baseline CACS/AACS and outcomes.

          Results

          The high CACS group ( N = 462) faced a significantly higher risk for cardiovascular outcomes (adjusted hazard ratio [aHR], 5.97; 95% confidence interval [CI], 2.01–17.7) and all-cause mortality (aHR, 2.74; 95% CI, 1.27–5.92) compared to the low CACS group ( N = 225). Similarly, the high AACS group ( N = 638) had an elevated risk for cardiovascular outcomes (aHR, 2.38; 95% CI, 1.16–4.88). Furthermore, the addition of CACS to prediction models improved prediction indices for cardiovascular outcomes. However, the risk of renal outcomes did not differ among CACS or AACS groups.

          Conclusion

          Pretransplant arterial calcification, characterized by high CACS or AACS, is an independent risk factor for cardiovascular outcomes and mortality in kidney transplant patients.

          Plain Language Summary

          Arterial calcification, accumulation of calcium in the arterial walls, vascular stiffness, and loss of elasticity of blood vessels can contribute to the development of cardiovascular diseases. Patients with chronic kidney disease and those undergoing dialysis have a considerably increased risk of vascular calcification. Even after kidney transplantation when kidney function has been restored, the prevalence of vascular calcification and subsequent cardiovascular disease remains high. Coronary artery calcification score and abdominal aortic calcification score are both well-established markers of vascular calcification. However, the impact of pretransplant vascular calcification scores on cardiovascular and renal outcomes in kidney transplant patients has not been established. When we analyzed 944 Korean kidney transplant patients, both vascular calcification scores were significantly associated with cardiovascular outcomes after kidney transplantation, but were not associated with renal outcomes. We also demonstrated that the addition of coronary artery calcification scores led to a modest improvement in the prediction performance for kidney transplant outcomes. Our findings suggest a potential role of pretransplant screening of coronary calcification scores and aortic calcification scores in risk stratification for post-kidney transplant outcomes.

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          Most cited references39

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            A new equation to estimate glomerular filtration rate.

            Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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              Multiple imputation using chained equations: Issues and guidance for practice

              Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. 2010 John Wiley & Sons, Ltd.

                Author and article information

                Journal
                Kidney Dis (Basel)
                Kidney Dis (Basel)
                KDD
                KDD
                Kidney Diseases
                S. Karger AG (Basel, Switzerland )
                2296-9381
                2296-9357
                16 April 2024
                August 2024
                : 10
                : 4
                : 249-261
                Affiliations
                [a ]Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, Seoul, Republic of Korea
                [b ]Department of Internal Medicine, Seoul National University College of Medicine, Bundang Hospital, Seongnam, Republic of Korea
                [c ]Department of Internal Medicine, Ewha Womans University Medical Center, Seoul, Republic of Korea
                [d ]Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
                [e ]Department of Transplantation Surgery, Severance Hospital, Yonsei University Health System, Seoul, Republic of Korea
                [f ]Department of Surgery, Sungkyunkwan University, Seoul Samsung Medical Center, Seoul, Republic of Korea
                [g ]Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
                [h ]Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
                [i ]Department of Internal Medicine, Gachon University, Gil Hospital, Incheon, Republic of Korea
                [j ]Department of Internal Medicine, Keimyung University, Dongsan Medical Center, Daegu, Republic of Korea
                Author notes
                Correspondence to: Jaeseok Yang, jcyjs@ 123456yuhs.ac
                Article
                538929
                10.1159/000538929
                11309755
                163cbb51-3826-40af-a4ed-5efed5203ee8
                © 2024 The Author(s). Published by S. Karger AG, Basel

                This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) ( http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission.

                History
                : 5 December 2023
                : 11 April 2024
                : 2024
                Page count
                Figures: 3, Tables: 4, References: 36, Pages: 13
                Funding
                This work was supported by Research Programs funded by the Korea Disease Control and Prevention Agency (2011E3300300, 2012E3301100, 2013E3301600, 2013E3301601, 2013E3301602, 2016E3300200, 2016E3300201, 2016E3300202, 2019E320100, 2019E320101, 2019E320102, 2022-11-007).
                Categories
                Research Article

                aortic artery calcification,cardiovascular disease,coronary artery calcification,kidney transplantation,renal outcome

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