Abstract
Objective
To analyze the influencing factors of oral frailty in Elderly hospitalized patients, develop a nomogram-based risk assessment model, and validate its assessment performance.
Methods
A total of 664 Elderly patients were selected from a tertiary hospital in Tianjin between January 2024 and October 2024. Data were collected using a general information questionnaire, the Oral Frailty Screening Scale, the Frailty Scale, the Social Support Rating Scale, the Mini-Nutritional Assessment Short Form, and the Pittsburgh Sleep Quality Index. Patients were categorized based on oral frailty severity. Chi-square tests and logistic regression analyses were used to identify influencing factors. Nomogram-based assessment tool was constructed using R software (version 4.2.2). Model performance was evaluated using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve. Internal validation was performed via bootstrap resampling, and calibration curves were generated. Clinical decision curve analysis was also conducted.
Results
The prevalence of oral frailty among Elderly patients was 26.6%. Multivariate analysis identified age, number missing of teeth, denture usage, social support score, gender, residential environment, subjective masticatory difficulty, xerostomia, dietary preferences, frailty severity, and nutritional status as independent risk factors (all P < 0.05). The nomogram model demonstrated an area under the curve (AUC) of 0.878 (95% CI: 0.845–0.913, P < 0.001), with sensitivity and specificity of 0.800 and 0.806, respectively. The Hosmer-Lemeshow χ² value was 10.966 (P = 0.372). In the internal validation cohort, the AUC was 0.918 (95% CI: 0.876–0.959, P < 0.001), with sensitivity and specificity of 0.842 and 0.849, respectively (Hosmer-Lemeshow χ² = 6.747, P = 0.881). Decision curve analysis indicated significant net clinical benefits within specific threshold ranges, supporting the model’s clinical utility.
Conclusion
The prevalence of oral frailty in Elderly hospitalized patients is notably high and influenced by multiple factors, including age, dental status, social support, gender, environmental factors, masticatory function, and nutritional status. the risk assessment model has good discriminative value for identifying oral frailty in hospitalized elderly patients.
Keywords: Oral frailty, Elderly patients, Aassessment model, Risk factors, Hospitalized patients
Across the globe, nations are experiencing a rapid demographic transition marked by a sustained increase in the proportion of older adults. According to World Health Organization (WHO) estimates, the number of individuals aged 80 years and older is projected to triple, reaching approximately 434 million by 2050—a trend with profound socioeconomic implications [1]. In older adults, advanced age is associated with an elevated burden of health challenges, including a higher prevalence of chronic diseases, multimorbidity [2, 3], and an increased risk of frailty [4]. Frailty is a distinct clinical syndrome characterized by reduced physiological reserve and heightened vulnerability to stressors. This condition is clinically significant due to its strong association with adverse outcomes such as falls, cognitive impairment, functional decline, and mortality [5–8].
Given the multidimensional and multisystemic nature of frailty, researchers and clinicians have expanded the frailty construct into several domains, including physical [9], cognitive [10], psychological, and social phenotypes [11]. However, there is no universal consensus on the prioritization of these domains, and several related issues remain subjects of ongoing debate [12]. In this context, the concept of oral frailty—a relatively novel geriatric syndrome proposed by Tanaka et al.—has gained increasing recognition. This construct has particular relevance amid the growing population of frail older adults and has been increasingly adopted in recent years [13].
Poor oral health status and declining oral function are highly prevalent among older adults [14–16] and have been linked to suboptimal nutritional status and chronic diseases [17, 18], as well as to various facets of physical frailty. A systematic review established a longitudinal association between oral health deficits—such as reduced tooth retention and impaired oral function—and the onset of frailty in older adults [19]. Acknowledging the progressive nature of oral health decline, Tanaka defined oral frailty as the age-related deterioration of oral functions, including mastication, swallowing, oral motor skills, and tongue pressure. Their work identified oral frailty as a significant associated factor of physical frailty, sarcopenia, disability, and mortality [13]. Moreover, another systematic review indicated that indicators of oral frailty can significantly predict major adverse health-related outcomes in older adults, including mortality, functional decline, weight loss, hospitalization, and falls [20]. These findings highlight the importance of early detection and monitoring of oral frailty, particularly among hospitalized geriatric patients.
Nomograms serve as graphical calculation tools that translate statistical models into accessible instruments for estimating individualized probabilities of clinical events, such as mortality or disease recurrence [21, 22]. They are widely used in clinical settings for risk prediction in conditions such as frailty [23] and COVID-19 pneumonia [24]. However, to date, few studies have explored risk factors for oral frailty specifically in hospitalized older adults. Therefore, this study aims to identify risk factors associated with oral frailty and to develop a nomogram for assessing its occurrence in Chinese hospitalized geriatric patients. We seek to evaluate the prevalence of oral frailty and analyze its contributing factors. Using logistic regression analysis, we will construct a visual nomogram to assist healthcare providers in the early identification of oral frailty risk, thereby supporting clinical decision-making and guiding targeted interventions.
Materials and methods
Study design and participants
This cross-sectional study was conducted in the Geriatrics Department of a tertiary hospital in Tianjin, China, with continuous data collection from January 2024 to October 2024. Inclusion criteria were: (1) age 60 years or older; (2) hospitalized patients regardless of admission diagnosis; and (3) provision of written informed consent by either the patient or their primary caregiver (typically a family member). Exclusion criteria included: (1) patients unable to cooperate due to medical conditions; and (2) patients with severe cognitive impairment or psychiatric disorders. The studies involving humans were approved by the Medical Ethics Committee at Tianjin Fifth Central Hospital(Approval Notice No. WZX-EC-KY2025025).
Sample size calculation
The sample size was determined to ensure adequate statistical power for multivariable logistic regression analysis based on the events-per-variable (EPV) criterion [25]. Considering the anticipated prevalence of oral frailty (approximately 26.6% based on previous literature or pilot study) and the inclusion of 8 independent variables in the final model, a minimum of 80 outcome events was required to achieve an EPV of 10. To obtain at least 80 events, the required total sample size was calculated as 80 events / 0.266 ≈ 300 participants. To account for potential missing data and improve model stability, we ultimately enrolled 664 participants. This sample yielded 177 outcome events, resulting in an EPV of 22.1 (177 events / 8 variables), which substantially exceeds the recommended minimum threshold of 10–15 EPV, ensuring stable and reliable parameter estimates.
General clinical characteristics
Demographic and clinical data were collected for all participants, including age, gender, height, body mass index (BMI), number missing of teeth, number of denture, educational background, annual personal income, smoking/alcohol history, dietary preferences, residence (urban/rural), living alone status, comorbidities, xerostomia, subjective chewing difficulties, and social support. Trained research nurses administered a structured questionnaire to collect these data, with primary caregivers assisting in responses during hospitalization when necessary.
Oral frailty assessment
Oral frailty was assessed using the 8-item Oral Frailty Screening Scale (OF-8) developed by Tanaka et al. [26]. This instrument evaluates five domains: masticatory ability, swallowing function, denture use, social participation, and oral health behaviors. The total score ranges from 0 to 11, with higher scores indicating poorer oral health status. A cutoff score of ≥ 4 was used to define oral frailty. In this study, the scale demonstrated good internal consistency (Cronbach’s α = 0.720).
Frailty evaluation
Frailty status was determined using the FRAIL scale (Fatigue, Resistance, Ambulation, Illnesses, and Loss of weight) [27]. This 5-item instrument classifies participants as frail (score ≥ 3), pre-frail (score 1–2), or robust (score 0). While valued for its brevity and predictive validity, cultural adaptation was implemented for local context. The scale showed excellent reliability in our sample (Cronbach’s α = 0.818).
Social support measurement
Social support was evaluated using the Social Support Rating Scale (SSRS) developed by Xiao Shuiyuan [28]. This 10-item instrument measures three dimensions: objective support (3 items, score range 1–16), subjective support (4 items, 4–16), and support utilization (3 items, 3–12). Higher total scores indicate stronger social support. The scale demonstrated high reliability (Cronbach’s α = 0.894–0.940) and good construct validity (subscale-total correlations: 0.724–0.835).
Nutritional status assessment
Nutritional risk was screened using the Mini Nutritional Assessment-Short Form (MNA-SF) [29]. This 6-item tool evaluates BMI, weight loss, acute disease/stress, mobility, neuropsychological problems, and food intake. Scores range from 0 to 14, with scores < 11 indicating malnutrition risk. To minimize the influence of the inpatient environment and standardized hospital diets, all assessments were conducted within 48 h of admission, reflecting the participants’ baseline nutritional status prior to the current hospitalization.
Sleep quality evaluation
Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) [30]. This 19-item instrument assesses seven components: subjective sleep quality, sleep latency, duration, habitual efficiency, disturbances, medication use, and daytime dysfunction. Global scores (0–21) were categorized as excellent (0–5), good (6–10), moderate (11–15), or poor (16–21) sleep quality. A cutoff > 7 defined sleep disturbance in this study.
Statistical analysis
Statistical procedures were distributed between two platforms to leverage their respective strengths: IBM SPSS Statistics (Version 26.0) was used for baseline descriptive statistics, univariate comparisons, and the development of the logistic regression model. R software (Version 4.4.1)—specifically the rms, foreign, and regplot packages—was utilized for constructing the nomogram, calculating the C-index, and generating calibration curves for model validation. Continuous variables were expressed as mean ± standard deviation (SD) or medians with interquartile ranges (IQR) based on their distribution, while categorical variables were reported as frequencies and percentages. Missing data were addressed using a complete-case analysis approach, as the proportion of missing values for all key variables was less than 5%, ensuring the integrity of the statistical inference. To delineate the baseline profile of oral frailty, inter-group comparisons were conducted using independent-sample t-tests, Mann-Whitney U tests, chi-square tests, or Fisher’s exact tests as dictated by the data type and distribution. To develop and validate the risk assessment model, the total sample (N = 664) was randomly partitioned into a development set (n = 465, 70%) and a testing set (n = 199, 30%) using a computer-generated random seed. All potential risk factors identified as statistically significant (P < 0.05) in the univariate analysis were included in a multivariate logistic regression analysis, employing the forward likelihood ratio (LR) method to isolate independent correlates of oral frailty. Prior to model inclusion, multicollinearity among candidate predictors was rigorously assessed using the Variance Inflation Factor (VIF), with a threshold of VIF < 5 indicating the absence of significant redundancy. The independent factors derived from the regression analysis were subsequently integrated to construct a visual nomogram for risk identification. The discriminative performance of the nomogram was quantified by the concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) in both the development and testing sets [31]. To adjust for potential over-optimism and ensure the stability of the model’s coefficients, internal validation was performed via bootstrap resampling with 1,000 replicates to calculate the C-index. Model calibration was evaluated using calibration curves and the Hosmer-Lemeshow goodness-of-fit test to assess the agreement between identified probabilities and observed outcomes. Two-tailed P < 0.05 defining statistical significance. This study strictly adhered to the TRIPOD (Transparent Reporting of a multivariable assessment model) guidelines.
Results
Comparison of baseline characteristics and outcomes between development and validation cohorts
A total of 664 hospitalized older adults were included in this study. Participants were divided into a development cohort (n = 465) and a validation cohort (n = 199). Oral frailty was identified in 124 individuals (26.67%) in the development cohort and 53 (26.63%) in the validation cohort, with no significant difference in incidence between the two cohorts (P > 0.05). Baseline characteristics were comparable between the cohorts, as all measured variables showed no statistically significant differences (all P > 0.05),as shown in Table 1.
Table 1.
Baseline characteristics of development and validation cohorts
| Variables | Development cohort (n = 465) | Validation cohort (n = 199) | Statistic | P |
|---|---|---|---|---|
| Age (year), median [IQR] | 83.00 (72.00, 91.00) | 82.00 (75.50, 91.00) | Z=-0.21 | 0.832 |
| Height (cm), median [IQR] | 169.00 (162.00, 175.00) | 167.00 (161.00, 176.00) | Z=-0.31 | 0.754 |
| BMI (kg/m2), median [IQR] | 23.00 (20.00, 26.00) | 23.00 (19.50, 26.00) | Z=-0.57 | 0.571 |
| Number of missing teeth, median [IQR] | 1.00 (0.00, 2.00) | 1.00 (0.00, 2.00) | Z=-0.12 | 0.901 |
| Number of dentures, median [IQR] | 3.00 (1.00, 4.00) | 2.00 (1.00, 4.00) | Z=-0.47 | 0.640 |
| Social support, median [IQR] | 31.00 (27.00, 36.00) | 32.00 (27.00, 35.00) | Z=-0.13 | 0.900 |
| Gender, n (%) | χ²=0.21 | 0.647 | ||
| Female | 227 (48.82) | 101 (50.75) | ||
| Male | 238 (51.18) | 98 (49.25) | ||
| Residence, n (%) | χ²=0.17 | 0.685 | ||
| town | 244 (52.47) | 101 (50.75) | ||
| rural aera | 221 (47.53) | 98 (49.25) | ||
| Annual household income, n (%) | χ²=0.46 | 0.793 | ||
| > 30,000 | 86 (18.49) | 41 (20.60) | ||
| 10,000 ~ 30,000 | 185 (39.78) | 79 (39.70) | ||
| < 10,000 | 194 (41.72) | 79 (39.70) | ||
| Smoke, n (%) | χ²=0.00 | 0.959 | ||
| No | 230 (49.46) | 98 (49.25) | ||
| Yes | 235 (50.54) | 101 (50.75) | ||
| Drink, n (%) | χ²=1.54 | 0.215 | ||
| No | 207 (44.52) | 99 (49.75) | ||
| Yes | 258 (55.48) | 100 (50.25) | ||
| Standard of culture, n (%) | χ²=2.13 | 0.546 | ||
| Junior high school and below | 83 (17.85) | 43 (21.61) | ||
| Secondary / high school | 101 (21.72) | 41 (20.60) | ||
| College for professional training | 199 (42.80) | 76 (38.19) | ||
| Bachelor degree and above | 82 (17.63) | 39 (19.60) | ||
| Live alone, n (%) | χ²=1.22 | 0.270 | ||
| No | 266 (57.20) | 123 (61.81) | ||
| Yes | 199 (42.80) | 76 (38.19) | ||
| Comorbidity, n (%) | χ²=0.17 | 0.684 | ||
| < 2 | 100 (21.51) | 40 (20.10) | ||
| ≥2 | 365 (78.49) | 159 (79.90) | ||
| Subjective chewing difficulty, n (%) | χ²=0.45 | 0.502 | ||
| No | 200 (43.01) | 80 (40.20) | ||
| Yes | 265 (56.99) | 119 (59.80) | ||
| Xerostomia, n (%) | χ²=0.48 | 0.487 | ||
| No | 274 (58.92) | 123 (61.81) | ||
| Yes | 191 (41.08) | 76 (38.19) | ||
| Eating habits, n (%) | χ²=1.09 | 0.580 | ||
| Light diet | 263 (56.56) | 112 (56.28) | ||
| Salty | 124 (26.67) | 59 (29.65) | ||
| Sweet | 78 (16.77) | 28 (14.07) | ||
| Physical frailty, n (%) | χ²=0.42 | 0.812 | ||
| Non-frailty | 133 (28.60) | 55 (27.64) | ||
| Pre-frailty | 185 (39.78) | 76 (38.19) | ||
| Frailty | 147 (31.61) | 68 (34.17) | ||
| Nutritional status, n (%) | χ²=0.00 | 0.952 | ||
| Normal | 312 (67.10) | 134 (67.34) | ||
| Malnutrition | 153 (32.90) | 65 (32.66) | ||
| Sleep quality, n (%) | χ²=0.41 | 0.520 | ||
| Normal | 244 (52.47) | 99 (49.75) | ||
| Poor | 221 (47.53) | 100 (50.25) |
Continuous variables are expressed as mean ± standard deviation or median [interquartile range] based on normality tests; Categorical variables are expressed as frequencies (percentages). P-values represent the comparison between the development and testing sets using independent t-tests, Mann-Whitney U tests, or chi-square tests
Associated factors for oral frailty in hospitalized elderly patients
The development cohort was stratified into oral frailty and non-oral frailty status for univariate analysis of potential risk factors. Significant differences were observed between the oral frailty status in the following variables: advanced age, greater number of missing teeth, higher denture usage, lower social support scores, gender, residence, subjective chewing difficulty, xerostomia, dietary preferences, frailty status, and nutritional status (all P < 0.05). No significant differences were found in other clinical parameters (all P > 0.05; see Table 2).Subsequently, multivariate logistic regression analysis was performed incorporating the above 11 factors as independent variables, with the occurrence of oral frailty as the dependent variable. The results identified the following factors as independent correlates of oral frailty in Elderly patients: age, Number of missing teeth, number of dentures, social support, subjective masticatory difficulty, dry mouth, dietary habits, and frailty status (all P < 0.05), as detailed in Table 3. The collinearity diagnostic results showed that the VIF values for all included variables < 5, indicating that there was no multi-collinearity among the independent variables.
Table 2.
Univariate analysis of factors associated with oral frailty in Elderly inpatients in the development cohort
| Variables | Non-oral frailty (n = 343) | Oral frailty (n = 121) | Statistic | P |
|---|---|---|---|---|
| Age (year), median [IQR] | 80.00 (70.00, 89.00) | 87.00 (80.00, 93.00) | Z=-6.08 | < 0.001 |
| Height (cm), median [IQR] | 168.00 (162.00, 175.50) | 169.00 (161.00, 177.00) | Z=-0.73 | 0.464 |
| BMI (kg/m2), median [IQR] | 23.00 (20.00, 26.00) | 23.00 (20.00, 26.00) | Z=-0.96 | 0.336 |
| Number of missing teeth, median [IQR] | 0.00 (0.00, 1.00) | 1.00 (1.00, 4.00) | Z=-7.84 | < 0.001 |
| Number of dentures, median [IQR] | 2.00 (1.00, 3.00) | 3.00 (2.00, 5.00) | Z=-6.65 | < 0.001 |
| Social support, median [IQR] | 32.00 (28.00, 36.00) | 29.00 (24.00, 35.00) | Z=-4.44 | < 0.001 |
| Gender, n (%) | χ²=7.23 | 0.007 | ||
| Female | 182 (53.06) | 47 (38.84) | ||
| Male | 161 (46.94) | 74 (61.16) | ||
| Residence, n (%) | χ²=8.92 | 0.003 | ||
| town | 193 (56.27) | 49 (40.50) | ||
| rural aera | 150 (43.73) | 72 (59.50) | ||
| Annual household income, n (%) | χ²=5.24 | 0.073 | ||
| > 30,000 | 78 (22.74) | 22 (18.18) | ||
| 10,000 ~ 30,000 | 133 (38.78) | 38 (31.40) | ||
| < 10,000 | 132 (38.48) | 61 (50.41) | ||
| Smoke, n (%) | χ²=0.45 | 0.505 | ||
| No | 168 (48.98) | 55 (45.45) | ||
| Yes | 175 (51.02) | 66 (54.55) | ||
| Drink, n (%) | χ²=1.89 | 0.169 | ||
| No | 158 (46.06) | 47 (38.84) | ||
| Yes | 185 (53.94) | 74 (61.16) | ||
| Standard of culture, n (%) | χ²=0.82 | 0.846 | ||
| Junior high school and below | 68 (19.83) | 22 (18.18) | ||
| Secondary / high school | 71 (20.70) | 24 (19.83) | ||
| College for professional training | 148 (43.15) | 51 (42.15) | ||
| Bachelor degree and above | 56 (16.33) | 24 (19.83) | ||
| Live alone, n (%) | χ²=0.04 | 0.849 | ||
| No | 199 (58.02) | 69 (57.02) | ||
| Yes | 144 (41.98) | 52 (42.98) | ||
| Comorbidity, n (%) | χ²=0.40 | 0.527 | ||
| < 2 | 70 (20.41) | 28 (23.14) | ||
| ≥2 | 273 (79.59) | 93 (76.86) | ||
| Subjective chewing difficulty, n (%) | χ²=5.69 | 0.017 | ||
| No | 162 (47.23) | 42 (34.71) | ||
| Yes | 181 (52.77) | 79 (65.29) | ||
| Xerostomia, n (%) | χ²=5.51 | 0.019 | ||
| No | 212 (61.81) | 60 (49.59) | ||
| Yes | 131 (38.19) | 61 (50.41) | ||
| Eating habits, n (%) | χ²=10.20 | 0.006 | ||
| Light diet | 203 (59.18) | 60 (49.59) | ||
| Salty | 99 (28.86) | 32 (26.45) | ||
| Sweet | 41 (11.95) | 29 (23.97) | ||
| Physical frailty, n (%) | χ²=7.95 | 0.019 | ||
| Non-frailty | 109 (31.78) | 25 (20.66) | ||
| Pre-frailty | 134 (39.07) | 46 (38.02) | ||
| Frailty | 100 (29.15) | 50 (41.32) | ||
| Nutritional status, n (%) | χ²=11.76 | < 0.001 | ||
| Normal | 248 (72.30) | 67 (55.37) | ||
| Malnutrition | 95 (27.70) | 54 (44.63) | ||
| Sleep quality, n (%) | χ²=0.95 | 0.331 | ||
| Normal | 178 (51.90) | 69 (57.02) | ||
| Poor | 165 (48.10) | 52 (42.98) |
Table 3.
Multivariate logistic regression analysis of factors associated with oral frailty in elderly patients in the development cohort
| Variables | β | S.E | Z | P | OR (95%CI) |
|---|---|---|---|---|---|
| Intercept | -6.65 | 1.51 | -4.40 | < 0.001 | 0.00 (0.00 ~ 0.03) |
| Gender | |||||
| Female | 1.00 (Reference) | ||||
| Male | 0.51 | 0.29 | 1.75 | 0.080 | 1.66 (0.94 ~ 2.93) |
| Residence | |||||
| town | 1.00 (Reference) | ||||
| rural aera | 0.29 | 0.29 | 1.00 | 0.318 | 1.34 (0.76 ~ 2.37) |
| Subjective chewing difficulty | |||||
| No | 1.00 (Reference) | ||||
| Yes | 0.64 | 0.30 | 2.17 | 0.030 | 1.90 (1.06 ~ 3.38) |
| Xerostomia | |||||
| No | 1.00 (Reference) | ||||
| Yes | 0.62 | 0.29 | 2.14 | 0.032 | 1.86 (1.05 ~ 3.28) |
| Eating habits | |||||
| Light diet | 1.00 (Reference) | ||||
| Salty | -0.02 | 0.33 | -0.07 | 0.944 | 0.98 (0.51 ~ 1.88) |
| Sweet | 0.92 | 0.40 | 2.30 | 0.021 | 2.52 (1.15 ~ 5.54) |
| Physical frailty | |||||
| Non-frailty | 1.00 (Reference) | ||||
| Pre-frailty | 0.25 | 0.37 | 0.68 | 0.497 | 1.29 (0.62 ~ 2.67) |
| Frailty | 0.96 | 0.38 | 2.52 | 0.012 | 2.61 (1.24 ~ 5.51) |
| Nutritional status | |||||
| Normal | 1.00 (Reference) | ||||
| Poor | 0.50 | 0.30 | 1.68 | 0.093 | 1.65 (0.92 ~ 2.94) |
| Age | 0.06 | 0.01 | 4.22 | < 0.001 | 1.06 (1.03 ~ 1.09) |
| Number of missing teeth | 0.62 | 0.10 | 6.06 | < 0.001 | 1.85 (1.52 ~ 2.26) |
| Number of dentures | 0.52 | 0.09 | 5.71 | < 0.001 | 1.68 (1.40 ~ 2.00) |
| Social support | -0.12 | 0.03 | -4.27 | < 0.001 | 0.89 (0.84 ~ 0.94) |
Development of the assessment model
Based on the eight independent correlates identified through multivariable logistic regression analysis (age, number of dentures, number missing of teeth, social support, subjective chewing difficulty, xerostomia, dietary preferences, and frailty status), a nomogram visualization model was constructed (Fig. 1). The mathematical formulation of the assessment model was expressed as:
 |
Fig. 1.
Nomogram. Note: The model assigns points to risk factors (top scale) which sum to a "Total Points" (bottom scale) to derive predicted probability. For example, a 75-year-old patient with 4 missing teeth (66 pts), 4 dentures (50 pts), an Social Support Rating Scale score of 30 (30 pts), sweet-eating habits (23 pts), and chewing difficulty (24 pts) totals 193 points, corresponding to a ~62% risk of oral frailty
where P represents the probability of oral frailty occurrence calculated as 
, with e being the natural logarithm and x = logit(P).
Internal validation and evaluation of the assessment model
Discrimination
The constructed model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.878 (95% CI: 0.845–0.913), as shown in Fig. 2. At the optimal cutoff value of 0.253, the model achieved a sensitivity of 80.0% and specificity of 80.6%, with a maximum Youden’s index of 1.61. Internal validation using the bootstrap resampling method (1000 repetitions) further confirmed the model’s robust discrimination, yielding an AUC of 0.918 (95% CI: 0.876–0.959), with improved sensitivity (84.2%) and specificity (84.9%) at a similar cutoff value (0.252) and maximum Youden’s index of 1.69.
Fig. 2.
Area under the ROC curve of the assessment model in the development cohort (a) and the validation cohort (b)
Calibration
The calibration curves for both the development and validation cohorts indicated strong agreement between predicted and observed probabilities, with the prediction curve and bootstrap-corrected calibration curve closely aligning with the ideal diagonal line (Fig. 3). The Hosmer-Lemeshow goodness-of-fit test results for internal (χ² = 10.966, P = 0.372) and internal validation (χ² = 6.747, P = 0.881) confirmed excellent model fit, suggesting no significant deviation between predicted and actual outcomes. These results collectively demonstrate that the nomogram maintains high discriminative ability and reliability across different patient samples, supporting its potential clinical utility for oral frailty risk stratification in Elderly hospitalized patients.
Fig. 3.
Calibration curves of the development cohort (a) and the validation cohort (b)
Clinical applicability
Decision curve analysis demonstrated substantial clinical utility of the assessment model across both development and validation cohorts. Within threshold probability ranges of 2%–93% (development cohort) and 2%–91% (validation cohort), the model’s net benefit curve consistently surpassed the extreme “treat-all” and “treat-none” reference lines (Fig. 4).
Fig. 4.
Decision curve analysis of the development cohort (a) and the validation cohort (b)
Discussion
The present study identified advanced age as a significant associated factor of oral frailty in hospitalized older patients (OR = 1.06, P < 0.001), which aligns with previous research. Hironaka et al. [32] similarly reported a strong association between age and oral frailty (OR = 1.075, P < 0.001), demonstrating an increasing prevalence with advancing age. This finding is further corroborated by Hihara et al. [33], who observed positive correlations between age and oral frailty questionnaire scores, with significantly higher scores in the ≥ 85 years age level compared to both the 65–69 (P < 0.001) and 70–74 (P < 0.001) year-old level. Consistent results have also been reported by Kugimiya et al. [34], Tanaka et al. [13], and Hu et al. [35]. The underlying physiological mechanisms may involve age-related declines in masticatory efficiency, salivary gland atrophy, reduced synthesis and secretion of saliva, and subsequent impairment of oral self-cleaning capacity and mucosal resistance [36]. Furthermore, studies indicate a progressive deterioration in oral health literacy among older adults, with lower literacy levels significantly associated with more severe periodontitis, higher plaque indices, and greater tooth loss—all potential contributors to the elevated risk of oral frailty in advanced age [37]. Collectively, these cumulative age-related physiological and cognitive changes create a vicious cycle of declining oral function, underscoring the importance of targeted preventive interventions for geriatric populations.
The present study confirmed that hospitalized older adults with a high number of dentures and missing teeth are more susceptible to oral frailty, a conclusion that is consistent with the findings of Tanaka et al. [13]. With advancing age, common oral conditions such as gingival recession and tooth loosening enlarge the gaps between teeth and gums, promoting bacterial accumulation and increasing the risk of oral infections and inflammation. Moreover, tooth loss directly compromises essential oral functions, including occlusion, mastication, and swallowing, leading to eating difficulties in older adults and subsequently contributing to malnutrition and digestive disorders. Research by Izuora et al. [38] involving diabetic patients with a mean age of 58.9 years revealed that only 6.4% retained a full dentition (32 teeth), while 15.3% were completely edentulous. It is also noteworthy that many older individuals tend to avoid harder foods due to discomfort associated with denture use—a compensatory behavior that may accelerate the decline of oral function and worsen oral frailty. Clinical evidence indicates that consistent oral care and good hygiene practices can effectively reduce tooth loss. Therefore, it is recommended that edentulous older patients receive appropriate denture prostheses in a timely manner to restore dental arch integrity, supplemented by professional masticatory function training and nutritional guidance. Such integrated approaches hold significant clinical value for the prevention and management of oral frailty.
Social support significantly influences the occurrence of oral frailty in Elderly hospitalized patients with chronic diseases—a finding largely consistent with previous research [32]. It is well established that social support serves as a critical protective factor for maintaining physical health in older adults. Defined as the psychological or material assistance individuals receive from their social networks, social support can effectively encourage the adoption and maintenance of health-promoting behaviors. Among older hospitalized patients with chronic diseases, those with higher levels of social support are more likely to have common oral health issues—such as dry mouth, chewing difficulties, choking while eating, and oral pain—identified early and addressed through clinical intervention, thereby reducing the risk of progression to oral frailty [39].Therefore, in the context of geriatric care for hospitalized patients with chronic illnesses, assessing a patient’s level of social support is not merely a psychosocial concern but a crucial component of a comprehensive medical assessment. Screening for social support should be integrated into standard admission protocols, enabling healthcare teams to identify vulnerable individuals and connect them with appropriate resources, such as social workers, dietary counselors, or community-based senior support programs, to proactively break this cycle and preserve both oral and systemic health.
Subjective chewing difficulty and xerostomia (dry mouth) were also identified as independent correlate in this model, consistent with the findings reported by Hu et al. [35], who confirmed both subjective swallowing difficulty and xerostomia as influencing factors for oral frailty. From a physiological standpoint, reduced salivary secretion not only compromises oral lubrication but also disrupts the coordination of chewing and swallowing, thereby aggravating the severity of subjective swallowing impairment. Simultaneously, the decline in antibacterial components in saliva disturbs the balance of the oral microbiota, elevating susceptibility to oral infections—a process that further compromises oral function. Moreover, inadequate food intake resulting from subjective swallowing difficulties may contribute to malnutrition, which in turn adversely affects the health of salivary glands and oral mucosa, ultimately establishing a vicious cycle. These findings underscore the close interrelationship between oral dysfunction and systemic health in older adults, highlighting the importance of comprehensive assessment and intervention strategies that address both the mechanical and physiological dimensions of oral frail.
The findings of this study also identified dietary preference as a associated factor for oral frailty. Sugars present in sweet foods serve as the primary energy source for oral bacteria, particularly Streptococcus mutans [40]. During carbohydrate metabolism, these bacteria generate acidic by-products that reduce oral pH, contributing to enamel demineralization and dental caries [41]. Prolonged acid exposure weakens tooth structure and diminishes its resistance to decay. Saliva, a crucial defensive component of oral health, helps buffer acidic conditions through its inherent neutralizing capacity. However, excessive sugar consumption can overwhelm this buffering mechanism, delaying pH recovery and creating a favorable environment for bacterial proliferation. Chronic intake of sugar-rich foods also disturbs the ecological balance of the oral microbiota, favoring the dominance of cariogenic species that thrive in high-sugar environments. This microbial shift not only increases caries risk but may also contribute to the development of periodontal diseases, thereby accelerating oral frailty [42]. Moreover, high-sugar diets have been associated with impaired immune function and reduced self-repair capacity of the oral mucosa [43], increasing tissue vulnerability to bacterial invasion and further compromising oral health. Therefore, during hospitalization, it is essential to strengthen dietary guidance, rationally modify food textures, encourage oral muscle exercise, and enhance masticatory performance. Such nutritional interventions can help optimize dietary patterns and improve oral function in Elderly patients, underscoring the value of integrating diet-based strategies into oral frailty prevention protocols.
Oral frailty may contribute to the development of chronic systemic diseases and inadequate or imbalanced nutrient intake, thereby exacerbating physical frailty; conversely, physical frailty can further worsen oral frailty, forming a bidirectional relationship. A study on the decline of oral function and its association with physical frailty among community-dwelling older adults revealed that individuals with impaired oral function had significantly higher adjusted odds ratios for limb frailty or overall frailty [44]. Another investigation into the relationship between physical frailty indicators and oral frailty demonstrated that slower gait speed was significantly associated with oral frailty (OR = 0.850, 95% CI = 0.730–0.970, P = 0.019) [45]. Older adults with reduced gait speed often experience limited social engagement, which may lead to decreased use of the pharyngeal and perioral muscles, subsequently impairing oral motor function. Furthermore, a longitudinal study exploring the association between oral frailty and malnutrition in community-dwelling older adults indicated that oral frailty could lead to malnutrition and unintentional weight loss, thereby elevating the risk of physical frailty [26]. In particular, older adults with chewing and swallowing difficulties often avoid hard-to-chew foods such as fruits, vegetables, and meats—key sources of essential nutrients—due to discomfort or functional limitations. As a result, those with poor oral health are more likely to have inadequate intake of protein, vitamins, and dietary fiber. These findings collectively suggest that interventions such as denture prostheses, regular dental examinations, and professional oral hygiene care can effectively maintain and restore masticatory function in older adults. In addition, implementing swallowing training may help improve swallowing efficiency, diversify food choices, and optimize nutritional status, ultimately contributing to the prevention of both physical and oral frailty.
This study developed and validated a risk assessment model for oral frailty in Elderly hospitalized patients using logistic regression analysis. The optimal cutoff value derived from Youden’s index provides a pragmatic tool for clinical decision-making. In a busy hospital setting, this threshold can function as a screening trigger: patients identified by the nomogram as being above the cutoff score should be prioritized for comprehensive oral geriatric assessment and multidisciplinary intervention. For instance, those at high risk could be enrolled in early-stage oral function training or receive targeted nutritional counseling to mitigate the progression of oral frailty. By integrating this numerical threshold into routine clinical workflows, healthcare providers can transition from generalized care to a more stratified and proactive management strategy, ultimately optimizing the allocation of medical resources and improving patient outcomes. The model demonstrated strong goodness-of-fit and excellent discriminative ability, with area under the curve (AUC) values of 0.878 (95% CI: 0.845–0.913, P < 0.001) in the training cohort and 0.918 (95% CI: 0.876–0.959, P < 0.001) in the validation cohort. Calibration curves and decision curve analysis (DCA) further confirmed its high calibration accuracy and clinical utility, with the Hosmer–Lemeshow test indicating good fit (P = 0.472). Regarding the clinical applicability, the DCA demonstrated a positive net benefit across a wide range of threshold probabilities. However, the most clinically relevant decision-making window lies between 10% and 40%. In the context of geriatric care, a 10% threshold reflects a conservative approach where early, low-risk interventions (e.g., oral hygiene education) are justified even at lower risk levels. Conversely, the 40% threshold marks a transition to high-risk status where intervention becomes mandatory. By focusing on this 10%–40% ‘grey zone,’ the nomogram effectively assists clinicians in optimizing resource allocation for patients whose risk is high enough to warrant concern but not yet so obvious as to make a prediction model redundant. Although several assessment tools for oral frailty are available internationally, including combined subjective–objective measures [13], six-item assessments [46], the Oral Frailty Index-5 [47], Oral Frailty Index-8 [26], the Oral and Maxillofacial Frailty Index [48], and standardized questionnaires [49], only the Oral Frailty Index-8 has been linguistically and culturally adapted for Chinese populations by Chen Zongmei et al. [50], with no indigenous instrument currently available. The nomogram developed in this study addresses this gap by offering a practical and complementary tool to conventional dental examinations. It enables healthcare providers to quantitatively assess oral frailty risk and implement early interventions in hospitalized Elderly patients, thereby representing a meaningful step forward in geriatric oral health assessment within clinical settings.
This study has several limitations. First, it should be noted that this study adopted a cross-sectional design, which means the model identifies factors associated with the presence of oral frailty at a single point in time. Therefore, the model serves as a point-of-care screening tool rather than a longitudinal tool for forecasting future risk. The factors identified, such as sleep quality and social support, are correlated with oral frailty, but causal relationships cannot be definitively established. Second, while the stepwise regression method was used to maintain the model’s clinical parsimony and usability, we acknowledge its potential for variable selection bias. Thirdly, this was a single-center study, and the model was evaluated using internal validation (split-sample method). Although the nomogram demonstrated robust performance in the testing set, its external validity remains to be established.
Conclusion
This study reveals a notably high prevalence of oral frailty among Elderly hospitalized patients in China, highlighting the need for increased clinical awareness and proactive management. Healthcare providers should implement systematic oral frailty screening in this population, combined with early risk assessment and tailored interventions targeting modifiable associated factors. Comprehensive preventive strategies—including improved patient education on frailty prevention and the establishment of structured intervention protocols—are essential to reduce its incidence. Moreover, the Nomogram-based assessment tool developed through logistic regression analysis exhibits strong discriminative performance (C-statistic = 0.878–0.918) and good calibration, offering clinicians a practical and visually accessible tool to quantify individual frailty risk. This model serves as a clinically applicable aid for facilitating evidence-based decision-making and enabling timely intervention in hospitalized older adults. These findings emphasize the value of integrating standardized oral frailty assessment into routine geriatric care to enhance clinical outcomes.
Acknowledgements
We appreciate all participants who dedicated their time and to the investigators whose diligent efforts made this work possible.
Authors' contributions
Xiuxia Shi performed data analysis and wrote the main manuscript text. Xiuhong Zhou supervised the project. All authors reviewed the manuscript. Yan Yang conducted formal analysis. Runnan Pei developed the methodology. Ying Song implemented the software. Qi Yu performed validation. Yuxia Jin curated the data.
Funding
The authors declare that they don’t use any external funding.
Data availability
Data supporting the findings of this study are available from corresponding author Xiuhong Zhou upon reasonable request.
Declarations
Ethics approval and consent to participate
The studies involving human participants were reviewed and approved by the Medical Ethics Committee of Tianjin Fifth Central Hospital (Approval Notice No. WZX-EC-KY2025025). All participants provided their written informed consent to participate in this study. The studies were conducted in accordance with the local legislation and institutional requirements. A brief description of the study goals and participants’ rights was provided prior to the survey/interview; the act of completing and submitting the questionnaire was considered implied consent to participate.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data supporting the findings of this study are available from corresponding author Xiuhong Zhou upon reasonable request.