Evidence map›Paper›PMID 41281726›Full record

ArticleAmerican journal of preventive cardiology2025

Predictive value of different long-term (10-year) blood pressure parameters variability in cardiovascular disease: A prospective cohort study.

Yixiu Chen, Hongmin Liu, Mo Zhang, Zhihui Liu, Qian Liu, Shihe Liu, Wenchao Yao, Yajing Liang, Yicheng Liao, Yufeng Bian and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of preventive cardiology, 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. Trial
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.

Yixiu ChenDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Hongmin LiuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Mo ZhangDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Zhihui LiuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Qian LiuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Shihe LiuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Wenchao YaoSchool of Public Health, Hebei Key Laboratory for Organ Fibrosis Research, North China University of Science and Technology, Tangshan, Hebei, China.
Yajing LiangDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Yicheng LiaoDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Yufeng BianDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Renjie FuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Shuohua ChenDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Shouling WuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.
Yuntao WuDepartment of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: We aimed to compare the predictive value of long-term variability in systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) over a 10-year period for cardiovascular diseases (CVDs). Methods: A prospective cohort of 44,938 participants were categorized into four groups based on the quartiles of variability of SBP, DBP, and PP. Cox proportional hazards regression models were applied to assess the impact of SBP, DBP, and PP variability on CVDs. The predictive value of SBP, DBP, and PP variability for CVDs and specific cardiovascular outcomes was evaluated based on the China-PAR model. Results: During a mean follow-up period of 5.04 years, compared to the Q1, the HR and 95 %CI of CVDs was 1.28 (1.11,1.48) in the Q4 of SBP, 1.33 (1.17,1.51) in the Q4 of DBP, and 1.25 (1.06,1.47) in the Q4 of PP (all Conclusion: Increases in SBP, DBP, and PP variability independently elevate the risk of CVDs and its subtypes, regardless of absolute blood pressure levels. Furthermore, SBP, DBP, and PP variability can improve the predictive value of the China-PAR model for CVDs risk, with SBP variability demonstrating the strongest predictive capacity.

Indexed as

10-yearBlood pressure variabilityCardiovascular DiseasesComparePredictive value

Identifiers

PMID41281726
PMCPMC12630324

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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.