Evidence map›Paper›PMID 40697916›Full record

ArticleFrontiers in medicine2025

Patient-centered integration of the treatment and prevention of cardiovascular diseases in the community: a digital intelligence exploration.

Zhaoyuan Zhou, Xiaotong Gao, Meichen Jiang, Jiachun Wang, Qiang Zhang, Zhen Wang, Zhe Yan, Kai Yu, Xinyue Chen, Xiaoqing He and 1 more

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

11 authors.

Zhaoyuan ZhouDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Xiaotong Gao *Department of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Meichen JiangDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Jiachun WangDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Qiang ZhangDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Zhen WangDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Zhe YanDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Kai YuDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Xinyue ChenDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Xiaoqing HeDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Shuang WangDepartment of General Practice, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We aimed to explore the benefits of the "Digital and Intelligent Patient-centered Integration of Treatment and Prevention for Cardiovascular Diseases in the Community" (DITP), which is a new model for managing cardiovascular disease risk factors in high-risk elderly stroke patients. Methods: A total of 338 elderly high-risk stroke patients were included and subjected to a 6-month self-controlled DITP study. Results: The five chronic diseases with the highest prevalence rates were hypertension (89.94%), dyslipidemia (73.37%), overweight (65.70%), diabetes (52.96%), and atrial fibrillation/valvular heart disease (2.07%). Moreover, 86.7% of patients had two or more simultaneous chronic diseases, and 27.22% had a history of smoking. After 6 months of intervention, (1) with respect to the control of multiple risk factors, there were improvements in blood pressure and blood lipid compliance rates. The blood glucose compliance rate (50.30% preintervention vs. 62.43% postintervention) and the optimal risk factor control rate (3.85% preintervention vs. 8.88% postintervention) were significantly improved (P < 0.05). (2) Regarding physiological indicators, fasting blood glucose (7.44 ± 2.37 mmol/L preintervention vs. 6.99 ± 2.15 mmol/L postintervention), cholesterol (5.56 ± 1.3 mmol/L preintervention vs. 5.20 ± 1.22 mmol/L postintervention), and low-density lipoprotein cholesterol levels significantly decreased ( Conclusion: This DITP model will help actively improve the health of elderly high-risk stroke patients in the long term and should be further applied and promoted in primary healthcare in the future.

Indexed as

digital platformelderly strokegeneral practitionerpatient-centered treatmentprimary healthcare

Identifiers

PMID40697916
PMCPMC12281826

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.