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Clinical Epidemiology of Kidney Disease
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Clinical Epidemiology of Kidney Disease

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: closed (25 April 2023) | Viewed by 5866

Special Issue Editor

Dr. Jinwei Wang

E-Mail Website
Guest Editor
1. Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
2. Peking University Institute of Nephrology, Beijing 100034, China
3. Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
4. Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Ministry of Education of China, Beijing 100034, China
5. Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100730, China
Interests: epidemiology; biostatistics; chronic kidney disease; acute kidney injury; cohort study; prediction model

Special Issue Information

Dear colleagues,

Kidney disease includes a broad spectrum of autoimmune disorders and complications of metabolic and systemetic diseases, as well as kidney injuries caused by toxicity and trauma. In the past two decades, with the establishment of uniform criteria for the definition and staging of chronic kidney disease and acute kidney injury, clinical research for the disease has seen rapid progress, especially in exploring novel biomarkers, testing new treatment agencies and building risk prediction models for the better management of patients and high-risk populations. In addition, with the advent of the big data era, electronic health records—although possibly limited by problems of relevance for study and data quality issues—have shown great potential to faciliate the conduct of both observational “real-world” studies and clinical trials. With all these in mind, this Special Issue in clinical epidemiology of kidney disease invites original research with novel findings in risk factors, diagnosis, treatment, management strategies, and risk prediction models of kidney disease to further advance the development of the field.

Dr. Jinwei Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • chronic kidney disease
  • acute kidney injury
  • randomized clinical trial
  • cohort study
  • comparative effectiveness study
  • biomarkers
  • risk prediction model
  • electronic health records

Published Papers (3 papers)

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Research

11 pages, 609 KiB  
Article
ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study
by Miao Hui, Jun Ma, Hongyu Yang, Bixia Gao, Fang Wang, Jinwei Wang, Jicheng Lv, Luxia Zhang, Li Yang and Minghui Zhao
J. Clin. Med. 2023, 12(4), 1504; https://doi.org/10.3390/jcm12041504 - 14 Feb 2023
Cited by 3 | Viewed by 2077
Abstract
Background and objectives: In light of the growing burden of chronic kidney disease (CKD), it is of particular importance to create disease prediction models that can assist healthcare providers in identifying cases of CKD individual risk and integrate risk-based care for disease progress [...] Read more.
Background and objectives: In light of the growing burden of chronic kidney disease (CKD), it is of particular importance to create disease prediction models that can assist healthcare providers in identifying cases of CKD individual risk and integrate risk-based care for disease progress management. The objective of this study was to develop and validate a new pragmatic end-stage kidney disease (ESKD) risk prediction utilizing the Cox proportional hazards model (Cox) and machine learning (ML). Design, setting, participants, and measurements: The Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE), a multicenter CKD cohort in China, was employed as the model’s training and testing datasets, with a split ratio of 7:3. A cohort from Peking University First Hospital (PKUFH cohort) served as the external validation dataset. The participants’ laboratory tests in those cohorts were conducted at PKUFH. We included individuals with CKD stages 1~4 at baseline. The incidence of kidney replacement therapy (KRT) was defined as the outcome. We constructed the Peking University-CKD (PKU-CKD) risk prediction model employing the Cox and ML methods, which include extreme gradient boosting (XGBoost) and survival support vector machine (SSVM). These models discriminate metrics by applying Harrell’s concordance index (Harrell’s C-index) and Uno’s concordance (Uno’s C). The calibration performance was measured by the Brier score and plots. Results: Of the 3216 C-STRIDE and 342 PKUFH participants, 411 (12.8%) and 25 (7.3%) experienced KRT with mean follow-up periods of 4.45 and 3.37 years, respectively. The features included in the PKU-CKD model were age, gender, estimated glomerular filtration rate (eGFR), urinary albumin–creatinine ratio (UACR), albumin, hemoglobin, medical history of type 2 diabetes mellitus (T2DM), and hypertension. In the test dataset, the values of the Cox model for Harrell’s C-index, Uno’s C-index, and Brier score were 0.834, 0.833, and 0.065, respectively. The XGBoost algorithm values for these metrics were 0.826, 0.825, and 0.066, respectively. The SSVM model yielded values of 0.748, 0.747, and 0.070, respectively, for the above parameters. The comparative analysis revealed no significant difference between XGBoost and Cox, in terms of Harrell’s C, Uno’s C, and the Brier score (p = 0.186, 0.213, and 0.41, respectively) in the test dataset. The SSVM model was significantly inferior to the previous two models (p < 0.001), in terms of discrimination and calibration. The validation dataset showed that XGBoost was superior to Cox, regarding Harrell’s C, Uno’s C, and the Brier score (p = 0.003, 0.027, and 0.032, respectively), while Cox and SSVM were almost identical concerning these three parameters (p = 0.102, 0.092, and 0.048, respectively). Conclusions: We developed and validated a new ESKD risk prediction model for patients with CKD, employing commonly measured indicators in clinical practice, and its overall performance was satisfactory. The conventional Cox regression and certain ML models exhibited equal accuracy in predicting the course of CKD. Full article
(This article belongs to the Special Issue Clinical Epidemiology of Kidney Disease)
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26 pages, 7673 KiB  
Article
Global, Regional, and National Incidence and Disability-Adjusted Life-Years for Urolithiasis in 195 Countries and Territories, 1990–2019: Results from the Global Burden of Disease Study 2019
by Juan Li, Yue Zhao, Zhuang Xiong and Guoqiang Yang
J. Clin. Med. 2023, 12(3), 1048; https://doi.org/10.3390/jcm12031048 - 29 Jan 2023
Cited by 2 | Viewed by 2207
Abstract
Purpose: Urolithiasis is highly prevalent worldwide. The aim of this study was to report the results of the Global Burden of Disease 2019 study on urolithiasis burden estimates grouped by gender, regions, countries or territories, and sociodemographic index (SDI) from 1990 to 2019 [...] Read more.
Purpose: Urolithiasis is highly prevalent worldwide. The aim of this study was to report the results of the Global Burden of Disease 2019 study on urolithiasis burden estimates grouped by gender, regions, countries or territories, and sociodemographic index (SDI) from 1990 to 2019 globally. Methods: We reported detailed estimates and temporal trends of the burden estimates of urolithiasis from 1990 to 2019 in 195 countries and territories and further evaluated the relationship between these estimates and SDI, a composite indicator of income per person, years of education, and fertility as a measurement of country/region socio-economic level. Urolithiasis incidence and disability-adjusted life years by gender, regions, countries or territories, and SDI were reported. The percentage change and estimated annual percentage change of these burden estimates were calculated to quantify temporal trends. Results: From 1990 to 2019, the age-standardized incidence rate (ASIR) and disability-adjusted life years (DALYs) of urolithiasis decreased globally by 0.459% and 1.898% per year, respectively. Such a trend of ASIR was prominently due to the decline in the middle, high-middle, and high SDI countries, including Eastern Asia, high-income Eastern Europe, and high-income North America. During this period, these estimates increased in low and low-middle SDI countries, particularly in South Asia, Andean Latin America, and Western Europe. A decline in DALYs was observed in all SDI countries. An approximate positive linear association existed between the burden estimate’s decreased APC and SDI level, except at the high SDI level. Both males and females showed the same trend. Conclusions: This study provides comprehensive knowledge of the burden estimate of urolithiasis. Although the burden estimates of urolithiasis showed a global decrease during the past 29 years, this progress has yet to be universal; the increasing trends were observed in countries with low and low-middle SDI countries. Research in these countries is needed and helps with the appropriate allocation of health resources for prevention, screening, and treatment strategies. Full article
(This article belongs to the Special Issue Clinical Epidemiology of Kidney Disease)
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9 pages, 1139 KiB  
Article
Association between Urolithiasis and History Proton Pump Inhibitor Medication: A Nested Case-Control Study
by So Young Kim, Dae Myoung Yoo, Woo Jin Bang and Hyo Geun Choi
J. Clin. Med. 2022, 11(19), 5693; https://doi.org/10.3390/jcm11195693 - 26 Sep 2022
Cited by 3 | Viewed by 1214
Abstract
A few retrospective studies have suggested the risk of urolithiasis associated with the use of proton pump inhibitors (PPIs). The current research intended to estimate the risk of urolithiasis according to previous PPI use. A nested case-control study was conducted using the National [...] Read more.
A few retrospective studies have suggested the risk of urolithiasis associated with the use of proton pump inhibitors (PPIs). The current research intended to estimate the risk of urolithiasis according to previous PPI use. A nested case-control study was conducted using the National Health Insurance Service-National Health Screening Cohort in Korea. A total of 28,962 patients with urolithiasis and 115,848 control participants were selected. The previous prescription history of PPI with days of PPI prescription was collected. To calculate the odds ratios (OR) of past, current, and days of PPI use for urolithiasis, logistic regression models were used. Subgroup analyses were conducted. The urolithiasis group demonstrated a higher rate of current PPI users than the control group (60.9% vs. 43.7%). The current PPI users indicated 2.49 times higher odds for urolithiasis than no PPI users (95% confidence intervals [CI] = 2.33–2.66). A longer duration of PPI use was associated with greater odds for urolithiasis (adjusted OR = 1.65 (95% CI = 1.54–1.77) < 1.97 (95% CI = 1.84–2.11) < 2.32 (95% CI = 2.14–2.49) for 1–19 days, 30–364 days, and 365 or more days of PPI prescription). All subgroup analyses described a consistently positive association of previous PPI use with urolithiasis. Prior PPI use was related to a higher risk of urolithiasis. The relationship between previous PPI use and urolithiasis demonstrated a dose-response association. Full article
(This article belongs to the Special Issue Clinical Epidemiology of Kidney Disease)
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