A risk prediction model for oral frailty in elderly patients with COPD was constructed based on the health ecology model

A risk prediction model for oral frailty in elderly patients with COPD was constructed based on the health ecology model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A risk prediction model for oral frailty in elderly patients with COPD was constructed based on the health ecology model Xuan Qiao, Shasha Gao, Haixia Zhao, Yiming Lu, Huijun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7400728/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Elderly patients with COPD often have oral health problems, such as dry oral mucosa, tooth loss, and gum disease. Dry oral mucosa makes it difficult for food to slide and chew in the mouth. Tooth loss or gum disease can affect chewing and occlusal functions, causing difficulty in swallowing during meals in elderly COPD patients and increasing the risk of dysphagia. Oral weakness is an independent risk factor for dysphagia in elderly patients with COPD. This study aims to explore the influencing factors of oral frailty in elderly patients with chronic obstructive pulmonary disease (COPD) and to construct a nomogram prediction model. Methods: From July 2025 to August 2025, convenience sampling was used to select 320 rows of elderly patients with chronic obstructive pulmonary disease in a ClassⅲGrade A hospital in Shandong Province as the research objects. Among them, 223 cases were included in the modeling group and 97 cases were included in the validation group. The oral frailty Index-8 was used to screen oral frailty, and a score ≥4 was defined as oral frailty. Multivariate Logistic regression was used to analyze the risk factors of oral frailty in elderly patients with chronic obstructive pulmonary disease. R software was used to establish a risk prediction model and draw a nomogram to visualize the model. ROC curve, Hosmer Lemeshow(H-L) test, calibration curve, and decision curve were used to verify the prediction effect of the model. Results: The incidence of oral frailty in elderly patients with chronic obstructive pulmonary disease was 92.5%. The influencing factors of oral frailty in elderly patients with chronic obstructive pulmonary disease were nutrition, the degree of dyspnea, and the type of chronic disease. The area under the ROC curve of the modeling group and the validation group was 0.97(95%CI: 0.94-1.00) and 0.92(95%CI: 0.83-1.00), respectively. The calibration curves of the two groups were well fitted (P=0.999, P=0.727). The decision curves of the two groups showed that the model had high clinical practicability. Conclusions: The nomogram prediction model constructed in this study has good efficacy, which is conducive to clinical nursing staff to early screen the risk of oral frailty in elderly patients with chronic pulmonary obstructive disease. Trial registration: Not applicable. Ethical Committee Approval： This study was approved by the Medical Ethics Review Committee of Jinzhou Medical University (Approval No. JZMULL2025269) on 17 March 2025 The elderly Chronic obstructive pulmonary disease Oral Frailty A nomogram Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by chronic airway inflammatory response and chronic structural destruction of lung tissue. Incomplete reversible airflow limitation is its main pathophysiological feature [ 1 , 2 ]. According to the 2024 World Health Organization (WHO) data [ 3 ], COPD is the fourth leading cause of death in the world, causing 3.5 million deaths in 2021, accounting for about 5% of the total global deaths. There are about 100 million COPD patients in China, among whom the prevalence rate of elderly people over 60 years old is as high as 27%, which has become one of the main diseases causing disability and death in the elderly in China[ 4 ]. Oral Frailty [ 5 – 7 ] refers to the change of various oral conditions (number of teeth, oral hygiene, oral function, etc.) with the increase of age, accompanied by the decline of oral health interest, physical and mental reserve capacity, and eating disorders. The overall impact of oral frailty is the deterioration of physical and mental function. Studies have shown[ 8 ] that oral frailty is an independent risk factor for dysphagia in elderly COPD patients, similar to the research results of Lu Qian et al[ 9 ]. The reason may be that elderly COPD patients often have oral health problems, such as dry oral mucosa, tooth loss, gum disease, etc. Dry oral mucosa makes the sliding and chewing process of food in the mouth difficult. Tooth loss or gum disease can affect chewing and occlusal functions, causing difficulty in swallowing during meals in elderly COPD patients and increasing the risk of dysphagia. Liang Yuanjun et al [ 10 ] ] found that the incidence of oral frailty in elderly hospitalized COPD patients with severe conditions was 96.1%, which is a predictor of frailty in elderly COPD patients. At present, there are relatively few studies on oral frailty in elderly COPD patients both at home and abroad, and there is no risk prediction model for oral frailty in COPD patients. The health ecology model holds that individual health is the result of complex interactions between personal traits and environmental factors. This model can be divided into the following levels from the inside out: First comes the individual's innate traits, followed by psychological behavior and lifestyle. The outer layer includes the influence of family and social networks. The outer layer is the environmental conditions of life and work. The outermost layer is the macro policies and environmental factors. This model constructs a comprehensive analytical framework for the integrated analysis of the formation mechanism of health conditions[ 11 , 12 ]. Therefore, guided by the theory of health ecology models, this study conducts multi-dimensional classification and a comprehensive summary of the influencing factors of oral frailty in elderly hospitalized COPD patients, and constructs a nomogram model for the risk of oral frailty in elderly COPD patients. The aim is to enable clinical medical staff to screen oral frailty in elderly COPD patients at an early stage and intervene promptly, thereby improving the quality of life of COPD patients. To provide a basis for reducing its adverse health outcomes. Materials and Methods Research Design This is a cross-sectional study, with the research subjects being patients in the respiratory ward of a tertiary general hospital in Shandong Province, China. This study was conducted solely through questionnaires and did not involve any invasive procedures, thus posing no risks or harm to patients. Subjects Using the convenient sampling method, a total of 320 patients with chronic obstructive pulmonary disease from a Classⅲ Grade A hospital in Shandong Province were selected as the research objects from July to August 2025. Inclusion criteria: 1) meet the diagnostic criteria for chronic obstructive pulmonary disease in the "Guidelines for Primary diagnosis, treatment and Management of Chronic obstructive pulmonary Disease in China (2024)"[ 13 ]; 2) age ≥ 60 years old; 3) the disease is in a stable stage; 4) have a clear consciousness and can cooperate to complete the investigation; 5) Informed consent of patients. Exclusion criteria:1) patients with severe organic diseases; 2) malignant tumors; 3) patients with mental illness; 4) unable to communicate normally; 5) Oral diseases due to trauma Sample size estimation According to the sample size estimation formula of quantitative research in nursing research[ 14 ], N = 4U 2 αS 2 /δ 2 , where U is the U value corresponding to the test level, S represents the standard deviation, and σ represents the allowable error. The standard deviation S and the allowable error δ can be derived from the data in the pilot experiment (or from other results in the literature) [ 15 ]. Usually α = 0.05, this study takes a two-sided test,U = 1.96, the allowable error is [0.25S,0.5S][ 16 ],and the standard deviation is S = 1.93[ 10 ]. According to the above formula, and taking into account the 15% sample attrition rate, the sample size range is about 70–283 cases. Therefore, a total of 320 elderly patients with COPD who met the criteria were included in this study. Selection of variables Based on the health ecology model and literature review, this study selected the influencing factors that might lead to oral frailty in elderly COPD patients. (1) Personal innate characteristics: gender, age, occupation, place of birth, course of COPD, degree of dyspnea, type of chronic disease, type of oral medication, nutrition, frailty, intact teeth, and oral diseases; (2) psychological behavior: smoking, exercise, eating speed, eating habits, oral health related self-efficacy (3) family community network: marital status; (4) Working and living conditions: previous occupation, hukou location and monthly income; (5) Policy environment: type of medical insurance. Instrument of survey General Information Questionnaire By reviewing the relevant literature, The questionnaire included gender, age, occupation, place of origin, marital status, monthly income, type of medical insurance, course of COPD, degree of dyspnea, types of chronic diseases, types of oral drugs, smoking, exercise, eating speed, eating habits, types of daily diet, intact teeth, and oral diseases. Oral Frailty Index-8 (OFI-8) The scale was developed by Tanaka et al [ 17 ]in 2021, and Chen Zongmei et al [ 18 ]translated it into a Chinese version in 2023 to screen for oral frailty in the elderly. The Cronbach'sα coefficient was 0.692, which had good reliability and validity. The scale included 5 dimensions (whether to use dentures, swallowing function, social participation, oral health-related behaviors, and chewing ability) and 8 items. The total score ranged from 0 to 11 points, and ≥ 4 points were defined as oral frailty. Higher scores indicated worse oral conditions. Fatigue Resistance Ambulation Illness and Loss of weight Scale (FRAIL) The scale was proposed by the International Nutrition and Aging Society (IANA) in 2008 and is widely used in the assessment of frailty of the elderly in China. Wei Yin et al[ 19 ] completed the localization revision in 2018, and its Cronbach's α coefficient was 0.826, which had good reliability. The scale was composed of five dimensions, including fatigue degree, resistance, gait speed, number of chronic diseases, and weight change. Each dimension was scored using a dichotomous method, with 1 point assigned to a "yes" response and 0 points assigned to a "no" response. The total score was evaluated as follows: a score of 0 indicated no frailty, 1–2 indicated pre-frailty, and ≥ 3 was considered as frailty. The Cronbach's α coefficient of the scale in this study was 0.830. Mini-Nutritional Assessment Short Form (MNA-SF) This scale was developed by Rubenstein et al ] [ 20 ]in 2001 for the screening of malnutrition in the elderly with high sensitivity and specificity. Zhang Yan et al[ 21 ]analyzed the internal consistency of the scale, and its Cronbach's α coefficient was 0.711, indicating that the reliability of the scale was good. The scale consists of six domains, including changes in diet, weight loss in the past 3 months, mobility, stress or acute illness, neuropsychiatric illness, and body mass index or calf circumference. The total score was 14. A score of 0–7 indicates that malnutrition is already present, 8–11 indicates that there is a risk of malnutrition, and ≥ 12 indicates normal nutritional status. The Cronbach's α coefficient of the scale in this study was 0.828. Geriatric Self-Efficacy Scale for Oral Health (GSEOH) The scale was developed by Ohara et al[ 22 ]in 2017 to investigate the oral health-related self-efficacy of the elderly in Japan. Xu Yuxin et al[ 23 ]conducted the Chinese version and reliability and validity test of the scale in 2021. The Cronbach's α coefficient of the Chinese version of the scale was 0.913; the reliability of the scale was good, and the scale had good validity. The scale was composed of three dimensions, including the oral hygiene habits dimension, the oral function dimension, and the oral treatment habits dimension. The Likert 4-point scoring method was used, with 1 being not confident at all, and 4 being very confident. The total score ranged from 20 to 80; the higher the score, the higher the level of self-efficacy. The Cronbach'sα coefficient of the scale in this study was 0.932. Survey methods In this study, the on-site questionnaire method was used to complete the data collection, and the investigators were responsible for the distribution and recovery of the questionnaires. Then, the purpose and significance of the survey were explained to the subjects and their accompanying family members, and the information was promised to be confidential. The survey was carried out after informed consent. For subjects who could not complete the questionnaire independently, the researchers or their family members would assist them in filling in the questionnaire. In this study, a total of 350 questionnaires were distributed, and 320 valid questionnaires were collected, with an effective recovery rate of 91.4%. Statistical analysis SPSS 25.0 software was used for data analysis. For quantitative variables with skewed distribution, median and interquartile range M (P25, P75) were used for descriptive statistics. Categorical variables were presented as frequencies and percentages. Non-parametric tests such as the Mann-Whitney U test (two-group comparison) or Kruskal-Wallis H test (multi-group comparison) were used for quantitative variables comparison between groups, and the Chi-square test or Fisher's exact test was used for categorical variables comparison between groups. In addition, the significant variables screened by univariate analysis were included in the multivariate Logistic regression model to determine the relevant influencing factors for the risk of oral frailty in elderly patients with chronic obstructive pulmonary disease. RStudio software was used to draw a nomogram according to the influencing factors, and calculate the confusion matrix of the prediction model according to the best Youden index to evaluate the performance of the model. The ROC curve took the true positive rate as the vertical axis and the false positive rate as the horizontal axis, and the area under the curve (AUC) measured the discrimination power. The closer the AUC was to 1, the stronger the discrimination power was. The calibration curve was compared between the predicted probability and the actual frequency; the closer to the 45-degree diagonal, the better the calibration. Hosmer-Lemeshow test P > 0.05 indicated a good fit. The decision analysis curve was based on the net benefit of different threshold probabilities. When comparing the model with "full intervention" and "no intervention" strategies, the clinical value of the model was higher when the net benefit was obvious. All tests α = 0.05. Results Characteristics of the participants A total of 320 elderly COPD patients who met the criteria were included in this study, of whom 296 had oral frailty and 24 had non-oral frailty (Fig. 1 ). There was no significant difference in the main baseline characteristics between the training set and the validation set (Table S1 ) (P > 0.05), indicating that the data of the two groups were balanced and comparable, which provided a reliable basis for subsequent model validation. Univariate analysis of oral frailty risk in elderly patients with COPD The results of the univariate analysis are shown (Table 1 ). There were statistically significant differences in the incidence of oral frailty among elderly COPD patients in terms of age, malnutrition, types of chronic diseases, degree of dyspnea, and intact teeth (P < 0.05). Table 1 Univariate analysis of oral frailty(n = 320) Variables Total (n = 320) Non-oral frailty (n = 24) Oral frailty (n = 296) Statistic P MNA -SF sum, M (Q₁, Q₃) 3.00 (2.00, 4.00) 12.50 (4.00, 13.25) 2.00 (2.00, 4.00) Z=-5.84 < .001 GSEOH sum, M (Q₁, Q₃) 62.00 (59.00, 65.00) 62.50 (59.75, 66.00) 62.00 (59.00, 65.00) Z=-1.20 0.230 FRAIL sum, M (Q₁, Q₃) 1.00 (0.00, 2.00) 1.00 (0.00, 1.00) 1.00 (0.00, 2.00) Z=-0.60 0.546 Sex, n(%) χ²=0.30 0.587 Male 216 (67.50) 15 (62.50) 201 (67.91) Female 104 (32.50) 9 (37.50) 95 (32.09) Age, n(%) χ²=33.31 < .001 60–69 129 (40.31) 23 (95.83) 106 (35.81) 70–79 99 (30.94) 1 (4.17) 98 (33.11) 80–89 69 (21.56) 0 (0.00) 69 (23.31) ≥90 23 (7.19) 0 (0.00) 23 (7.77) Occupation, n(%) - 0.520 Personnel of public institutions 60 (18.75) 4 (16.67) 56 (18.92) Professional and technical personnel 84 (26.25) 6 (25.00) 78 (26.35) Enterprise employees 93 (29.06) 5 (20.83) 88 (29.73) Freelancer 51 (15.94) 7 (29.17) 44 (14.86) Farmer 32 (10.00) 2 (8.33) 30 (10.14) Registered permanent, n(%) χ²=1.85 0.174 City 213 (66.56) 19 (79.17) 194 (65.54) Rural 107 (33.44) 5 (20.83) 102 (34.46) Marital status, n(%) - 0.368 Unmarried 32 (10.00) 1 (4.17) 31 (10.47) Married 237 (74.06) 17 (70.83) 220 (74.32) Divorced 51 (15.94) 6 (25.00) 45 (15.20) Pension, n(%) - 0.415 0 27 (8.44) 2 (8.33) 25 (8.45) <1000 83 (25.94) 5 (20.83) 78 (26.35) 1000-<3000 139 (43.44) 12 (50.00) 127 (42.91) 3000-<5000 53 (16.56) 2 (8.33) 51 (17.23) ≥5000 18 (5.62) 3 (12.50) 15 (5.07) Insurance, n(%) - 0.242 Resident Medical Insurance 193 (60.31) 18 (75.00) 175 (59.12) Employee Medical Insurance 91 (28.44) 3 (12.50) 88 (29.73) Public expense 21 (6.56) 2 (8.33) 19 (6.42) Own expense 15 (4.69) 1 (4.17) 14 (4.73) The Course of COPD, n(%) - 0.008 ≤1 (Year) 99 (30.94) 6 (25.00) 93 (31.42) 2–5 158 (49.38) 7 (29.17) 151 (51.01) 6–9 39 (12.19) 8 (33.33) 31 (10.47) ≥10 24 (7.50) 3 (12.50) 21 (7.09) Types of chronic diseases, n(%) - 5 18 (5.62) 0 (0.00) 18 (6.08) Types of oral medications, n(%) - 0.350 1 (kind) 89 (27.81) 7 (29.17) 82 (27.70) 2–3 158 (49.38) 10 (41.67) 148 (50.00) 4–5 49 (15.31) 3 (12.50) 46 (15.54) >5 24 (7.50) 4 (16.67) 20 (6.76) Degree of breathing difficulty, n(%) χ²=73.90 < .001 0 (Grade) 39 (12.19) 16 (66.67) 23 (7.77) 1 107 (33.44) 6 (25.00) 101 (34.12) 2 82 (25.62) 1 (4.17) 81 (27.36) 3 70 (21.88) 1 (4.17) 69 (23.31) 4 22 (6.88) 0 (0.00) 22 (7.43) Smoke, n(%) χ²=2.07 0.150 No 129 (40.31) 13 (54.17) 116 (39.19) Yes 191 (59.69) 11 (45.83) 180 (60.81) Sport, n(%) χ²=0.19 0.661 No 213 (66.56) 15 (62.50) 198 (66.89) Yes 107 (33.44) 9 (37.50) 98 (33.11) The speed of eating, n(%) χ²=1.08 0.581 Slow 107 (33.44) 10 (41.67) 97 (32.77) Moderate 179 (55.94) 11 (45.83) 168 (56.76) Fast 34 (10.62) 3 (12.50) 31 (10.47) Healthy diet, n(%) χ²=2.63 0.105 No 85 (26.56) 3 (12.50) 82 (27.70) Yes 235 (73.44) 21 (87.50) 214 (72.30) Oral disease, n(%) χ²=0.56 0.452 No 102 (31.88) 6 (25.00) 96 (32.43) Yes 218 (68.12) 18 (75.00) 200 (67.57) Perfect teeth, n(%) χ²=14.72 < .001 No 188 (58.75) 23 (95.83) 165 (55.74) Yes 132 (41.25) 1 (4.17) 131 (44.26) Z: Mann-Whitney test, χ²: Chi-square test, -: Fisher exact M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile Multivariate logistic regression analysis of oral frailty in elderly COPD patients Taking the variables with P < 0.05 in the univariate analysis of the risk of oral frailty in elderly COPD patients as independent variables and oral frailty as the dependent variable, a multivariate Logistic regression analysis was conducted. The results (Table 2 ) showed that malnutrition, types of chronic diseases, and the degree of dyspnea were independent risk factors for oral frailty in elderly COPD patients (P < 0.05). Table 2 The logistic regression analysis of oral frailty (n = 320) Variables β S.E Z P OR (95%CI) Intercept 0.78 1.01 0.77 0.438 2.18 (0.30 ~ 15.65) Types of chronic diseases 1 (Kind) 1.00 (Reference) 2–3 3.15 1.14 2.77 0.006 23.35 (2.51 ~ 216.88) 4–5 21.71 4435.61 0.00 0.996 2678391784.60 (0.00 ~ Inf) >5 19.64 7023.27 0.00 0.998 338565589.44 (0.00 ~ Inf) MNA -SF sum -0.33 0.10 -3.48 < .001 0.72 (0.60 ~ 0.86) Degree of breathing difficulty 0 (Grade) 1.00 (Reference) 1 3.13 1.12 2.80 0.005 22.82 (2.56 ~ 203.81) 2 3.59 1.51 2.38 0.018 36.30 (1.87 ~ 703.00) 3 22.09 3391.01 0.01 0.995 3924539812.80 (0.00 ~ Inf) 4 20.03 6204.78 0.00 0.997 500640389.64 (0.00 ~ Inf) OR: Odds Ratio, CI: Confidence Interval Construction of a nomogram of oral frailty in elderly COPD patients and evaluation of its testing efficacy For the variables that were meaningful in the multivariate analysis, a nomogram was constructed using R Studio to visualize the risk factors (Fig. 2 ). Based on the total score of micronutritional assessment screening in elderly COPD patients, the degree of dyspnea, and the type of chronic disease, the total score could be calculated corresponding to the nomogram to predict the probability of oral frailty. The area under the receiver operating characteristic curve of the training set was 0.97(95%CI:0.94-1.00) (Fig. 3 A), and that under the receiver operating characteristic curve of the validation set was 0.92(95%CI:0.83-1.00) (Fig. 3 B). The AUC values of both the training set and the validation set were greater than 0.8, indicating that the model has a strong ability to distinguish oral frailty. The calibration curve shows that the risk of oral frailsia predicted by the model is highly consistent with the actual situation (Figs. 4 A and 4 B), and the Hosmer-Lemeshow test (P = 0.999 for the training set and P = 0.727 for the validation set) indicates a high degree of model fit. The decision curves of the oral frailty prediction model show that the net benefits of the decision curves corresponding to the models in both the training set and the validation set are higher than those of the "total intervention" and "no intervention" strategies, indicating that the model has high clinical application value. (Figs. 5 A and 5 B). Discussion Based on the theoretical guidance of the health ecology model, this study comprehensively integrated multi-dimensional factors such as personal innate characteristics, psychological and behavioral patterns, family and community network, working and living conditions, and policy environment of elderly COPD patients, and constructed a nomogram model for oral frailty in elderly COPD patients. The results of the study showed that the incidence of oral weakness in elderly COPD patients was as high as 92.5%, which was consistent with the study of Liang Yuanjun et al. (96.1%) [10] . Compared with the community elderly (70.3%)[ 24 ], the rural elderly (44.7%)[ 25 ], the elderly in nursing homes (31.0)[ 26 ], the elderly patients with Parkinson's disease (28.67%)[ 27 ], the elderly patients with stroke (47.8%)[ 28 ], and the prevalence of oral frailty in patients with diabetes mellitus (45.4%)[ 29 ]. The high prevalence of oral frailty in elderly COPD patients may be due to the effect of long-term drug use on the oral microenvironment in elderly COPD patients. Some patients with COPD require long-term use of antibiotics, inhaled glucocorticoids, or long-acting β₂ receptor agonists. These drugs[ 30 , 31 ] may inhibit salivary gland function, resulting in decreased salivary secretion and changes in physical and chemical properties (such as increased viscosity and decreased buffering capacity). It can lead to a series of oral problems, such as dry mouth, dental caries, periodontal disease, and fungal infection, and eventually increase the risk of oral frailty. Previous studies have suggested[ 32 ] that for such patients, increasing the frequency of oral cleaning (such as increasing the frequency of brushing teeth and gargling) and gargling 2%-4% sodium bicarbonate solution regularly for oral cleaning can effectively reduce the incidence of oral fungal infection and improve oral health. The results of this study show that the types of chronic diseases are risk factors for oral frailty in elderly COPD patients. The more types of chronic diseases elderly COPD patients have, the higher the probability of oral frailty, which is consistent with the research of Fan Xiaoli et al[ 33 , 34 ]. When multiple chronic diseases (such as diabetes, cardiovascular diseases, osteoporosis, etc.) coexist, Multiple types of drugs, such as bronchodilators, glucocorticoids, antihypertensive drugs, and hypoglycemic drugs need to be used in combination. Drug interactions may inhibit salivary gland function, alter the balance of oral flora, leading to dry mouth, weakened mucosal barrier, and increased risk of infection[ 34 ]. The pathological mechanisms of different chronic diseases can produce synergistic effects. For instance, the hyperglycemic state of diabetes and the chronic inflammation of COPD jointly disrupt the oral immune microenvironment, accelerating the destruction of periodontal tissues and the decline in mucosal repair capacity[ 35 ]. In addition, the coexistence of multiple diseases is often accompanied by an unbalanced intake of nutrients (such as protein and vitamin deficiencies), which further weakens the metabolic function of oral tissues, exacerbates the decline of oral muscle strength, abnormal sensations, and functional degeneration[ 36 ]. Therefore, clinical medical staff need to optimize drug treatment plans, reduce adverse drug reactions, maintain the balance of oral flora, and lower the incidence of infections. When COPD patients have other diseases, medical staff should consider the treatment plans for different diseases, pay attention to the mutual influence among diseases, keep the patients' oral immune microenvironment normal, and at the same time, patients with multiple coexisting diseases should pay attention to supplementing protein and vitamins to meet the body's nutritional needs. This study showed that the lower the MNA screening score, the more prone to oral frailty, and the risk of oral frailty increased in patients with malnutrition (MNA screening score 0–7). Consistent with the study by Iwasaki et al[ 37 ], malnutrition determined using the MNA-SF score was directly related to oral weakness. Studies have shown that the direct manifestations of oral frailty, such as masticatory dysfunction and swallowing dysfunction, are important independent risk factors for malnutrition. Both of them affect the mechanical processing and transport of food, forming a vicious circle of "oral function degradation, nutrient intake limitation, and body metabolic imbalance"[ 38 ]. Therefore, medical and nursing staff should pay close attention to the nutritional status of patients and supplement nutrients according to the patient's constitution. For elderly patients with chewing inconvenience, they can ensure nutritional intake by adjusting the diet form to meet the body's metabolic needs. This study also shows that elderly COPD patients with severe dyspnea are more likely to suffer from oral frailty, which is consistent with the research of Chen Zongmei et al [10] . When COPD patients experience severe dyspnea due to airway obstruction or decreased lung function, they are more likely to compensate through mouth breathing. This abnormal breathing pattern may lead to accelerated evaporation of water from the oral mucosa (causing dry mouth) and persistent spasms of oral muscle groups (such as masticatory muscles and tongue muscles). Persistent dry mouth can weaken the function of the oral mucosal barrier and increase the risk of dental caries, periodontal disease, and fungal infections. Long-term spasms of the oral muscle groups may accelerate the degenerative changes of muscle fibers, leading to functional degeneration such as decreased tongue pressure and reduced chewing efficiency, and ultimately inducing oral weakness. Therefore, medical staff need to optimize drug treatment plans and guide COPD patients to undergo long-term oxygen therapy and pulmonary rehabilitation training measures to alleviate breathing difficulties. In addition, increasing the frequency of oral hygiene for COPD patients, applying 4℃ coconut water spray (to relieve dry mouth), acupoint massage (to relax oral muscle groups), and oral function training (such as tongue muscle strength training and chewing function rehabilitation) can directly improve the oral microenvironment and prevent further deterioration of oral function[ 39 – 41 ]. Conclusion Our study showed that oral frailty was present in the majority of elderly COPD patients. Types of chronic diseases, MNA screening score, and degree of dyspnea were the factors affecting the occurrence of oral frailty. In our study, a visual nomogram prediction model based on the three core risk factors was constructed, and individualized oral frailty risk prediction probability could be generated by quantitatively calculating the cumulative scores of each indicator. The application of this tool can help to accurately identify high-risk groups and provide a basis for early targeted intervention measures, so as to optimize the allocation of public health resources and reduce the medical expenditure burden of the elderly population, which has significant health economic value. Limitations Although the oral frailty risk prediction model constructed in this study has good screening efficiency, the visual nomogram tool has the advantages of convenient operation and intuitive results, which can effectively quantify the risk of oral frailty in the elderly. However, this study has some limitations. First of all, although the OF-8 questionnaire used in this study is convenient for screening oral frailty on a large scale, the data from the questionnaire come from subjects' self-reports, which may affect the objectivity of the results. Future research should consider the combination of subjective and objective survey tools to improve the authenticity and reliability of the assessment results. Secondly, the representative sample selected in this study is limited, and a large and multi-center population can be selected for further research. Finally, this study only used internal validation to evaluate the model's performance; to further evaluate the applicability of the model, future studies need to conduct external validation of the model. Declarations Compliance with the Declaration of Helsinki All procedures involving human participants were performed in accordance with the ethical standards of the Declaration of Helsinki (2013) Ethics approval and consent to participate Informed consent was obtained from all individual participants or their legally authorized representatives before enrollment Consent for publication Not applicable. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no financial support. Authors' contributions QX took the lead in writing the original draft and contributed to validation, methodology design, formal analysis, data curation, and conceptualization, and was a major contributor in writing the manuscript. GSS participated in formal analysis and data curation. ZHX was involved in validation and formal analysis. LYM contributed to validation and conceptualization. ZHJ was responsible for reviewing, editing, supervision, conceptualization, methodology, and project administration. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Woodruff PG, Agusti A, Roche N, Singh D, Martinez FJ. 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Introduction to Statistical Methods in Biomedical Research. Journal of Sun Yat-sen University. (Medical Sci Edition). 2007;028(6):640. (in Chinese). Tanaka T, Hirano H, Ohara Y, Nishimoto M, Iijima K. Oral Frailty Index-8 in the risk assessment of new-onset oral frailty and functional disability among community-dwelling older adults. Arch Gerontol Geriatr. 2021;94:104340. Chen Zongmei T, Ying L, Yuanjun Z, Huanhuan J, Yun. Shi Guofeng: Sinicization and Reliability and Validity Test of Oral Frailty Screening Scale for the Elderly. Nurs Res. 2023;37(21):3808–12. (in Chinese). Wei Yin C, Yanpei Y, Xiaoli, Xu Y. Research on the Localization and Reliability and Validity of Frailty Risk Screening Tools for Elderly Inpatients. Chin J Practical Nurs. 2018;34(20):1526–30. (in Chinese). Rubenstein LZ, Harker JO, Salvà A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol Biol Sci Med Sci. 2001;56(6):M366–372. Yu R. Observation on the Application of Miniature Nutritional Assessment Forms in Nutritional Screening for Elderly Inpatients with Chronic Diseases Weekly Digest ·. Pension Wkly 2024(5):164–6. (in Chinese). Ohara Y, Yoshida N, Kawai H, Obuchi S, Yoshida H, Mataki S, Hirano H, Watanabe Y. Development of an oral health-related self-efficacy scale for use with older adults. Geriatr Gerontol Int. 2017;17(10):1406–11. Xu Yuxin W, Hongmei M, Junchi Z. Chinese Localization and Reliability and Validity Test of Self-efficacy Scale Related to Oral Health in the Elderly. Nurs Res. 2021;35(16):2858–63. (in Chinese). Wang Min Y, Wenjuan L, Ting Z, Jinmei L, Dongxia Z, Cuicui D, Yingyi. Gong Xiyan, Liao Changju: Construction and Verification of a Risk Prediction Model for Oral Frailty in the Elderly in the Community. Chin J Nurs. 2025;60(3):274–80. (in Chinese). Tang Ji T, Xiaoyan Z, Li C, Hao Y, Xing Z, Quanxiang Y, Jingyuan. Analysis of the Prevalence and Influencing Factors of Oral Asthenia among the Elderly in Rural Areas of Guizhou Province. Chronic Disease Prev Control China. 2023;31(5):327–31. (in Chinese). Wei Jingyi Z, Qiuyan H, Wei L, Xing Z. Analysis of the Occurrence and Influencing Factors of Oral Weakness among the Elderly in Elderly Care Institutions. J Sichuan Univ (Medical Science). 2024;55(4):947–57. (in Chinese). Guo Rao X, Donghui. Construction and Verification of a Nomogram Model for Oral Frailty in Elderly Patients with Parkinson's Disease. Chronic Disease Prev Control China. 2025;33(5):363–8. (in Chinese). Ma R, Fan X, Tao X, Zhang W, Li Z. Development and validation of risk-predicting model for oral frailty in older adults patients with stroke. BMC Oral Health. 2025;25(1):263. Zhong Lei Z, Hui X, Jing L, Yawei X, Xiaoting W. Construction of a nomogram Prediction Model for Oral frailty Risk in Elderly Patients with Type 2 diabetes. J Practical Clin Med. 2024;28(16):98–103108. (in Chinese). Khijmatgar S, Belur G, Venkataram R, Karobari MI, Marya A, Shetty V, Chowdhury A, Gootveld M, Lynch E, Shetty S, et al. Oral Candidal Load and Oral Health Status in Chronic Obstructive Pulmonary Disease (COPD) Patients: A Case-Cohort Study. Biomed Res Int. 2021;2021:5548746. Patton B, McPhillips O, Ríordáin RN. The impact of systemic conditions and polypharmacy on older patients’ oral health and dental treatment. J Ir Dent Assoc 2022, 68(Oral Care in Older Adults). Zhimin Y. Hua Hong: Standardized Diagnostic Concepts and Prevention Strategies for Oral Candidiasis. Chin J Stomatology. 2022;57(7):780–5. (in Chinese). Xiaoli F, Ruirui M, Wei Z. Liu Yunjie: Analysis of the Current Situation and Influencing Factors of Oral Frailty in Elderly Stroke Patients. J Qiqihar Med Univ 2024, 45(18):1797–800, Cap. 1793. (in Chinese). Tuğrul E. The Relationship Between Inhaler Use and Oral Problems in Patients with COPD and Affecting Factors: A Cross-Sectional Study. Florence Nightingale J Nurs. 2022;30(2):196–201. Chapple IL, Genco R, Workshop* WGJEA. Diabetes and periodontal diseases: consensus report of the Joint EFP/AAP Workshop on Periodontitis and Systemic Diseases. J Periodontol. 2013;84:S106–12. Lee HJ, Huh Y, Sunwoo S. Association Between the Number of Chronic Diseases and Oral Health Problems in Korean Adults. Oral Health Prev Dent. 2024;22:57–62. Iwasaki M, Motokawa K, Watanabe Y, Shirobe M, Inagaki H, Edahiro A, Ohara Y, Hirano H, Shinkai S, Awata S. Association between Oral Frailty and Nutritional Status among Community-Dwelling Older Adults: the Takashimadaira Study. J Nutr Health Aging. 2020;24(9):1003–10. Iwasaki M, Hirano H, Ohara Y, Motokawa K. The association of oral function with dietary intake and nutritional status among older adults: Latest evidence from epidemiological studies. Jpn Dent Sci Rev. 2021;57:128–37. Chen W, Dongyang Z, Zhixun G, Xiaoyi Y. The Effect of 4℃ Coconut Water Spray in Alleviating dry mouth and Gastrointestinal Dysfunction after Colorectal Cancer Surgery. Nurs Res. 2020;34(21):3807–12. (in Chinese). Xi Yucui Y, Yuan F, Yadi Z. Evaluation of the Effect of Comprehensive Nursing Measures in Alleviating dry mouth in Critically Ill Neurosurgical Patients. Mod Clin Nurs. 2021;20(09):24–9. (in Chinese). Shirobe M, Watanabe Y, Tanaka T, Hirano H, Kikutani T, Nakajo K, Sato T, Furuya J, Minakuchi S, Iijima K. Effect of an Oral Frailty Measures Program on Community-Dwelling Elderly People: A Cluster-Randomized Controlled Trial. Gerontology. 2022;68(4):377–86. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Editor invited by journal 25 Aug, 2025 Submission checks completed at journal 23 Aug, 2025 First submitted to journal 23 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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2","display":"","copyAsset":false,"role":"figure","size":32663,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the risk of oral frailty\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7400728/v1/75306c15f85c5fd1b2268be0.png"},{"id":91084981,"identity":"dc7ff592-ebda-44fc-8c87-dab03a1ac15d","added_by":"auto","created_at":"2025-09-11 12:18:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eThe ROC curve of the training dataset\u003c/p\u003e\n\u003cp\u003eB.\u003cstrong\u003e \u003c/strong\u003eThe ROC curve of the verification dataset\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7400728/v1/e54063a37c0055a9e8e17c4c.png"},{"id":91086600,"identity":"fbac8303-bdab-4116-88d8-ad6817dc3fbb","added_by":"auto","created_at":"2025-09-11 12:26:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Calibration curve of the training dataset\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Calibration curve of the verification dataset\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7400728/v1/54180c2048546bf2138d910f.png"},{"id":91084979,"identity":"3fbd8de2-46c5-4cd8-ae0b-8f5c67bb6086","added_by":"auto","created_at":"2025-09-11 12:18:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eThe DCA curve of the training dataset\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. The DCA curve of the verification dataset\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7400728/v1/3f57938bd0a44852645f29f3.png"},{"id":98243744,"identity":"e0fa7772-18d5-48d9-aa82-6053a4c69116","added_by":"auto","created_at":"2025-12-15 16:10:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1427123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400728/v1/41177302-795e-4489-924d-2ab5cf06f22e.pdf"},{"id":91084976,"identity":"1c4b753b-4e03-4a66-b450-b079ed176f14","added_by":"auto","created_at":"2025-09-11 12:18:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":45508,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7400728/v1/55cc6fae40be6bd02d907403.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A risk prediction model for oral frailty in elderly patients with COPD was constructed based on the health ecology model","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by chronic airway inflammatory response and chronic structural destruction of lung tissue. Incomplete reversible airflow limitation is its main pathophysiological feature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the 2024 World Health Organization (WHO) data [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], COPD is the fourth leading cause of death in the world, causing 3.5\u0026nbsp;million deaths in 2021, accounting for about 5% of the total global deaths. There are about 100\u0026nbsp;million COPD patients in China, among whom the prevalence rate of elderly people over 60 years old is as high as 27%, which has become one of the main diseases causing disability and death in the elderly in China[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Oral Frailty [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] refers to the change of various oral conditions (number of teeth, oral hygiene, oral function, etc.) with the increase of age, accompanied by the decline of oral health interest, physical and mental reserve capacity, and eating disorders. The overall impact of oral frailty is the deterioration of physical and mental function. Studies have shown[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] that oral frailty is an independent risk factor for dysphagia in elderly COPD patients, similar to the research results of Lu Qian et al[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The reason may be that elderly COPD patients often have oral health problems, such as dry oral mucosa, tooth loss, gum disease, etc. Dry oral mucosa makes the sliding and chewing process of food in the mouth difficult. Tooth loss or gum disease can affect chewing and occlusal functions, causing difficulty in swallowing during meals in elderly COPD patients and increasing the risk of dysphagia. Liang Yuanjun et al [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003csup\u003e]\u003c/sup\u003efound that the incidence of oral frailty in elderly hospitalized COPD patients with severe conditions was 96.1%, which is a predictor of frailty in elderly COPD patients. At present, there are relatively few studies on oral frailty in elderly COPD patients both at home and abroad, and there is no risk prediction model for oral frailty in COPD patients. The health ecology model holds that individual health is the result of complex interactions between personal traits and environmental factors. This model can be divided into the following levels from the inside out: First comes the individual's innate traits, followed by psychological behavior and lifestyle. The outer layer includes the influence of family and social networks. The outer layer is the environmental conditions of life and work. The outermost layer is the macro policies and environmental factors. This model constructs a comprehensive analytical framework for the integrated analysis of the formation mechanism of health conditions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, guided by the theory of health ecology models, this study conducts multi-dimensional classification and a comprehensive summary of the influencing factors of oral frailty in elderly hospitalized COPD patients, and constructs a nomogram model for the risk of oral frailty in elderly COPD patients. The aim is to enable clinical medical staff to screen oral frailty in elderly COPD patients at an early stage and intervene promptly, thereby improving the quality of life of COPD patients. To provide a basis for reducing its adverse health outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis is a cross-sectional study, with the research subjects being patients in the respiratory ward of a tertiary general hospital in Shandong Province, China. This study was conducted solely through questionnaires and did not involve any invasive procedures, thus posing no risks or harm to patients.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSubjects\u003c/h3\u003e\n\u003cp\u003eUsing the convenient sampling method, a total of 320 patients with chronic obstructive pulmonary disease from a Classⅲ Grade A hospital in Shandong Province were selected as the research objects from July to August 2025. Inclusion criteria: 1) meet the diagnostic criteria for chronic obstructive pulmonary disease in the \"Guidelines for Primary diagnosis, treatment and Management of Chronic obstructive pulmonary Disease in China (2024)\"[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; 2) age\u0026thinsp;\u0026ge;\u0026thinsp;60 years old; 3) the disease is in a stable stage; 4) have a clear consciousness and can cooperate to complete the investigation; 5) Informed consent of patients. Exclusion criteria:1) patients with severe organic diseases; 2) malignant tumors; 3) patients with mental illness; 4) unable to communicate normally; 5) Oral diseases due to trauma\u003c/p\u003e\n\u003ch3\u003eSample size estimation\u003c/h3\u003e\n\u003cp\u003eAccording to the sample size estimation formula of quantitative research in nursing research[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], N\u0026thinsp;=\u0026thinsp;4U\u003csup\u003e2\u003c/sup\u003eαS\u003csup\u003e2\u003c/sup\u003e/δ\u003csup\u003e2\u003c/sup\u003e, where U is the U value corresponding to the test level, S represents the standard deviation, and σ represents the allowable error. The standard deviation S and the allowable error δ can be derived from the data in the pilot experiment (or from other results in the literature) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Usually α\u0026thinsp;=\u0026thinsp;0.05, this study takes a two-sided test,U\u0026thinsp;=\u0026thinsp;1.96, the allowable error is [0.25S,0.5S][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e],and the standard deviation is S\u0026thinsp;=\u0026thinsp;1.93[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to the above formula, and taking into account the 15% sample attrition rate, the sample size range is about 70\u0026ndash;283 cases. Therefore, a total of 320 elderly patients with COPD who met the criteria were included in this study.\u003c/p\u003e\n\u003ch3\u003eSelection of variables\u003c/h3\u003e\n\u003cp\u003eBased on the health ecology model and literature review, this study selected the influencing factors that might lead to oral frailty in elderly COPD patients. (1) Personal innate characteristics: gender, age, occupation, place of birth, course of COPD, degree of dyspnea, type of chronic disease, type of oral medication, nutrition, frailty, intact teeth, and oral diseases; (2) psychological behavior: smoking, exercise, eating speed, eating habits, oral health related self-efficacy (3) family community network: marital status; (4) Working and living conditions: previous occupation, hukou location and monthly income; (5) Policy environment: type of medical insurance.\u003c/p\u003e\n\u003ch3\u003eInstrument of survey\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGeneral Information Questionnaire\u003c/h2\u003e\u003cp\u003eBy reviewing the relevant literature, The questionnaire included gender, age, occupation, place of origin, marital status, monthly income, type of medical insurance, course of COPD, degree of dyspnea, types of chronic diseases, types of oral drugs, smoking, exercise, eating speed, eating habits, types of daily diet, intact teeth, and oral diseases.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOral Frailty Index-8 (OFI-8)\u003c/h3\u003e\n\u003cp\u003eThe scale was developed by Tanaka et al [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]in 2021, and Chen Zongmei et al [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]translated it into a Chinese version in 2023 to screen for oral frailty in the elderly. The Cronbach'sα coefficient was 0.692, which had good reliability and validity. The scale included 5 dimensions (whether to use dentures, swallowing function, social participation, oral health-related behaviors, and chewing ability) and 8 items. The total score ranged from 0 to 11 points, and \u0026ge;\u0026thinsp;4 points were defined as oral frailty. Higher scores indicated worse oral conditions.\u003c/p\u003e\n\u003ch3\u003eFatigue Resistance Ambulation Illness and Loss of weight Scale (FRAIL)\u003c/h3\u003e\n\u003cp\u003eThe scale was proposed by the International Nutrition and Aging Society (IANA) in 2008 and is widely used in the assessment of frailty of the elderly in China. Wei Yin et al[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] completed the localization revision in 2018, and its Cronbach's α coefficient was 0.826, which had good reliability. The scale was composed of five dimensions, including fatigue degree, resistance, gait speed, number of chronic diseases, and weight change. Each dimension was scored using a dichotomous method, with 1 point assigned to a \"yes\" response and 0 points assigned to a \"no\" response. The total score was evaluated as follows: a score of 0 indicated no frailty, 1\u0026ndash;2 indicated pre-frailty, and \u0026ge;\u0026thinsp;3 was considered as frailty. The Cronbach's α coefficient of the scale in this study was 0.830.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMini-Nutritional Assessment Short Form (MNA-SF)\u003c/h2\u003e\u003cp\u003eThis scale was developed by Rubenstein et al\u003csup\u003e]\u003c/sup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]in 2001 for the screening of malnutrition in the elderly with high sensitivity and specificity. Zhang Yan et al[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]analyzed the internal consistency of the scale, and its Cronbach's α coefficient was 0.711, indicating that the reliability of the scale was good. The scale consists of six domains, including changes in diet, weight loss in the past 3 months, mobility, stress or acute illness, neuropsychiatric illness, and body mass index or calf circumference. The total score was 14. A score of 0\u0026ndash;7 indicates that malnutrition is already present, 8\u0026ndash;11 indicates that there is a risk of malnutrition, and \u0026ge;\u0026thinsp;12 indicates normal nutritional status. The Cronbach's α coefficient of the scale in this study was 0.828.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGeriatric Self-Efficacy Scale for Oral Health (GSEOH)\u003c/h2\u003e\u003cp\u003eThe scale was developed by Ohara et al[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]in 2017 to investigate the oral health-related self-efficacy of the elderly in Japan. Xu Yuxin et al[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]conducted the Chinese version and reliability and validity test of the scale in 2021. The Cronbach's α coefficient of the Chinese version of the scale was 0.913; the reliability of the scale was good, and the scale had good validity. The scale was composed of three dimensions, including the oral hygiene habits dimension, the oral function dimension, and the oral treatment habits dimension. The Likert 4-point scoring method was used, with 1 being not confident at all, and 4 being very confident. The total score ranged from 20 to 80; the higher the score, the higher the level of self-efficacy. The Cronbach'sα coefficient of the scale in this study was 0.932.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSurvey methods\u003c/h2\u003e\u003cp\u003eIn this study, the on-site questionnaire method was used to complete the data collection, and the investigators were responsible for the distribution and recovery of the questionnaires. Then, the purpose and significance of the survey were explained to the subjects and their accompanying family members, and the information was promised to be confidential. The survey was carried out after informed consent. For subjects who could not complete the questionnaire independently, the researchers or their family members would assist them in filling in the questionnaire. In this study, a total of 350 questionnaires were distributed, and 320 valid questionnaires were collected, with an effective recovery rate of 91.4%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eSPSS 25.0 software was used for data analysis. For quantitative variables with skewed distribution, median and interquartile range M (P25, P75) were used for descriptive statistics. Categorical variables were presented as frequencies and percentages. Non-parametric tests such as the Mann-Whitney U test (two-group comparison) or Kruskal-Wallis H test (multi-group comparison) were used for quantitative variables comparison between groups, and the Chi-square test or Fisher's exact test was used for categorical variables comparison between groups. In addition, the significant variables screened by univariate analysis were included in the multivariate Logistic regression model to determine the relevant influencing factors for the risk of oral frailty in elderly patients with chronic obstructive pulmonary disease. RStudio software was used to draw a nomogram according to the influencing factors, and calculate the confusion matrix of the prediction model according to the best Youden index to evaluate the performance of the model. The ROC curve took the true positive rate as the vertical axis and the false positive rate as the horizontal axis, and the area under the curve (AUC) measured the discrimination power. The closer the AUC was to 1, the stronger the discrimination power was. The calibration curve was compared between the predicted probability and the actual frequency; the closer to the 45-degree diagonal, the better the calibration. Hosmer-Lemeshow test P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated a good fit. The decision analysis curve was based on the net benefit of different threshold probabilities. When comparing the model with \"full intervention\" and \"no intervention\" strategies, the clinical value of the model was higher when the net benefit was obvious. All tests α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e\u003cp\u003eA total of 320 elderly COPD patients who met the criteria were included in this study, of whom 296 had oral frailty and 24 had non-oral frailty (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was no significant difference in the main baseline characteristics between the training set and the validation set (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the data of the two groups were balanced and comparable, which provided a reliable basis for subsequent model validation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate analysis of oral frailty risk in elderly patients with COPD\u003c/h2\u003e\u003cp\u003eThe results of the univariate analysis are shown (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were statistically significant differences in the incidence of oral frailty among elderly COPD patients in terms of age, malnutrition, types of chronic diseases, degree of dyspnea, and intact teeth (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis of oral frailty(n\u0026thinsp;=\u0026thinsp;320)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;320)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-oral frailty\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOral frailty\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;296)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMNA -SF sum, M (Q₁, Q₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.00, 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.50 (4.00, 13.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00 (2.00, 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGSEOH sum, M (Q₁, Q₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.00 (59.00, 65.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.50 (59.75, 66.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.00 (59.00, 65.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRAIL sum, M (Q₁, Q₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.00, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.00, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.00, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216 (67.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e201 (67.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104 (32.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95 (32.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=33.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129 (40.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (95.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106 (35.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99 (30.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98 (33.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80\u0026ndash;89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69 (21.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (23.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23 (7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (7.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonnel of public institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (16.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (18.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional and technical personnel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84 (26.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78 (26.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnterprise employees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93 (29.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (20.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88 (29.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFreelancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (15.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (29.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (14.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (8.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30 (10.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegistered permanent, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e213 (66.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (79.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e194 (65.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107 (33.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (20.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102 (34.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31 (10.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e237 (74.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (70.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e220 (74.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (15.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45 (15.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (8.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (8.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25 (8.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83 (25.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (20.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78 (26.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000-\u0026lt;3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e139 (43.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e127 (42.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3000-\u0026lt;5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53 (16.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (8.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51 (17.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (5.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResident Medical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e193 (60.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e175 (59.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployee Medical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91 (28.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88 (29.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic expense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21 (6.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (8.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (6.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOwn expense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (4.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14 (4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe Course of COPD, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;1 (Year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99 (30.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93 (31.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158 (49.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (29.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e151 (51.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (12.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (33.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31 (10.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (7.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (7.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypes of chronic diseases, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 (kind)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102 (31.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (70.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85 (28.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165 (51.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (29.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e158 (53.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (10.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (11.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18 (6.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypes of oral medications, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 (kind)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89 (27.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (29.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82 (27.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158 (49.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (41.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49 (15.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46 (15.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (7.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (16.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20 (6.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegree of breathing difficulty, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=73.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0 (Grade)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (12.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (66.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (7.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107 (33.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101 (34.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82 (25.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81 (27.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70 (21.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (23.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (6.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (7.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129 (40.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (54.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116 (39.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191 (59.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (45.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180 (60.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSport, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e213 (66.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e198 (66.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107 (33.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98 (33.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe speed of eating, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107 (33.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (41.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97 (32.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e179 (55.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (45.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e168 (56.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (10.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31 (10.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy diet, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85 (26.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82 (27.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e235 (73.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (87.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e214 (72.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOral disease, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102 (31.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96 (32.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e218 (68.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e200 (67.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerfect teeth, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=14.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e188 (58.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (95.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e165 (55.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132 (41.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131 (44.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eZ: Mann-Whitney test, χ\u0026sup2;: Chi-square test, -: Fisher exact M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate logistic regression analysis of oral frailty in elderly COPD patients\u003c/h2\u003e\u003cp\u003eTaking the variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis of the risk of oral frailty in elderly COPD patients as independent variables and oral frailty as the dependent variable, a multivariate Logistic regression analysis was conducted. The results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that malnutrition, types of chronic diseases, and the degree of dyspnea were independent risk factors for oral frailty in elderly COPD patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe logistic regression analysis of oral frailty (n\u0026thinsp;=\u0026thinsp;320)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.18 (0.30\u0026thinsp;~\u0026thinsp;15.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypes of chronic diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 (Kind)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.35 (2.51\u0026thinsp;~\u0026thinsp;216.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4435.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2678391784.60 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7023.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e338565589.44 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMNA -SF sum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72 (0.60\u0026thinsp;~\u0026thinsp;0.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegree of breathing difficulty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0 (Grade)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.82 (2.56\u0026thinsp;~\u0026thinsp;203.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.30 (1.87\u0026thinsp;~\u0026thinsp;703.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3391.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3924539812.80 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6204.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e500640389.64 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of a nomogram of oral frailty in elderly COPD patients and evaluation of its testing efficacy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the variables that were meaningful in the multivariate analysis, a nomogram was constructed using R Studio to visualize the risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the total score of micronutritional assessment screening in elderly COPD patients, the degree of dyspnea, and the type of chronic disease, the total score could be calculated corresponding to the nomogram to predict the probability of oral frailty. The area under the receiver operating characteristic curve of the training set was 0.97(95%CI:0.94-1.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and that under the receiver operating characteristic curve of the validation set was 0.92(95%CI:0.83-1.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The AUC values of both the training set and the validation set were greater than 0.8, indicating that the model has a strong ability to distinguish oral frailty. The calibration curve shows that the risk of oral frailsia predicted by the model is highly consistent with the actual situation (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and the Hosmer-Lemeshow test (P\u0026thinsp;=\u0026thinsp;0.999 for the training set and P\u0026thinsp;=\u0026thinsp;0.727 for the validation set) indicates a high degree of model fit. The decision curves of the oral frailty prediction model show that the net benefits of the decision curves corresponding to the models in both the training set and the validation set are higher than those of the \"total intervention\" and \"no intervention\" strategies, indicating that the model has high clinical application value. (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on the theoretical guidance of the health ecology model, this study comprehensively integrated multi-dimensional factors such as personal innate characteristics, psychological and behavioral patterns, family and community network, working and living conditions, and policy environment of elderly COPD patients, and constructed a nomogram model for oral frailty in elderly COPD patients. The results of the study showed that the incidence of oral weakness in elderly COPD patients was as high as 92.5%, which was consistent with the study of Liang Yuanjun et al. (96.1%) \u003csup\u003e[10]\u003c/sup\u003e. Compared with the community elderly (70.3%)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the rural elderly (44.7%)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the elderly in nursing homes (31.0)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the elderly patients with Parkinson's disease (28.67%)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the elderly patients with stroke (47.8%)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and the prevalence of oral frailty in patients with diabetes mellitus (45.4%)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The high prevalence of oral frailty in elderly COPD patients may be due to the effect of long-term drug use on the oral microenvironment in elderly COPD patients. Some patients with COPD require long-term use of antibiotics, inhaled glucocorticoids, or long-acting β₂ receptor agonists. These drugs[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] may inhibit salivary gland function, resulting in decreased salivary secretion and changes in physical and chemical properties (such as increased viscosity and decreased buffering capacity). It can lead to a series of oral problems, such as dry mouth, dental caries, periodontal disease, and fungal infection, and eventually increase the risk of oral frailty. Previous studies have suggested[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] that for such patients, increasing the frequency of oral cleaning (such as increasing the frequency of brushing teeth and gargling) and gargling 2%-4% sodium bicarbonate solution regularly for oral cleaning can effectively reduce the incidence of oral fungal infection and improve oral health.\u003c/p\u003e\u003cp\u003eThe results of this study show that the types of chronic diseases are risk factors for oral frailty in elderly COPD patients. The more types of chronic diseases elderly COPD patients have, the higher the probability of oral frailty, which is consistent with the research of Fan Xiaoli et al[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. When multiple chronic diseases (such as diabetes, cardiovascular diseases, osteoporosis, etc.) coexist, Multiple types of drugs, such as bronchodilators, glucocorticoids, antihypertensive drugs, and hypoglycemic drugs need to be used in combination. Drug interactions may inhibit salivary gland function, alter the balance of oral flora, leading to dry mouth, weakened mucosal barrier, and increased risk of infection[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The pathological mechanisms of different chronic diseases can produce synergistic effects. For instance, the hyperglycemic state of diabetes and the chronic inflammation of COPD jointly disrupt the oral immune microenvironment, accelerating the destruction of periodontal tissues and the decline in mucosal repair capacity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, the coexistence of multiple diseases is often accompanied by an unbalanced intake of nutrients (such as protein and vitamin deficiencies), which further weakens the metabolic function of oral tissues, exacerbates the decline of oral muscle strength, abnormal sensations, and functional degeneration[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, clinical medical staff need to optimize drug treatment plans, reduce adverse drug reactions, maintain the balance of oral flora, and lower the incidence of infections. When COPD patients have other diseases, medical staff should consider the treatment plans for different diseases, pay attention to the mutual influence among diseases, keep the patients' oral immune microenvironment normal, and at the same time, patients with multiple coexisting diseases should pay attention to supplementing protein and vitamins to meet the body's nutritional needs.\u003c/p\u003e\u003cp\u003eThis study showed that the lower the MNA screening score, the more prone to oral frailty, and the risk of oral frailty increased in patients with malnutrition (MNA screening score 0\u0026ndash;7). Consistent with the study by Iwasaki et al[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], malnutrition determined using the MNA-SF score was directly related to oral weakness. Studies have shown that the direct manifestations of oral frailty, such as masticatory dysfunction and swallowing dysfunction, are important independent risk factors for malnutrition. Both of them affect the mechanical processing and transport of food, forming a vicious circle of \"oral function degradation, nutrient intake limitation, and body metabolic imbalance\"[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, medical and nursing staff should pay close attention to the nutritional status of patients and supplement nutrients according to the patient's constitution. For elderly patients with chewing inconvenience, they can ensure nutritional intake by adjusting the diet form to meet the body's metabolic needs.\u003c/p\u003e\u003cp\u003eThis study also shows that elderly COPD patients with severe dyspnea are more likely to suffer from oral frailty, which is consistent with the research of Chen Zongmei et al\u003csup\u003e[10]\u003c/sup\u003e. When COPD patients experience severe dyspnea due to airway obstruction or decreased lung function, they are more likely to compensate through mouth breathing. This abnormal breathing pattern may lead to accelerated evaporation of water from the oral mucosa (causing dry mouth) and persistent spasms of oral muscle groups (such as masticatory muscles and tongue muscles). Persistent dry mouth can weaken the function of the oral mucosal barrier and increase the risk of dental caries, periodontal disease, and fungal infections. Long-term spasms of the oral muscle groups may accelerate the degenerative changes of muscle fibers, leading to functional degeneration such as decreased tongue pressure and reduced chewing efficiency, and ultimately inducing oral weakness. Therefore, medical staff need to optimize drug treatment plans and guide COPD patients to undergo long-term oxygen therapy and pulmonary rehabilitation training measures to alleviate breathing difficulties. In addition, increasing the frequency of oral hygiene for COPD patients, applying 4℃ coconut water spray (to relieve dry mouth), acupoint massage (to relax oral muscle groups), and oral function training (such as tongue muscle strength training and chewing function rehabilitation) can directly improve the oral microenvironment and prevent further deterioration of oral function[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study showed that oral frailty was present in the majority of elderly COPD patients. Types of chronic diseases, MNA screening score, and degree of dyspnea were the factors affecting the occurrence of oral frailty. In our study, a visual nomogram prediction model based on the three core risk factors was constructed, and individualized oral frailty risk prediction probability could be generated by quantitatively calculating the cumulative scores of each indicator. The application of this tool can help to accurately identify high-risk groups and provide a basis for early targeted intervention measures, so as to optimize the allocation of public health resources and reduce the medical expenditure burden of the elderly population, which has significant health economic value.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eAlthough the oral frailty risk prediction model constructed in this study has good screening efficiency, the visual nomogram tool has the advantages of convenient operation and intuitive results, which can effectively quantify the risk of oral frailty in the elderly. However, this study has some limitations. First of all, although the OF-8 questionnaire used in this study is convenient for screening oral frailty on a large scale, the data from the questionnaire come from subjects' self-reports, which may affect the objectivity of the results. Future research should consider the combination of subjective and objective survey tools to improve the authenticity and reliability of the assessment results. Secondly, the representative sample selected in this study is limited, and a large and multi-center population can be selected for further research. Finally, this study only used internal validation to evaluate the model's performance; to further evaluate the applicability of the model, future studies need to conduct external validation of the model.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with the Declaration of Helsinki\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants were performed in accordance with the ethical standards of the Declaration of Helsinki (2013)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants or their legally authorized representatives before enrollment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQX took the lead in writing the original draft and contributed to validation, methodology design, formal analysis, data curation, and conceptualization, and was a major contributor in writing the manuscript. GSS participated in formal analysis and data curation. ZHX was involved in validation and formal analysis. LYM contributed to validation and conceptualization. ZHJ was responsible for reviewing, editing, supervision, conceptualization, methodology, and project administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWoodruff PG, Agusti A, Roche N, Singh D, Martinez FJ. Current concepts in targeting chronic obstructive pulmonary disease pharmacotherapy: making progress towards personalised management. Lancet. 2015;385(9979):1789\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarudadri S, Woodruff PG. Targeting Chronic Obstructive Pulmonary Disease Phenotypes, Endotypes, and Biomarkers. 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Gerontology. 2022;68(4):377\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"The elderly, Chronic obstructive pulmonary disease, Oral Frailty, A nomogram, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7400728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7400728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Elderly patients with COPD often have oral health problems, such as dry oral mucosa, tooth loss, and gum disease. Dry oral mucosa makes it difficult for food to slide and chew in the mouth. Tooth loss or gum disease can affect chewing and occlusal functions, causing difficulty in swallowing during meals in elderly COPD patients and increasing the risk of dysphagia. Oral weakness is an independent risk factor for dysphagia in elderly patients with COPD. This study aims to explore the influencing factors of oral frailty in elderly patients with chronic obstructive pulmonary disease (COPD) and to construct a nomogram prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e From July 2025 to August 2025, convenience sampling was used to select 320 rows of elderly patients with chronic obstructive pulmonary disease in a ClassⅲGrade A hospital in Shandong Province as the research objects. Among them, 223 cases were included in the modeling group and 97 cases were included in the validation group. The oral frailty Index-8 was used to screen oral frailty, and a score ≥4 was defined as oral frailty. Multivariate Logistic regression was used to analyze the risk factors of oral frailty in elderly patients with chronic obstructive pulmonary disease. R software was used to establish a risk prediction model and draw a nomogram to visualize the model. ROC curve, Hosmer Lemeshow(H-L) test, calibration curve, and decision curve were used to verify the prediction effect of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe incidence of oral frailty in elderly patients with chronic obstructive pulmonary disease was 92.5%. The influencing factors of oral frailty in elderly patients with chronic obstructive pulmonary disease were nutrition, the degree of dyspnea, and the type of chronic disease. The area under the ROC curve of the modeling group and the validation group was 0.97(95%CI: 0.94-1.00) and 0.92(95%CI: 0.83-1.00), respectively. The calibration curves of the two groups were well fitted (P=0.999, P=0.727). The decision curves of the two groups showed that the model had high clinical practicability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe nomogram prediction model constructed in this study has good efficacy, which is conducive to clinical nursing staff to early screen the risk of oral frailty in elderly patients with chronic pulmonary obstructive disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Committee Approval：\u003c/strong\u003eThis study was approved by the Medical Ethics Review Committee of Jinzhou Medical University (Approval No. JZMULL2025269) on 17 March 2025\u003c/p\u003e","manuscriptTitle":"A risk prediction model for oral frailty in elderly patients with COPD was constructed based on the health ecology model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 12:18:36","doi":"10.21203/rs.3.rs-7400728/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T09:48:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T18:44:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T11:07:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136351325587049127051195166248933115465","date":"2025-09-13T05:58:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330464638989163323010383213651647341329","date":"2025-09-08T06:50:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T13:18:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-04T02:47:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-25T06:54:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-23T09:59:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-08-23T09:56:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ee90262-1de5-4594-9531-45ec0eaa563e","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:02:30+00:00","versionOfRecord":{"articleIdentity":"rs-7400728","link":"https://doi.org/10.1186/s12903-025-07186-6","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2025-12-12 15:57:10","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-09-11 12:18:36","video":"","vorDoi":"10.1186/s12903-025-07186-6","vorDoiUrl":"https://doi.org/10.1186/s12903-025-07186-6","workflowStages":[]},"version":"v1","identity":"rs-7400728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7400728","identity":"rs-7400728","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}