Evidence map›Paper›PMID 39746668›Full record

ArticleAnnals of medicine2025

Predictive risk factors for adverse events during tooth extraction among elderly patients with cardiovascular diseases.

Shaojun Ma, Xu Chen, Yurun Zhai, Xinyi Sun, Jing Sheng, Yun Sun, Haiya Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of 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

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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

7 authors.

Shaojun MaDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xu ChenDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yurun ZhaiDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xinyi SunDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jing ShengDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yun SunDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haiya WangDepartment of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTooth extraction is a risk factor for cardiovascular events, particularly in elderly patients. However, no clinical tool has been developed to date to predict the risk of adverse events (AEs) during tooth extraction. MATERIALS AND

methodsWe prospectively enrolled 774 elderly patients (aged ≥ 60 years) with cardiovascular disease (CVD) who were scheduled to undergo tooth extraction at the dental surgery department of Shanghai Ninth People's Hospital from January 2021 to July 2022. To determine the predictive risk factors for AEs, we collected and recorded 62 factors on general characteristics, clinical information, physical and imaging examinations, psychological tests, perioperative characteristics, and surgical characteristics.

resultsWe used a univariate logistic regression model to explore the 62 potential risk factors and included 21 factors in a multivariate model (all

conclusionsWe developed and validated a predictive model with 11 clinical factors for the AEs during tooth extraction in elderly patients with CVD with well efficiency.

Indexed as

Cardiovascular DiseasesTooth ExtractionAgedAged, 80 and overChinaFemaleHumansLogistic ModelsMaleMiddle AgedPostoperative ComplicationsProspective StudiesRisk AssessmentRisk Factorsadverse eventCardiovascular diseaseselderlyrisk factorstooth extraction

Identifiers

PMID39746668
PMCPMC11703507

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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.