Evidence map›Paper›PMID 40896565›Full record

ArticleJournal of geriatric cardiology : JGC2025

Development of cardiovascular clinical research data warehouse and real-world research.

Dan-Dan Li, Ya-Ni Yu, Zhi-Jun Sun, Chang-Fu Liu, Tao Chen, Dong-Kai Shan, Xiao-Dan Tuo, Jun Guo, Yun-Dai Chen

Abstract read
In one paragraph

Article in Journal of geriatric cardiology : JGC, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Dan-Dan Li *Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Ya-Ni Yu *Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Zhi-Jun SunDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Chang-Fu LiuDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Tao ChenDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Dong-Kai ShanDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Xiao-Dan TuoDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Jun GuoDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
Yun-Dai ChenDepartment of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. Methods: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. Results: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. Conclusion: The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

Identifiers

PMID40896565
PMCPMC12394958

What OpenQuestion holds

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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.