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Updated: May 27, 2025
Oral Health Assessment by Lay Personnel for Older Adults
Published on: February 2, 2020
Development and validation of risk-predicting model for oral frailty in older adults patients with stroke
Ruirui Ma1, Xiaoli Fan2, Xiubin Tao1
1Department of Neurology, The First Affiliated Hospital of Wannan Medical College, Wuhu, China.
Objective:
This study aimed to construct a nomogram predicting oral frailty risk in Older adults patients with stroke.
Design:
A cross-sectional study.
Methods:
We selected 664 hospitalised older adults patients with stroke in a tertiary hospital from January 2023 to March 2024. Among them, 451 cases from January to December 2023 formed the modelling group, and 213 cases from January to March 2024 served as the validation group. Univariate and multivariate logistic regression analyses identified independent risk factors for oral frailty. A nomogram was developed and visualised using columnar charts. The model's predictive performance was assessed using the receiver operating characteristic curve and the Hosmer-Lemeshow test.
Findings:
The prevalence of oral frailty was 47.7% in the modelling group and 47.9% in the validation groups, respectively. Age (OR = 10.351), frailty (OR = 9.171), number of comorbidities (OR = 11.301), nutritional risk (OR = 17.419), NIHSS (OR = 13.234), oral health evaluation index (OR = 0.316), Barthel index (OR = 0.247) were identified as significant independent risk factors. The areas under the receiver operating characteristic curve for the modelling and validation groups were 0.945 and 0.915, respectively.
Conclusion:
A high prevalence of oral frailty was observed among Older adults patients with stroke with diverse risk factors. The nomogram provides an effective screening tool for identifying patients at high risk of oral frailty early in their hospital stay. The risk-prediction model showed good predictive efficacy and clinical utility.This study introduces a nomogram to predict oral frailty and identify associated risk factors in Older adults patients with stroke early on. It supports personalised care and precision medicine approaches in clinical practice.
