Evidence map›Paper›PMID 36829751›Full record

ArticleBioengineering (Basel, Switzerland)2023

A Novel and Noninvasive Risk Assessment Score and Its Child-to-Adult Trajectories to Screen Subclinical Renal Damage in Middle Age.

Chen Chen, Guanzhi Liu, Chao Chu, Wenling Zheng, Qiong Ma, Yueyuan Liao, Yu Yan, Yue Sun, Dan Wang, Jianjun Mu

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Chen ChenDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Guanzhi LiuDepartment of Orthopedics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Chao ChuDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Wenling ZhengDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Qiong MaDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Yueyuan LiaoDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Yu YanDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Yue SunDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Dan WangDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Jianjun MuDepartment of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.

Funding

Clinical Research Award of the First Affiliated Hospital of Xi'an Jiaotong University XJTU1AF-CRF-2019-004International Joint Research Center for Cardiovascular Precision Medicine of Shaanxi Province 2020GHJD-14National Natural Science Foundation of China 82070437National Natural Science Foundation of China 82200472the Chinese Academy of Medical Sciences & Peking Union Medical College 2017-CXGC03-2the Key Project of the Ministry of Science and Technology of China 2017YFC0211703
6 · The paper itself

Abstract

This study aimed to develop a noninvasive, economical and effective subclinical renal damage (SRD) risk assessment tool to identify high-risk asymptomatic people from a large-scale population and improve current clinical SRD screening strategies. Based on the Hanzhong Adolescent Hypertension Cohort, SRD-associated variables were identified and the SRD risk assessment score model was established and further validated with machine learning algorithms. Longitudinal follow-up data were used to identify child-to-adult SRD risk score trajectories and to investigate the relationship between different trajectory groups and the incidence of SRD in middle age. Systolic blood pressure, diastolic blood pressure and body mass index were identified as SRD-associated variables. Based on these three variables, an SRD risk assessment score was developed, with excellent classification ability (AUC value of ROC curve: 0.778 for SRD estimation, 0.729 for 4-year SRD risk prediction), calibration (Hosmer-Lemeshow goodness-of-fit test

Indexed as

group-based trajectory modelingmachine learningrisk assessment toolscreening strategysubclinical renal damage

Identifiers

PMID36829751
PMCPMC9952229

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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.