Evidence map›Paper›PMID 38937426›Full record

ArticleDrugs & aging2024

Development of a Predictive Model for Potentially Inappropriate Medications in Older Patients with Cardiovascular Disease.

Chun-Ying Lee, Yun-Shiuan Chuang, Chew-Teng Kor, Yi-Ting Lin, Yu-Hsiang Tsao, Pei-Ru Lin, Hui-Min Hsieh, Mei-Chiou Shen, Ya-Ling Wang, Tzu-Jung Fang and 1 more

Abstract read
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Article in Drugs & aging, 2024. 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

11 authors.

Chun-Ying LeeDepartment of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Yun-Shiuan ChuangDepartment of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Chew-Teng KorBig Data Center, Changhua Christian Hospital, Changhua, Taiwan.
Yi-Ting LinDepartment of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Yu-Hsiang TsaoDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
Pei-Ru LinBig Data Center, Changhua Christian Hospital, Changhua, Taiwan.
Hui-Min HsiehCenter for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan.
Mei-Chiou ShenDepartment of Pharmacy, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Ya-Ling WangDepartment of Pharmacy, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Tzu-Jung FangSchool of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
Yen-Tze LiuBig Data Center, Changhua Christian Hospital, Changhua, Taiwan. 144084@cch.org.tw.ORCID 0000-0002-5922-2295

Funding

Changhua Christian Hospital and Kaohsiung Medical University TB0486
6 · The paper itself

Abstract

backgroundOlder patients with cardiovascular disease (CVD) are highly susceptible to adverse drug reactions due to age-related physiological changes and the presence of multiple comorbidities, polypharmacy, and potentially inappropriate medications (PIMs).

objectiveThis study aimed to develop a predictive model to identify the use of PIMs in older patients with CVD.

methodsData from 2012 to 2021 from the Changhua Christian Hospital Clinical Research Database (CCHRD) and the Kaohsiung Medical University Hospital Research Database (KMUHRD) were analyzed. Participants over the age of 65 years with CVD diagnoses were included. The CCHRD data were randomly divided into a training set (80% of the database) and an internal validation set (20% of the database), while the KMUHRD data served as an external validation set. The training set was used to construct the prediction models, and both validation sets were used to validate the proposed models.

resultsA total of 48,569 patients were included. Comprehensive data analysis revealed significant associations between the use of PIMs and clinical factors such as total cholesterol, glycated hemoglobin (HbA1c), creatinine, and uric acid levels, as well as the presence of diabetes, hypertension, and cerebrovascular accidents. The predictive models demonstrated moderate power, indicating the importance of these factors in assessing the risk of PIMs.

conclusionsThis study developed predictive models that improve understanding of the use of PIMs in older patients with CVD. These models may assist clinicians in making informed decisions regarding medication safety.

Indexed as

Cardiovascular DiseasesPotentially Inappropriate Medication ListAgedAged, 80 and overDatabases, FactualFemaleHumansInappropriate PrescribingMalePolypharmacy

Identifiers

PMID38937426
PMCPMC11322215

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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.