Evidence map›Paper›PMID 39076957›Full record

ArticleReviews in cardiovascular medicine2024

Development and Implementation of a Dynamically Updated Big Data Intelligence Platform Using Electronic Medical Records for Secondary Hypertension.

Nanfang Li, Qing Zhu, Yujie Dang, Yin Zhou, Xintian Cai, Mulalibieke Heizhati, Delian Zhang, Xiaoguang Yao, Qin Luo, Junli Hu and 4 more

Abstract read
In one paragraph

Article in Reviews in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

14 authors.

Nanfang LiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.ORCID https://orcid.org/0000-0003-1505-8566
Qing ZhuHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Yujie DangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Yin ZhouMedical Department, Yidu Cloud (Beijing) Technology Co., Ltd., 100191 Beijing, China.
Xintian CaiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Mulalibieke HeizhatiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Delian ZhangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Xiaoguang YaoHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Qin LuoHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Junli HuHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Guoliang WangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Yingchun WangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Menghui WangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Jing HongHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The accurate identification and diagnosis of secondary hypertension is critical,especially while cardiovascular heart disease continues to be the leading cause of death. To develop a big data intelligence platform for secondary hypertension using electronic medical records to contribute to future basic and clinical research. Methods: Using hospital data, the platform, named Hypertension DATAbase at Urumchi (UHDATA), included patients diagnosed with hypertension at the People's Hospital of Xinjiang Uygur Autonomous Region since December 2004. The electronic data acquisition system, the database synchronization technology, and data warehouse technology (extract-transform-load, ETL) for the scientific research big data platform were used to synchronize and extract the data from each business system in the hospital. Standard data elements were established for the platform, including demographic and medical information. To facilitate the research, the database was also linked to the sample database system, which includes blood samples, urine specimens, and tissue specimens. Results: From December 17, 2004, to August 31, 2022, a total of 295,297 hypertensive patients were added to the platform, with 53.76% being males, with a mean age of 59 years, and 14% with secondary hypertension. However, 75,802 patients visited the Hypertension Center at our hospital, with 43% (32,595 patients) being successfully diagnosed with secondary hypertension. The database contains 1458 elements, with an average fill rate of 90%. The database can continuously include the data for new hypertensive patients and add new data for existing hypertensive patients, including post-discharge follow-up information, and the database updates every 2 weeks. Presently, some studies that are based on the platform have been published. Conclusions: Using computer information technology, we developed and implemented a big database of dynamically updating electronic medical records for patients with hypertension, which is helpful in promoting future research on secondary hypertension.

Indexed as

databaseelectronic medical recordssecondary hypertension

Identifiers

PMID39076957
PMCPMC11263842

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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.