Evidence map›Paper›PMID 31467053›Full record

ArticleBMJ open2019

Using electronic health record data to establish a chronic kidney disease surveillance system in China: protocol for the China Kidney Disease Network (CK-NET)-Yinzhou Study.

Jinwei Wang, Beiyan Bao, Peng Shen, Guilan Kong, Yu Yang, Xiaoyu Sun, Guohui Ding, Bixia Gao, Chao Yang, Minghui Zhao and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

18 citing papers in PubMed, 30 citations in OpenAlex.

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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

12 authors at 4 institutions in 1 country.

Jinwei Wang *Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China.ORCID 0000-0003-0128-8408
Beiyan Bao *Renal Division, Ningbo Yinzhou No. 2 Hospital, Ningbo, Zhejiang, China.
Peng ShenYinzhou District Center for Disease Control and Prevention, Ningbo, China.
Guilan KongCenter for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China.
Yu YangCenter for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China.
Xiaoyu SunCenter for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China.
Guohui DingCollege of Computer Science, Shenyang Aerospace University, Shenyang, China.
Bixia GaoRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Chao YangRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Minghui ZhaoRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Hongbo LinYinzhou District Center for Disease Control and Prevention, Ningbo, China.
Luxia ZhangRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China zhanglx@bjmu.edu.cn.
Peking University · CNNingbo Center for Disease Control and Prevention · CNNingbo No. 2 Hospital · CNShenyang Aerospace University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChronic kidney disease (CKD) is an important public health problem worldwide. However, there are few active disease surveillance systems for it. The China Kidney Disease Network (CK-NET) was established as a comprehensive surveillance system for CKD using various data sources. As part of this, the proposed CK-NET-Yinzhou study aims to build a regional surveillance system in a developed coastal area in China to obtain detailed dynamic information about kidney disease and to improve the ability to manage the disease effectively. METHODS AND ANALYSIS: Yinzhou is a district of Ningbo city, Zhejiang province. The district has a population of more than 1 million. By 2016, 98% were registered in a regional health information system that started in 2009. This system includes administrative databases containing general demographic characteristics, health check information, inpatient and outpatient electronic medical records, health insurance information, disease surveillance and management information, and death certificates. We will use longitudinal individual electronic health record data to identify people with CKD by repeated laboratory measurements and diagnostic codes. We will also evaluate the associated risk factors, prognosis and disease management. An intelligent clinical decision support system (CDSS) will be developed based on clinical guidelines, domain expert knowledge and real-world data, and will be integrated into the hospital information system. ETHICS AND DISSEMINATION: The CK-NET-Yinzhou study has been reviewed and approved by the Peking University First Hospital Ethics Committee. Privacy of local residents registered with the health information system will be tightly protected through the study process. The findings of the study will be disseminated through peer-reviewed journal articles, posters and presentations in national and international scientific conferences, as well as among local practitioners through the CDSS.

Indexed as

Electronic Health RecordsChinaDecision Support Systems, ClinicalHumansPopulation SurveillanceRenal Insufficiency, ChronicRisk Factorschronic kidney diseaseclinical decision support systemelectronic health recordsurveillance system

Identifiers

PMID31467053
PMCPMC6719833
OpenAlexW2970957811

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.