Oral frailty and its influencing factors in elderly patients with multimorbidity: a cross-sectional study

OBJECTIVES: This study aims to investigate the prevalence and influencing factors of oral frailty among elderly patients with multimorbidity. DESIGN: A cross-sectional study. METHODS: Two hundred and thirty-five elderly people with multimorbidity were enrolled in this cross-sectional study in the the Second Affiliated Hospital of Soochow University from March 2024 to December 2024. A series of questionnaires were conducted: the Oral Frailty Index-8 (OFI-8), the Family Assessment Device (APGAR), the Nutritional Risk Screening 2002 (NRS 2002), the Barthel Index, the Charlson Comorbidity Index (CCI), the Groningen Frailty Indicator (GFI), and the Oral Health Assessment Tool (OHAT). Independent t test, chi-square tests, Mann-Whitney U test, binary logistic regression, spearman correlation coeffcients, and receiver operating characteristic (ROC) curve analysis were used to analyze the data. RESULTS: In this study, the prevalence of oral frailty among elderly patients with multimorbidity was 78.3%. The number of missing teeth, the number of readmission within one year, comorbidity degree, frailty, and dry mouth were the influencing factors of oral frailty (P < 0.05). The evaluation of the prediction results showed that the frailty (area under the curve [AUC]: 0.727, 95% confidence interval [CI]: 0.656-0.798), the number of missing teeth (area under the curve [AUC]: 0.694, 95% confidence interval [CI]: 0.615-0.773), the dry mouth (area under the curve [AUC]: 0.652, 95% confidence interval [CI]: 0.565-0.739), the number of readmission within one year (area under the curve [AUC]: 0.649, 95% confidence interval [CI]: 0.565-0.732), and the comorbidity degree (area under the curve [AUC]: 0.617, 95% confidence interval [CI]: 0.525-0.710) had a higher predictive value for OF. CONCLUSION: The prevalence of oral frailty among elderly patients with multimorbidity was high. The elderly who have a higher frequency of readmission within one year, missing more teeth, severe comorbidity degree, severe frailty degree, and dry mouth symptoms were more prone to oral frailty. Clinical practitioners should screen and intervene for this group. They should actively provide oral health knowledge, formulate personalized management plans, and work to prevent and control oral frailty, thereby improving the patients' overall quality of life.

Authors

Institutions

Publication Details

Journal
BMC Oral Health
Published
2026-06-26
DOI
https://doi.org/10.1186/s12903-026-09060-5
Primary Topic
Frailty in Older Adults
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Oral frailty and its influencing factors in elderly patients with multimorbidity: a cross-sectional study

Xingxin Wang, Yun Guo, Lin Xu, Yuxing Wang et al.
BMC Oral Health
Frailty in Older Adults
article

Oral frailty and its influencing factors in elderly patients with multimorbidity: a cross-sectional study

Xingxin Wang, Yun Guo, Lin Xu, Yuxing Wang, Kaiyue Zhi, Meifang Zhou, X Y Zhou
article en

Abstract

OBJECTIVES: This study aims to investigate the prevalence and influencing factors of oral frailty among elderly patients with multimorbidity. DESIGN: A cross-sectional study. METHODS: Two hundred and thirty-five elderly people with multimorbidity were enrolled in this cross-sectional study in the the Second Affiliated Hospital of Soochow University from March 2024 to December 2024. A series of questionnaires were conducted: the Oral Frailty Index-8 (OFI-8), the Family Assessment Device (APGAR), the Nutritional Risk Screening 2002 (NRS 2002), the Barthel Index, the Charlson Comorbidity Index (CCI), the Groningen Frailty Indicator (GFI), and the Oral Health Assessment Tool (OHAT). Independent t test, chi-square tests, Mann-Whitney U test, binary logistic regression, spearman correlation coeffcients, and receiver operating characteristic (ROC) curve analysis were used to analyze the data. RESULTS: In this study, the prevalence of oral frailty among elderly patients with multimorbidity was 78.3%. The number of missing teeth, the number of readmission within one year, comorbidity degree, frailty, and dry mouth were the influencing factors of oral frailty (P < 0.05). The evaluation of the prediction results showed that the frailty (area under the curve [AUC]: 0.727, 95% confidence interval [CI]: 0.656-0.798), the number of missing teeth (area under the curve [AUC]: 0.694, 95% confidence interval [CI]: 0.615-0.773), the dry mouth (area under the curve [AUC]: 0.652, 95% confidence interval [CI]: 0.565-0.739), the number of readmission within one year (area under the curve [AUC]: 0.649, 95% confidence interval [CI]: 0.565-0.732), and the comorbidity degree (area under the curve [AUC]: 0.617, 95% confidence interval [CI]: 0.525-0.710) had a higher predictive value for OF. CONCLUSION: The prevalence of oral frailty among elderly patients with multimorbidity was high. The elderly who have a higher frequency of readmission within one year, missing more teeth, severe comorbidity degree, severe frailty degree, and dry mouth symptoms were more prone to oral frailty. Clinical practitioners should screen and intervene for this group. They should actively provide oral health knowledge, formulate personalized management plans, and work to prevent and control oral frailty, thereby improving the patients' overall quality of life.

BMC Oral Health
Soochow University (CN), Second Affiliated Hospital of Soochow University (CN)
Zero hunger
Openalex Percentile: Top 9%
Frailty in Older Adults
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.