Evidence map›Paper›PMID 34988199›Full record

ArticleAnnals of translational medicine2021

Prediction models and nomograms of 3-year risk of chronic kidney disease in China: a study from the Shanghai Suburban Adult Cohort and Biobank (2016-2020).

Yuting Yu, Qi Zhao, Yonggen Jiang, Na Wang, Xing Liu, Yun Qiu, Junjie Zhu, Xin Tong, Shuheng Cui, Genming Zhao

Abstract read
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Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

10 authors.

Yuting Yu *Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Qi Zhao *Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Yonggen JiangCenter for Disease Control and Prevention of Songjiang, Shanghai, China.
Na WangDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Xing LiuDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Yun QiuDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Junjie ZhuDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Xin TongDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Shuheng CuiDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Genming ZhaoDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) is a serious public health problem in China that requires the development and verification of sex-specific 3-year risk prediction models and nomograms of CKD to further guide personalized care.

methodsA 3-year community-based observational cohort study of 10,049 Chinese participants without CKD was begun in 2016 and participants were followed until August 2020. Stepwise multivariable-adjusted Cox regression analyses were conducted to select the candidate variables, including demographics and clinical parameters such as blood urea nitrogen (BUN) and estimated glomerular filtration rate (eGFR), into the prediction model. We used the C-statistic to evaluate discrimination, and the Brier score for calibration. A 10-fold cross-validation was conducted for internal validation to assess the model's stability.

resultsThe cumulative incidence of CKD was 4.25% (male: 3.81%, female: 4.55%). The eGFR, HbA1c variability, uric acid (UA), UA variability, BUN, albumin, and Hb were significant predictors for both sexes. In the female model, age, triglycerides and age at menarche were additional predictors. The models showed C-statistics of 0.934/0.951 (male/female). The model calibrated well across the deciles of predicted risk, with a Brier score of 0.007/0.009 (male/female).

conclusionsIn this study, we fitted the CKD 3-year risk prediction models with an accuracy rate of >90%. At the same time, we developed two nomograms to facilitate routine CKD risk prediction to provide individualized care in preventing or delaying CKD.

Indexed as

Chronic kidney disease (CKD)nomogramsrisk prediction models

Identifiers

PMID34988199
PMCPMC8667118

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.