Evidence map›Paper›PMID 40251528›Full record

ArticleBMC cardiovascular disorders2025

Use of pulse pressure index for cardiovascular outcomes assessment and development of a coronary heart disease model for the elderly.

Luo-Xiang Fang, Yi-Hao Wu, Tao Yao, Zhe-Ning Wang, Sang Qian, Ting Jiang, Jing Xu, Yuan-Nan Lin, Yue-Chun Li

Abstract read
In one paragraph

Article in BMC cardiovascular disorders, 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. Article
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.

Luo-Xiang Fang *Department of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Yi-Hao Wu *Department of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Tao YaoDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Zhe-Ning WangDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Sang QianDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Ting JiangDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Jing XuDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Yuan-Nan LinDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Yue-Chun LiDepartment of Cardiology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China. liyuechun1980@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsPulse pressure has been commonly used to assess atherosclerosis and cardiovascular outcomes with the defects of fluctuation. However, the pulse pressure index (PPI), a feasible alternative with the advantage of lower fluctuations, has not been sufficiently researched.

methodsThis study included 10,796 participants over 65 years from the National Health and Nutrition Examination Survey 1999-2018. Cox proportional hazards models, restricted cubic splines and subgroup analysis were used to investigate the association between PPI in the elderly and all-cause and cardiovascular mortality. Subsequently, a prediction model for identifying coronary heart disease (CHD) in elderly individuals was developed using three machine learning algorithms. The impact of each feature on CHD was visualized in the optimal model after comparing the performances of the models.

resultsThis study discovered that the highest levels of PPI were associated with a 28% increased all-cause [HR (95%CI) 1.28 (1.16, 1.42), P < 0.001] and a 36% increased risk of cardiovascular mortality [HR (95%CI) 1.36 (1.08, 1.72), P = 0.008] compared with the first quantile of PPI. The nonlinear relationships between PPI and mortality (all-cause: P for Nonlinear = 0.038; cardiovascular: P for Nonlinear = 0.005) were determined using restricted cubic spline curves. Among the three machine-learning models, random forest model showed the best performance (AUC 0.667 (0.638, 0.696)). In descending order of feature importance, PPI came in second place, with a positive relationship with CHD.

conclusionsThis study indicated a positive correlation between PPI and long-term adverse cardiovascular outcomes among the elderly. Notably, PPI has considerable predictive power for recognizing CHD.

Indexed as

Blood PressureCoronary DiseaseAgedAged, 80 and overAge FactorsCross-Sectional StudiesFemaleHeart Disease Risk FactorsHumansMachine LearningMaleNutrition SurveysPredictive Value of TestsPrognosisRisk AssessmentRisk FactorsAll-cause mortalityCardiovascular mortalityCoronary heart diseaseMachine learningNHANESPulse pressure index

Identifiers

PMID40251528
PMCPMC12007300

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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.