Evidence map›Paper›PMID 40287639›Full record

ArticleBMC geriatrics2025

Predictive model to identify multiple synergistic effects of geriatric syndromes on quality of life in older adults: a hospital-based pilot study.

Chien-Chou Su, Yung-Chen Yu, Deng-Chi Yang

Abstract read
In one paragraph

Article in BMC geriatrics, 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. Review
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

3 authors.

Chien-Chou SuClinical Innovation and Research Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Yung-Chen YuDepartment of Nursing, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Deng-Chi YangDepartment of Geriatrics and Gerontology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138 Sheng Li Road, Tainan City, 704, Tainan, Taiwan. stethoscope@hotmail.com.

Funding

National Cheng Kung University Hospital NCKUH-V101-10
6 · The paper itself

Abstract

backgroundQuality of life (QOL) has been reported to be associated with sociodemographic characteristics and geriatric syndromes in older adults, but the impact of interactions among multiple geriatric syndromes on QOL remains unexplored. We aimed to apply a machine learning method to evaluate the effects of interactions among multiple geriatric syndromes on QOL in older adults.

methodsWe recruited adults aged ≥ 65 years admitted to a tertiary medical center from June 2018 to September 2018. The main outcome was the three-level five-dimensional Euro-Quality of Life tool (EQ-5D-3 L) utility value. The random forest algorithm was used to identify and rank the strongest predictors of geriatric syndromes. The relation between predictors and outcomes was visualized with accumulated local effects plots and interaction effects. Model performance was evaluated by 5-fold cross-validation with metrics of R-square, the mean square error of estimation and the mean absolute error of estimation.

resultsThe study included 160 older adults with a mean age of 79 years. The top ten features that significantly influenced the utility prediction were activities of daily living (ADL), frailty, pain, the number of medications used, age, depression, the Charlson Comorbidity Index (CCI), body mass index (BMI), peptic ulcer, and emotional loneliness. The two-way interactions between ADL, frailty, and pain significantly interacted with other predictors.

conclusionADL, frailty, and pain are important factors to be considered when assessing QOL in older adults. It is important for clinicians to consider them together in clinical decision-making.

Indexed as

Activities of Daily LivingGeriatric AssessmentQuality of LifeAgedAged, 80 and overFemaleFrailtyHumansMachine LearningMalePilot ProjectsSyndromeGeriatric syndromesMachine learningOlder adultsQuality of life

Identifiers

PMID40287639
PMCPMC12032690

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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.