Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease

Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease | 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 Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease Yu-Feng Luo, Xi-Yuan Jiang, Yue-ju Wang, Wen-yan Ren, Long-fei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3866769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular disease (CVD) and frailty frequently coexist in older populations, resulting in a synergistic impact on health outcomes. This study aims to develop a prediction model for the risk of frailty among patients with cardiovascular disease. Methods Using data from the China Health and Retirement Longitudinal Study (CHARLS), a total of 2,457 patients with cardiovascular disease (CVD) in 2011 (n = 1,470) and 2015 (n = 987) were randomly divided into training set (n = 1,719) and validation set (n = 738) at a ratio of 7:3. LASSO regression analysis was used conducted to determine identify the predictor variables with the most significant influence on the model. Stepwise regression analysis and logistic regression model were used to analyze the risk factors of frailty in patients with cardiovascular disease. The prediction model was established by constructing a nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by the concordance index (C-index) and calibration curve. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. Results A total of 360 patients (17.2%) had frailty symptoms. Among the 29 independent variables, it was found that gender, age, pain, grip strength, vision, activities of daily living (ADL), and depression were significantly associated with the risk of frailty in CVD patients. Using these factors to construct a nomogram model, the model has good consistency and accuracy. The AUC values of the prediction model and the internal validation set were 0.859 (95%CI 0.836–0.882) and 0.860 (95%CI 0.827–0.894), respectively. The C-index of the prediction model and the internal validation set were 0.859 (95%CI 0.836–0.882) and 0.887 (95%CI 0.855–0.919), respectively. The Hosmer-Lemeshow test showed that the model's predicted probabilities were in reasonably good agreement with the actual observations. The calibration curve showed that the Nomogram model was consistent with the observed values. The robust predictive performance of the nomogram was confirmed by Decision Curve analysis (DCA). Conclusions This study established and validated a nomogram model, combining gender, age, pain, grip strength, ADL, visual acuity, and depression for predicting physical frailty in patients with cardiovascular disease. Developing this predictive model would be valuable for screening cardiovascular disease patients with a high risk of frailty. Predictive model Frailty Cardiovascular Disease Cardiovascular Disease patients Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Frailty is a multidimensional syndrome characterized by decreased physiological reserve, decreased physical function, and increased susceptibility to stressors[ 1 , 2 ]. Frailty and cardiovascular disease (CVD) often coexist in older populations. Older patients with CVD are most susceptible to developing frailty[ 3 ]. The prevalence of frailty in community-dwelling older adults is estimated to be 10% and rises to 60% in patients with CVD[ 4 ]. CVD patients with frailty experience a 2-fold increase in mortality as compared to their non-frail counterparts.[ 5 ] Thus, developing a risk-predictive model for high-risk cardiovascular disease is urgent for early intervention to delay the onset and progression of frailty in the older population. CVD accounts for about one-third of all deaths globally with 17.8 million deaths in 2017, an increase of 21.1% since 2007[ 6 ]. The relationship between CVD and frailty is complex and bidirectional[ 7 – 9 ]. Compared to non-frail individuals, frail individuals have a 15% or 47% increased risk of CVD, respectively[ 10 , 11 ]. Conversely, the fatality rate for CVD was higher among individuals in the frailty group compared to those in the non-frailty group[ 12 ]. CVD, particularly when associated with events like heart attacks or strokes, can lead to a decline in physical function, muscle strength, and overall health. The cardiovascular system's impairment can decrease exercise capacity and mobility, potentially accelerating the frailty process. Therefore, strategies that maintain cardiovascular health over life are needed to prevent frailty [ 13 ]. Accurate prediction of frailty can help identify high-risk CVD patients and guide treatment decisions. Frailty in patients with cardiovascular disease is the result of multiple factors. Available evidence suggests that frail and CVD patients share common biomarkers of oxidative stress and proinflammatory cytokines, as well as reduced concentrations of endogenous antioxidants. Cardiovascular risk factors, such as insufficient physical activity, smoking, obesity, and an improper diet, can also increase the risk of frailty in cardiovascular patients [ 14 ]. A variety of assessment methods have been developed for frailty. These include validated frailty scales, such as the Fried Frailty Phenotype and the Clinical Frailty Scale, which can be used to investigate frailty status and its influencing factors. Li and colleagues[ 15 ] thoroughly considered the influence of sociodemographic, behavioral, and social support factors on frailty in older adults. Based on these factors, a prediction model of frailty risk in the elderly was established. Sociodemographic, behavioral, and social support level risk factors have been shown to influence vulnerability. However, current models are only based on healthy people, and no prediction model for frailty in patients with cardiovascular disease has been reported. This study aims to screen the factors related to frailty and then construct a prediction model for frailty in patients with cardiovascular disease. Methods Study Study population The China Health and Retirement Longitudinal Study (CHARLS) is a long-term study of middle-aged and older people from all over China. Eligible people are chosen and enrolled through multiple rounds of random sampling [16] from 150 counties in 28 provinces [17]. The sampling method and questionnaire of the CHARLS have been conducted elsewhere [18]. The baseline survey of CHARLS was conducted from June 2011 to 2012. Respondents were followed up every two years with physical measurements and fasting blood samples collected. Face-to-face, computer-assisted personal interviews were used to collect information. Data from the CHARLS database in 2011 and 2015 were selected for analysis. After excluding participants with missing data, 2,417 patients were included in the analyses ( Fig. 1 ) . All participants signed written informed consent. The study was approved by Peking University's Ethics Review Board (IRB00001052-11015) [19]. Frailty status Frailty was assessed by the physical frailty phenotype (PFP), which has been previously validated in the CHARLS cohort and other cohorts [ 20 ]. It contains five components: weakness, slowness, exhaustion, a low level of physical activity, and weight loss: 1. weakness was measured using the self-reported item "difficulty in lifting or carrying a weight greater than 5 kg [ 21 ]; 2. Slowness was considered present if participants had difficulty walking 100 m or climbing several flights of stairs without rest, similar to the method used in previous studies [ 21 ]; 3. The Centre for Epidemiological Studies Depression Scale (CESD) had two items that measured exhaustion. Participants met the exhaustion criteria if they answered “Occasionally or a moderate amount of the time (3–4 days)” or “Most or all of the time (5–7 days)” to either of the two questions: “I felt everything I did was an effort during last week” and “I could not get going during last week”; 4. Low activity was identified in participants if they answered “no” to three questions: “During a usual week, did you do any vigorous activities for at least ten minutes continuously?”, “Did you do any moderate physical effort for at least ten minutes continuously?” and “Did you do any walking for at least ten minutes continuously?” 5. Weight loss was defined as unintentional loss of ≥ 5 kg in the past year or current body mass index (BMI) ≤ 18.5 kg/m 2 [ 20 ]. Weight loss is a better indicator of frailty than BMI and energy intake [ 22 ]. Respondents who met three or more criteria were defined as “frail,” otherwise as “non-frail. Cardiovascular disease (CVD) events Cardiovascular events include heart attacks and strokes. Similar to previous studies [ 23 , 24 ], CVD events were assessed by the following questions: " Have you been told by a doctor that you have been diagnosed with a heart attack, angina, coronary heart disease, heart failure, or other heart problems? " Participants who reported having a heart attack or stroke were defined as having cardiovascular disease [ 23 ]. Cofounding variables The variables included socio-demographic, behavioral, health status, and mental health factors. The socio-demographic factors included five items: age, sex, education level, marital status, and permanent address. The behavioral factors included six items: drinking history, smoking history, daily smoking amount, social involvement, sleep quality, and nighttime sleep duration. The health status included eight items: chronic history, waist circumference, grip strength, ADL score, vision, hearing, pain, and cognitive function. Through previous studies chronic histories selected as predictive of frailty were hypertension, dyslipidemia, diabetes, cancer, chronic lung disease, psychiatric problems, memory-related diseases, arthritis or rheumatism, liver disease, kidney disease, digestive disease, or asthma[ 25 – 30 ]. Mental health factors included two items: depression and life satisfaction[ 31 ]. Statistical methods The rank-sum test was used to analyze continuous variables, and the chi-square test was used to analyze categorical variables. The dataset was randomly divided into a training set (n = 1,719) and a validation set (n = 738) at a ratio of 7:3[ 32 ]. A nomogram was employed to depict the risk of frailty in individuals with cardiovascular disease. The model was established and validated using the minimum absolute contraction and selection operator (LASSO) analysis [ 33 – 35 ]. The features with non-zero coefficients in the LASSO regression model were selected and combined with the features selected in the LASSO regression model. The prediction model was constructed by multivariate logistic regression analysis. Features are considered as odds ratios (OR) and p-values of 95% confidence intervals (CI). The statistical significance levels were bilateral. Variables with P < 0.05 were included in the nomogram model. The multiple imputation method addressed missing data with a maximum missing value not exceeding 30% [ 36 ]. Discrimination, accuracy, and clinical validity were employed to validate the prediction model. The area under the receiver operating characteristic (ROC) curve (AUC) assessed the model's discrimination ability. Calibration curves calculated the agreement between predicted probabilities and observed outcomes. Decision curve analysis (DCA) evaluated clinical validity. R software, version 4.3.1, was used for all analyses. All tests were two-sided, and a significance level of P < 0.05 was considered statistically significant. Results Participant characteristics Table 1 shows the demographic and clinical characteristics of the participants. A total of 2,457 patients diagnosed with cardiovascular disease were included. The prevalence of frailty was 17.2%, with 360 of 2457 identified as frailty. 37.8% of the patients were aged 65–74 years. Among them, 997 (40.7%) were males and 1,451 (59.3%) were females. The elderly live in cities (70.4%), have less than a junior high school education (88.5%), and are unmarried (83.4%). There were significant differences in education level, household registration, marital status, and gender between the frail group and the non-frail group (P < 0.05). In the cardiovascular disease patient population, 70% (1,719) were randomly assigned to the training set, and 30% (738) were assigned to the validation set. More details on the comparison between the training and validation sets can be found in the Supplemental Table 1 , where there was no significant difference between the two groups (P > 0.05). Table 1 Baseline characteristics of the study population Variables Total Non-Frailty Frailty P 2457 n = 2097 n = 360 ADL score 6.00 [6.00, 6.00] 6.00 [6.00, 6.00] 6.00 [6.00, 6.00] < 0.001 Cognitive function 18.00 [14.00, 22.00] 19.00 [14.00, 22.00] 14.00 [9.50, 18.00] < 0.001 Grip strength (%) 558 (24.0) 415 (20.5) 143 (47.7) < 0.001 Waistline (cm) 88.50 [81.00, 97.00] 88.60 [81.40, 97.00] 88.00 [79.42, 97.00] 0.236 Nighttime sleep duration (h) 6.00 [5.00, 8.00] 6.00 [5.00, 8.00] 6.00 [4.00, 7.00] 0.001 Smoking per day 20.00 [10.00, 20.00] 20.00 [10.00, 20.00] 10.00 [6.00, 20.00] 0.167 Age, years (%) < 0.001 <55 497 (20.4) 463 (22.2) 34 (9.5) 55–64 722 (29.6) 588 (28.2) 134 (37.5) 65–74 923 (37.8) 812 (39.0) 111 (31.1) ≥ 75 298 (12.2) 220 (10.6) 78 (21.8) Gender (%) < 0.001 Male 997 (40.7) 894 (42.8) 103 (28.7) Female 1451 (59.3) 1195 (57.2) 256 (71.3) Education (%) < 0.001 Less than lower secondary 2175 (88.5) 1831 (87.3) 344 (95.6) Upper secondary or vocational training 226 (9.2) 211 (10.1) 15 (4.2) Tertiary 56 (2.3) 55 (2.6) 1 (0.3) Marital status (%) < 0.001 Married 408 (16.6) 311 (14.8) 97 (26.9) Unmarried 2049 (83.4) 1786 (85.2) 263 (73.1) Permanent address (%) < 0.001 Urban 1271 (70.4) 1046 (68.5) 225 (81.2) Rural 534 (29.6) 482 (31.5) 52 (18.8) Hypertension (%) 1268 (51.8) 1064 (50.9) 204 (56.7) 0.05 Dyslipidemia (%) 626 (26.0) 528 (25.7) 98 (27.5) 0.52 Diabetes (%) 339 (13.9) 270 (13.0) 69 (19.2) 0.002 Cancer (%) 46 (1.9) 38 (1.8) 8 (2.2) 0.746 Chronic lung disease (%) 492 (20.1) 397 (19.0) 95 (26.7) 0.001 Psychiatric problems (%) 58 (2.4) 36 (1.7) 22 (6.1) < 0.001 Memory-related disease (%) 2343 (95.6) 2014 (96.3) 329 (91.4) < 0.001 Arthritis or rheumatism (%) 1184 (48.3) 981 (46.8) 203 (56.5) 0.001 Liver disease (%) 183 (7.5) 153 (7.4) 30 (8.4) 0.585 Kidney disease (%) 288 (11.8) 242 (11.7) 46 (12.8) 0.580 Digestive disease (%) 809 (33.0) 677 (32.3) 132 (36.7) 0.121 Asthma (%) 209 (8.5) 160 (7.6) 49 (13.6) < 0.001 Alcohol consumption (%) 617 (25.1) 566 (27.0) 51 (14.2) < 0.001 Smoking (%) 567 (27.8) 490 (28.4) 77 (24.8) 0.229 Social activities (%) 0.109 Almost daily 662 (52.6) 599 (53.2) 63 (47.7) Almost every week 263 (20.9) 239 (21.2) 24 (18.2) Not regularly 333 (26.5) 288 (25.6) 45 (34.1) Poor sleep quality (%) < 0.001 Rarely or none of the time 994 (40.8) 919 (44.0) 75 (21.5) Some or a little of the time 386 (15.8) 352 (16.9) 34 (9.7) Occasionally or a moderate amount of the time 381 (15.6) 311 (14.9) 70 (20.1) Most or all of the time 675 (27.7) 505 (24.2) 170 (48.7) Depression (%) 838 (36.5) 626 (31.5) 212 (68.6) < 0.001 Life satisfaction (%) < 0.001 Good 1940 (84.3) 1734 (86.7) 206 (68.2) Fair 275 (11.9) 213 (10.7) 62 (20.5) Poor 87 (3.8) 53 (2.6) 34 (11.3) Vision (%) < 0.001 Good 881 (36.2) 793 (38.1) 88 (25.3) Fair 1130 (46.5) 987 (47.4) 143 (41.1) Poor 421 (17.3) 304 (14.6) 117 (33.6) Hearing (%) < 0.001 Good 762 (31.1) 681 (32.5) 81 (22.8) Fair 1168 (47.7) 1019 (48.7) 149 (41.9) Poor 520 (21.2) 394 (18.8) 126 (35.4) Pain (%) 1091 (44.5) 842 (40.2) 249 (69.9) < 0.001 LASSO and logistic regression of patients with CVD Using LASSO regression analysis and 10-fold cross-validation, 29 candidate characteristics were narrowed down to 9 potential candidate predictive predictors based on 2457 patients with cardiovascular disease among socio-demographic, behavioral, and health factors ( Fig. 2 A- 2 B ) to determine the best predictor of the model. Stepwise regression scores further screened seven candidate predictors. Finally, multiple logistic regression was used to establish the prediction model. The variance Inflation Factor (VIF) test reveals VIF values for all variables below 2. These results show no collinearity of the variables in the model and confirm a good fit. The predictive model included variables with a p-value less than 0.05 in multivariate logistic regression, and ultimately, gender, pain, grip strength, age, vision, ADL, and depression were significantly associated with frailty in patients with cardiovascular disease ( Table 2 ) . Table 2 The prediction model with multivariate logistic regression Variable OR 95%CI P Gender Male Reference Female 2.19 1.53,3.16 < 0.001 *** Pain No Reference Yes 2.21 1.56, 3.17 < 0.001 *** Grip strength Weakness Reference strong 2.07 1.47, 2.90 < 0.001 *** Age 1.06 1.04, 1.08 < 0.001 *** Vision Good Reference Fair 1.18 0.80, 1.76 0.401 Poor 1.88 1.22, 2.91 0.004 ** ADL 0.36 0.29, 0.43 < 0.001 *** Depression No Reference Yes 2.31 1.65, 3.25 < 0.001 *** Table 3 C-Index in the Array Based on Training Set and Validation Set Groups C-Index (95% CI) N P Training set 0.859 (0.836–0.882) 1719 < 0.001 Testing set 0.887 (0.855–0.919) 738 < 0.001 The proposed prediction model was illustrated using a nomogram, providing a quantitative tool for predicting the risk of frailty in patients with cardiovascular disease ( Fig. 3 ) . Predictive model evaluation By detecting the occurrence of frailty in patients with cardiovascular disease in the training and validation sets, the AUC value was calculated to evaluate the discrimination of the prediction model. As shown in ( Fig. 4 A- 4 B ) , the AUC value of the prediction model in the training set was 0.859 (95%CI = 0.836–0.882), the specificity was 0.734, and the sensitivity was 0.837. In the validation cohort, the AUC value was 0.860 (95%CI = 0.827–0.894), the specificity was 0.749, and the sensitivity was 0.826. The C-index of the training set was 0.859 (95%CI = 0.836–0.882), reflecting a relatively good discriminative ability. Similarly, the C-index of the validation set was 0.887(95%CI = 0.855–0.919), demonstrating satisfactory prediction results (Table 3). The nomogram model established in this study has good discrimination ability and predictive value and can correctly identify frail and non-frail patients. Calibration charts and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the nomogram (P > 0.05 indicated that the model fit well). The test results showed that the model had a good fit for both the training set (χ2 = 12.586, df = 8, P = 0.1269) and the validation set (χ2 = 11.322, df = 8, P = 0.1841). The calibration plots of the training and validation sets based on the multivariate logistic regression model are shown in ( Fig. 4 A- 4 B ) . The calibration curve of the nomogram showed high agreement between predicted and actual frailty probabilities in the training ( Fig. 5 A ) and validation ( Fig. 5 B ) sets. The DCA method was used to evaluate the clinical validity of the model, and the results are shown in ( Fig. 6 A- 6 B ) . From the decision curve, the net benefit of the prediction model for the internal validation set was significantly higher than the two extreme cases, indicating the nomogram model's superior net benefit and prediction accuracy. Discussion This study thoroughly considered the demographic and clinical characteristics proposed in previous studies that may influence frailty in older adults with cardiovascular disease [ 37 , 38 ]. The incidence of frailty in cardiovascular disease patients was 14.7%, and the incidence of frailty was higher than that in the general population. Based on these factors, the prediction model of frailty risk in the elderly was established, which is helpful for early intervention of high-risk patients and the formulation of measures to prevent frailty. The pathogenesis of frailty is complex and associated with multiple factors. This study revealed that gender was a predictor of frailty in patients with cardiovascular disease. The findings indicated that females with cardiovascular disease were more prone to developing frailty compared to males, aligning with previous research [ 39 , 40 ]. Women are more vulnerable to frailty due to physiological factors such as menopause and cardiovascular/metabolic changes, including insulin resistance, inflammation, and repetitive somatic weight gain[ 41 , 42 ]. Muscular atrophy, physical inactivity, and functional disability are more prevalent in older women than in men. Moreover, women may benefit from different types or intensities of exercise interventions, while men may benefit more from nutritional support [ 43 – 45 ]. Similarly, advancing age was identified as another significant risk factor for frailty in our study. Aging is associated with an increased prevalence of various health issues, including chronic conditions like diabetes, cardiovascular disease, and cancer. Age-related changes weaken the immune system, rendering individuals more susceptible to infections and diseases. Age emerges as an independent risk factor, heightening vulnerability to diverse health conditions and diseases. To maintain health as we age, we must adopt preventive measures such as regular exercise, a balanced diet, and routine physical examinations. Our predictive model revealed an association between pain and frailty, indicating a higher likelihood of frailty in patients with cardiovascular disease experiencing pain. This research aligns with a comprehensive review of both human studies and animal models, affirming the connection between pain and frailty and establishing pain as a predictive factor for frailty [ 46 ]. Longitudinal studies have previously found an association between baseline pain and the incidence of vulnerability during follow-up. This research suggest persistent pain is a risk factor for vulnerability [ 40 ]. The presence of pain may contribute to or expedite the onset of frailty in the elderly by impeding mobility and physical activity, fostering depression, encouraging social isolation, diminishing nutrient intake, and exacerbating co-morbidities[ 47 – 49 ]. These changes may make older adults vulnerable and less likely to adapt effectively to physiological stress [ 47 ]. Conversely, alterations in pain perception and the exacerbation of pain due to neurological, skeletal, immune, and endocrine changes can lead to frailty [ 48 ]. Therefore, routinely assessing pain in patients with cardiovascular disease can empower healthcare providers to stratify risks and devise interventions with a positive impact on reducing frailty and other adverse health outcomes. This study also found that grip strength was an independent predictor of frailty. Patients with cardiovascular disease have lower maximum grip strength of the main hand and are more likely to have weakness. The force measured grip strength applied when squeezing the dynamometer. This method reflects the capacity of hand and forearm muscles to generate power, offering insights into upper body strength and overall health[ 50 ]. This study also found that grip strength was an independent predictor of frailty. Patients with cardiovascular disease have lower maximum grip strength of the main hand and are more likely to have weakness. The force measured grip strength applied when squeezing the dynamometer. This method reflects the capacity of hand and forearm muscles to generate power, offering insights into upper body strength and overall health. The current study also revealed associations between frailty in patients with cardiovascular disease and vision and daily living activities (ADL). A previous large cohort study of Chinese older adults found that those with visual problems, distance vision impairment, near vision impairment, or glaucoma were more likely to develop frailty during follow-up[ 51 ]. More recent longitudinal studies demonstrated that vision impairment was associated with a higher incidence of frailty in older adults[ 52 – 54 ]. Vision impairment reflected chronological and biological aging[ 55 ] and was associated with an increased risk of a range of comorbidities including cardiovascular disease [ 56 ], diabetes [ 57 ], hypertension [ 58 ] and depression [ 59 ]. These systemic disorders are known to be associated with frailty. Cardiovascular disease patients with impaired ADL are more likely to have frailty. A previous cross-sectional study also confirmed the relationship between functional loss and frailty and confirmed that the ADL score predicts frailty [ 38 ]. Older adults with impairments in the ability to perform activities of daily living are more likely to suffer falls and fractures, which may lead to decreased muscle strength and bone mineral density, predisposing the patient to sarcopenia and osteoporosis and thereby increasing the risk of frailty [ 60 ]. Recognizing the impact of ADL limitations and implementing appropriate interventions can effectively mitigate these risks and enhance overall well-being. The present study also found that depression was associated with frailty in patients with CVD. It has been proven that depression and frailty share the same pathophysiological mechanism [ 61 , 62 ]. Depression is not merely a mood disorder but also a significant independent risk factor for vulnerability. Its impact on physical health, cognitive function, social support, self-care, and suicide risk makes it imperative to prioritize adequate mental health services. Integrated care models that emphasize comprehensive assessment and treatment of depression can help mitigate its negative consequences and enhance overall well-being. By recognizing and addressing depression as an independent risk factor, we can promote resilience and reduce vulnerability among individuals experiencing this mental health condition. Nomograms are widely employed as prediction models in various clinical research fields. These quantitative analysis diagrams depict the functional relationships between variables using planar coordinates connected by line segments. They serve to predict the probability of a clinical outcome event by summing up the scores of each predictor, providing a tangible and intuitive tool for risk assessment [ 58 ]. Nomograms for predicting frailty in patients with cardiovascular disease based on population data have not been reported. In this study, we found that gender, age, pain, grip strength, visual acuity, ADL, and depression were the main predictors of frailty in patients with CVD. The prediction model based on these seven frailty development factors has good identification, correction, and clinical validity, indicating that the prediction model is of certain value for effectively identifying individuals at high risk of frailty due to cardiovascular disease. The risk nomogram can quantify the risk ratio in the form of a score, and the probability of a patient's occurrence of a certain outcome can be obtained through simple calculation. Moreover, it can provide personalized risk assessments for each individual with high correlation and accuracy. Therefore, establishing a predictive model for physical frailty in patients with cardiovascular disease is a new achievement of this study. Our study has limitations. Firstly, some potential predictors were not provided in the CHARLS database, including dietary habits, blood indicators, and cardiovascular disease complications. Second, whether the results of this study can be generalized to other regions and countries needs to be further verified using external cohort data. Third, this study was retrospective and did not follow patients with cardiovascular disease. Therefore, more data from patients with long-term follow-up needs to be analyzed to refine the existing nomogram model. Conclusion This study established and validated a nomogram model for predicting physical frailty in patients with cardiovascular disease. Our nomogram model, combining gender, age, pain, grip strength, ADL, visual acuity, and depression, was internally validated as a useful tool for risk assessment. Developing this predictive model would be valuable for screening cardiovascular disease patients at high risk of frailty. Declarations Acknowledgement The authors acknowledged the CHARLS for contributing the data used in this work. Funding This work was supported by the Science and Technology Project of Suzhou (SKY2023122) and Interdisciplinary Basic Frontier Innovation Program of Suzhou Medical College of Soochow University (YXY2304042, YXY2304056). Approval of the research protocol: The protocol of CHARLS was approved by the Ethical Review Committee of Peking University. Informed Consent: All subjects enrolled signed informed consent. 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Theou, O., et al., Modifications to the frailty phenotype criteria: Systematic review of the current literature and investigation of 262 frailty phenotypes in the Survey of Health, Ageing, and Retirement in Europe . Ageing Res Rev, 2015. 21: p. 78–94. Chin, A.P.M.J., et al., How to select a frail elderly population? A comparison of three working definitions . J Clin Epidemiol, 1999. 52(11): p. 1015–21. Li, H., et al., Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle-Aged and Older Chinese Adults . JAMA Netw Open, 2019. 2(12): p. e1916591. Xie, W., et al., Cognitive Decline Before and After Incident Coronary Events (vol 73, pg 3041 , 2019). Journal of the American College of Cardiology, 2019. 74(9): p. 1274–1274. Perumareddi, P., Prevention of Hypertension Related to Cardiovascular Disease . Prim Care, 2019. 46(1): p. 27–39. Che, B., et al., Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data . Cardiovasc Diabetol, 2023. 22(1): p. 34. Caussy, C., A. Aubin, and R. Loomba, The Relationship Between Type 2 Diabetes, NAFLD, and Cardiovascular Risk . Curr Diab Rep, 2021. 21(5): p. 15. Ramalho, S.H.R. and A.M. Shah, Lung function and cardiovascular disease: A link . Trends Cardiovasc Med, 2021. 31(2): p. 93–98. England, B.R., et al., Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications . BMJ, 2018. 361: p. k1036. Meyer, P.W., et al., Rheumatoid arthritis and risk of cardiovascular disease . Cardiovasc J Afr, 2018. 29(5): p. 317–321. Mohebbi, M., et al., Psychometric properties of a short form of the Center for Epidemiologic Studies Depression (CES-D-10) scale for screening depressive symptoms in healthy community dwelling older adults . Gen Hosp Psychiatry, 2018. 51: p. 118–125. Wu, W.T., et al., Data mining in clinical big data: the frequently used databases, steps, and methodological models . Mil Med Res, 2021. 8(1): p. 44. Sauerbrei, W., P. Royston, and H. Binder, Selection of important variables and determination of functional form for continuous predictors in multivariable model building . Stat Med, 2007. 26(30): p. 5512–28. Friedman, J., T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent . J Stat Softw, 2010. 33(1): p. 1–22. Hu, J.Y., et al., When to consider logistic LASSO regression in multivariate analysis? Eur J Surg Oncol, 2021. 47(8): p. 2206. Sullivan, T.R., et al., Multiple imputation for handling missing outcome data when estimating the relative risk . BMC Med Res Methodol, 2017. 17(1): p. 134. Xue, Q.L., The frailty syndrome: definition and natural history . Clin Geriatr Med, 2011. 27(1): p. 1–15. Sousa, A.C., et al., Frailty syndrome and associated factors in community-dwelling elderly in Northeast Brazil . Arch Gerontol Geriatr, 2012. 54(2): p. e95-e101. Hou, Y., et al., Associations of frailty with cardiovascular disease and life expectancy: A prospective cohort study . Arch Gerontol Geriatr, 2022. 99: p. 104598. Quach, J., et al., The impact of cardiovascular health and frailty on mortality for males and females across the life course . BMC Med, 2022. 20(1): p. 394. Mahendru, A.A. and E. Morris, Cardiovascular disease in menopause: does the obstetric history have any bearing? Menopause Int, 2013. 19(3): p. 115–20. Crimmins, E.M., J.K. Kim, and A. Sole-Auro, Gender differences in health: results from SHARE, ELSA and HRS . Eur J Public Health, 2011. 21(1): p. 81–91. Baker, A.H. and J. Wardle, Sex differences in fruit and vegetable intake in older adults . Appetite, 2003. 40(3): p. 269–75. Shivappa, N., et al., The Relationship Between the Dietary Inflammatory Index and Incident Frailty: A Longitudinal Cohort Study . J Am Med Dir Assoc, 2018. 19(1): p. 77–82. Apostolo, J., et al., Effectiveness of interventions to prevent pre-frailty and frailty progression in older adults: a systematic review . JBI Database System Rev Implement Rep, 2018. 16(1): p. 140–232. D'Agnelli, S., et al., Frailty and pain, human studies and animal models . Ageing Res Rev, 2022. 73: p. 101515. Shega, J.W., et al., Persistent pain and frailty: a case for homeostenosis . J Am Geriatr Soc, 2012. 60(1): p. 113–7. Lohman, M.C., et al., Incorporating Persistent Pain in Phenotypic Frailty Measurement and Prediction of Adverse Health Outcomes . J Gerontol A Biol Sci Med Sci, 2017. 72(2): p. 216–222. Tse, M.M.Y., et al., Frailty is associated with pain and cognitive function in older people in post-acute care settings . Geriatr Nurs, 2020. 41(5): p. 530–535. Lauretani, F., et al., Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol (1985), 2003. 95(5): p. 1851-60. Shang, X., et al., Associations of vision impairment and eye diseases with frailty in community-dwelling older adults: a nationwide longitudinal study in China . Br J Ophthalmol, 2022. Swenor, B.K., et al., Visual Impairment and Frailty: Examining an Understudied Relationship . J Gerontol A Biol Sci Med Sci, 2020. 75(3): p. 596–602. Liljas, A.E.M., et al., Self-reported vision impairment and incident prefrailty and frailty in English community-dwelling older adults: findings from a 4-year follow-up study . J Epidemiol Community Health, 2017. 71(11): p. 1053–1058. Varadaraj, V., et al., Near Vision Impairment and Frailty: Evidence of an Association . Am J Ophthalmol, 2019. 208: p. 234–241. Mitnitski, A.B., et al., Frailty, fitness and late-life mortality in relation to chronological and biological age . BMC Geriatr, 2002. 2: p. 1. Hsueh, C.M., et al., Incidence and risk of major heart diseases in middle-aged adults with moderate to severe vision impairment: a population-based cohort study . Br J Ophthalmol, 2019. 103(8): p. 1054–1059. Danet-Lamasou, M., et al., Near Visual Impairment Incidence in Relation to Diabetes in Older People: The Three-Cities Study . J Am Geriatr Soc, 2018. 66(4): p. 699–705. Zheng, D.D., et al., Patterns of Chronic Conditions and Their Association With Visual Impairment and Health Care Use . JAMA Ophthalmol, 2020. 138(4): p. 387–394. Chou, K.L. and I. Chi, Combined effect of vision and hearing impairment on depression in elderly Chinese . Int J Geriatr Psychiatry, 2004. 19(9): p. 825–32. Perna, S., et al., Performance of Edmonton Frail Scale on frailty assessment: its association with multi-dimensional geriatric conditions assessed with specific screening tools . BMC Geriatr, 2017. 17(1): p. 2. Vaughan, L., A.L. Corbin, and J.S. Goveas, Depression and frailty in later life: a systematic review . Clin Interv Aging, 2015. 10: p. 1947–58. Soysal, P., et al., Relationship between depression and frailty in older adults: A systematic review and meta-analysis . Ageing Res Rev, 2017. 36: p. 78–87. Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3866769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267692152,"identity":"13a8dc8b-e60a-4d12-a706-6019fc57db9c","order_by":0,"name":"Yu-Feng Luo","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Feng","middleName":"","lastName":"Luo","suffix":""},{"id":267692153,"identity":"7da54a9d-0c4f-4ae6-bfdd-2947ec8106a5","order_by":1,"name":"Xi-Yuan Jiang","email":"","orcid":"","institution":"Kunshan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xi-Yuan","middleName":"","lastName":"Jiang","suffix":""},{"id":267692154,"identity":"4fa80642-063d-4be2-9170-c6f6a93a6602","order_by":2,"name":"Yue-ju Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yue-ju","middleName":"","lastName":"Wang","suffix":""},{"id":267692155,"identity":"07e468d7-f2fa-48a3-8be7-1c7dc923f657","order_by":3,"name":"Wen-yan Ren","email":"","orcid":"","institution":"Cambridge-Suda Genomic Resource Center, Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Wen-yan","middleName":"","lastName":"Ren","suffix":""},{"id":267692156,"identity":"674b0b30-502e-4cb7-b7ec-bbb36de3b281","order_by":4,"name":"Long-fei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACPuYDDAwfGBgYG4AcCaK0sLElMDDOIFkLMw+JWnjMpG3+HJbtb2A+eJuHwS6POC25bYeNZxxgS7bmYUguJqxFvnebdG7D4cSGA0C9PAwHEhsI28K7Tdriz+HE+Qf4v5GghYHtcOKGAzxsxGrh/2zZ25ZuvPEwm7HlHINkwlr42dgSb/z4Yy0773jzwxtvKuwIa0EAZhBhQLz6UTAKRsEoGAV4AACMjjSv1xYaWgAAAABJRU5ErkJggg==","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Long-fei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-01-15 14:20:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3866769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3866769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49893174,"identity":"209e396b-c2cc-467e-b8df-bf9864a38f2f","added_by":"auto","created_at":"2024-01-19 21:06:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33716,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants selection from the China Health and Retirement Longitudinal Study (CHARLS).\u003c/p\u003e","description":"","filename":"Figure144.png","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/ec9bf5f224af9ca8dc0817b9.png"},{"id":49893177,"identity":"eef2221d-5ae6-4d14-9992-c892693d9e13","added_by":"auto","created_at":"2024-01-19 21:06:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":690302,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic and clinical feature selection using the LASSO regression model.\u003c/p\u003e\n\u003cp\u003e(A) A coefficient profile was generated according to the logarithmic (lambda) sequence, and the optimal lambda produced non-zero coefficients. (B) The LASSO model's optimal parameter (lambda) was selected via tenfold cross-validation using minimum criteria. The partial likelihood deviation (binomial deviation) curve was plotted relative to the log (lambda). A virtual vertical line at the optimal value was drawn using one SE of minimum criterion (the 1-SE criterion).\u003c/p\u003e","description":"","filename":"Figure240.png","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/70d831e554ab9d7d95d407ea.png"},{"id":49893842,"identity":"a7d5cb6c-ee5f-4ef7-a41e-62a2a8a3a2ca","added_by":"auto","created_at":"2024-01-19 21:22:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10252,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the incidence of frailty in cardiovascular disease patients. The points of each feature were added to obtain the total points, and a vertical line was drawn on the total points to obtain the corresponding “risk of frailty”.\u003c/p\u003e","description":"","filename":"Figure323.png","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/1b764be26c25349ca9b8c924.png"},{"id":49893428,"identity":"edc76bf9-8c5c-49c3-abf9-0677d9ca6953","added_by":"auto","created_at":"2024-01-19 21:14:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309488,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for predicting frailty in the training and validation sets. (A) ROC curve of the model in the training set. \u0026nbsp;(B) ROC curve of the model in the validation set. Abbreviation:\u003cstrong\u003e \u003c/strong\u003eAUC, area under the receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"Figure420.png","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/f8190b0344a476f40f206832.png"},{"id":49893178,"identity":"69a1eaa6-5f37-4c16-a629-5a61c67a057e","added_by":"auto","created_at":"2024-01-19 21:06:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":445684,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for the nomogram. (A) Calibration curve of the model in the training set. (B) Calibration curve of the model in the validation set. The x-axis represents the predicted incidence risk of frailty. The y-axis represents the actual diagnosed Frailty. The diagonal dotted line represents a perfect prediction by an ideal model. The red line represents the model's performance; a closer fit to the diagonal dotted line represents a better prediction. The green solid line is bias-corrected by bootstrapping (B = 1000 repetitions), indicating observed model performance.\u003c/p\u003e","description":"","filename":"Figure515.png","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/19f5a32c42817830d4a7f85d.png"},{"id":49893843,"identity":"5f23d0e7-cc8b-4823-a8a5-90f74c0fcf8a","added_by":"auto","created_at":"2024-01-19 21:22:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":311069,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the incidence risk model of frailty. (A) Decision curve analysis of the training set. (B) Decision curve analysis of the validation set. The x-axis represents the predicted incidence risk of frailty. The y-axis indicates the net benefit. The red line represents the incidence risk model of frailty. The thin solid line represents the assumption that all patients are diagnosed with Frailty. The thick solid line represents the assumption that no patients are diagnosed with frailty.\u003c/p\u003e","description":"","filename":"Figure610.png","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/c0ab7b0f4d82302551976819.png"},{"id":75230867,"identity":"129ff194-66bc-4e81-8c73-8c1ecc1abba1","added_by":"auto","created_at":"2025-02-01 13:31:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2757366,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/a51ee48a-8073-4b79-8098-366e2e7dd5d3.pdf"},{"id":49893426,"identity":"4220d3bf-569b-441d-8a2a-79ef513570e6","added_by":"auto","created_at":"2024-01-19 21:14:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27925,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3866769/v1/c30fd00656f81fa4805b8431.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFrailty is a multidimensional syndrome characterized by decreased physiological reserve, decreased physical function, and increased susceptibility to stressors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Frailty and cardiovascular disease (CVD) often coexist in older populations. Older patients with CVD are most susceptible to developing frailty[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The prevalence of frailty in community-dwelling older adults is estimated to be 10% and rises to 60% in patients with CVD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. CVD patients with frailty experience a 2-fold increase in mortality as compared to their non-frail counterparts.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Thus, developing a risk-predictive model for high-risk cardiovascular disease is urgent for early intervention to delay the onset and progression of frailty in the older population.\u003c/p\u003e \u003cp\u003eCVD accounts for about one-third of all deaths globally with 17.8\u0026nbsp;million deaths in 2017, an increase of 21.1% since 2007[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The relationship between CVD and frailty is complex and bidirectional[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Compared to non-frail individuals, frail individuals have a 15% or 47% increased risk of CVD, respectively[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, the fatality rate for CVD was higher among individuals in the frailty group compared to those in the non-frailty group[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. CVD, particularly when associated with events like heart attacks or strokes, can lead to a decline in physical function, muscle strength, and overall health. The cardiovascular system's impairment can decrease exercise capacity and mobility, potentially accelerating the frailty process. Therefore, strategies that maintain cardiovascular health over life are needed to prevent frailty [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate prediction of frailty can help identify high-risk CVD patients and guide treatment decisions. Frailty in patients with cardiovascular disease is the result of multiple factors. Available evidence suggests that frail and CVD patients share common biomarkers of oxidative stress and proinflammatory cytokines, as well as reduced concentrations of endogenous antioxidants. Cardiovascular risk factors, such as insufficient physical activity, smoking, obesity, and an improper diet, can also increase the risk of frailty in cardiovascular patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A variety of assessment methods have been developed for frailty. These include validated frailty scales, such as the Fried Frailty Phenotype and the Clinical Frailty Scale, which can be used to investigate frailty status and its influencing factors. Li and colleagues[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] thoroughly considered the influence of sociodemographic, behavioral, and social support factors on frailty in older adults. Based on these factors, a prediction model of frailty risk in the elderly was established. Sociodemographic, behavioral, and social support level risk factors have been shown to influence vulnerability. However, current models are only based on healthy people, and no prediction model for frailty in patients with cardiovascular disease has been reported. This study aims to screen the factors related to frailty and then construct a prediction model for frailty in patients with cardiovascular disease.\u003c/p\u003e"},{"header":"Methods Study","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003eThe China Health and Retirement Longitudinal Study (CHARLS) is a long-term study of middle-aged and older people from all over China. Eligible people are chosen and enrolled through multiple rounds of random sampling [16] from 150 counties in 28 provinces [17]. The sampling method and questionnaire of the CHARLS have been conducted elsewhere [18]. The baseline survey of CHARLS was conducted from June 2011 to 2012. Respondents were followed up every two years with physical measurements and fasting blood samples collected. Face-to-face, computer-assisted personal interviews were used to collect information. Data from the CHARLS database in 2011 and 2015 were selected for analysis. After excluding participants with missing data, 2,417 patients were included in the analyses \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. All participants signed written informed consent. The study was approved by Peking University's Ethics Review Board (IRB00001052-11015) [19].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFrailty status\u003c/h2\u003e \u003cp\u003eFrailty was assessed by the physical frailty phenotype (PFP), which has been previously validated in the CHARLS cohort and other cohorts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It contains five components: weakness, slowness, exhaustion, a low level of physical activity, and weight loss:\u003c/p\u003e \u003cp\u003e1. weakness was measured using the self-reported item \"difficulty in lifting or carrying a weight greater than 5 kg [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e];\u003c/p\u003e\u003cp\u003e2. Slowness was considered present if participants had difficulty walking 100 m or climbing several flights of stairs without rest, similar to the method used in previous studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e];\u003c/p\u003e\u003cp\u003e3. The Centre for Epidemiological Studies Depression Scale (CESD) had two items that measured exhaustion. Participants met the exhaustion criteria if they answered \u0026ldquo;Occasionally or a moderate amount of the time (3\u0026ndash;4 days)\u0026rdquo; or \u0026ldquo;Most or all of the time (5\u0026ndash;7 days)\u0026rdquo; to either of the two questions: \u0026ldquo;I felt everything I did was an effort during last week\u0026rdquo; and \u0026ldquo;I could not get going during last week\u0026rdquo;;\u003c/p\u003e\u003cp\u003e4. Low activity was identified in participants if they answered \u0026ldquo;no\u0026rdquo; to three questions: \u0026ldquo;During a usual week, did you do any vigorous activities for at least ten minutes continuously?\u0026rdquo;, \u0026ldquo;Did you do any moderate physical effort for at least ten minutes continuously?\u0026rdquo; and \u0026ldquo;Did you do any walking for at least ten minutes continuously?\u0026rdquo;\u003c/p\u003e\u003cp\u003e5. Weight loss was defined as unintentional loss of \u0026ge;\u0026thinsp;5 kg in the past year or current body mass index (BMI)\u0026thinsp;\u0026le;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Weight loss is a better indicator of frailty than BMI and energy intake [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003eRespondents who met three or more criteria were defined as \u0026ldquo;frail,\u0026rdquo; otherwise as \u0026ldquo;non-frail.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCardiovascular disease (CVD) events\u003c/h2\u003e \u003cp\u003eCardiovascular events include heart attacks and strokes. Similar to previous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], CVD events were assessed by the following questions: \" Have you been told by a doctor that you have been diagnosed with a heart attack, angina, coronary heart disease, heart failure, or other heart problems? \" Participants who reported having a heart attack or stroke were defined as having cardiovascular disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCofounding variables\u003c/h2\u003e \u003cp\u003eThe variables included socio-demographic, behavioral, health status, and mental health factors.\u003c/p\u003e \u003cp\u003eThe socio-demographic factors included five items: age, sex, education level, marital status, and permanent address. The behavioral factors included six items: drinking history, smoking history, daily smoking amount, social involvement, sleep quality, and nighttime sleep duration. The health status included eight items: chronic history, waist circumference, grip strength, ADL score, vision, hearing, pain, and cognitive function. Through previous studies chronic histories selected as predictive of frailty were hypertension, dyslipidemia, diabetes, cancer, chronic lung disease, psychiatric problems, memory-related diseases, arthritis or rheumatism, liver disease, kidney disease, digestive disease, or asthma[\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Mental health factors included two items: depression and life satisfaction[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eThe rank-sum test was used to analyze continuous variables, and the chi-square test was used to analyze categorical variables. The dataset was randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;1,719) and a validation set (n\u0026thinsp;=\u0026thinsp;738) at a ratio of 7:3[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A nomogram was employed to depict the risk of frailty in individuals with cardiovascular disease. The model was established and validated using the minimum absolute contraction and selection operator (LASSO) analysis [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The features with non-zero coefficients in the LASSO regression model were selected and combined with the features selected in the LASSO regression model. The prediction model was constructed by multivariate logistic regression analysis. Features are considered as odds ratios (OR) and p-values of 95% confidence intervals (CI). The statistical significance levels were bilateral. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the nomogram model. The multiple imputation method addressed missing data with a maximum missing value not exceeding 30% [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Discrimination, accuracy, and clinical validity were employed to validate the prediction model. The area under the receiver operating characteristic (ROC) curve (AUC) assessed the model's discrimination ability. Calibration curves calculated the agreement between predicted probabilities and observed outcomes. Decision curve analysis (DCA) evaluated clinical validity. R software, version 4.3.1, was used for all analyses. All tests were two-sided, and a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic and clinical characteristics of the participants. A total of 2,457 patients diagnosed with cardiovascular disease were included. The prevalence of frailty was 17.2%, with 360 of 2457 identified as frailty. 37.8% of the patients were aged 65\u0026ndash;74 years. Among them, 997 (40.7%) were males and 1,451 (59.3%) were females. The elderly live in cities (70.4%), have less than a junior high school education (88.5%), and are unmarried (83.4%). There were significant differences in education level, household registration, marital status, and gender between the frail group and the non-frail group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the cardiovascular disease patient population, 70% (1,719) were randomly assigned to the training set, and 30% (738) were assigned to the validation set. More details on the comparison between the training and validation sets can be found in the \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e, where there was no significant difference between the two groups (P\u0026thinsp;\u0026gt;\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\u003eBaseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Frailty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2457\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2097\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;360\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00 [6.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00 [6.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00 [6.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.00 [14.00, 22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.00 [14.00, 22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.00 [9.50, 18.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrip strength (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e558 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaistline (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.50 [81.00, 97.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.60 [81.40, 97.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.00 [79.42, 97.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNighttime sleep duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00 [5.00, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00 [5.00, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00 [4.00, 7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.00 [10.00, 20.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.00 [10.00, 20.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00 [6.00, 20.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e497 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e463 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e722 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e588 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e923 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e812 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e298 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e997 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e894 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e1451 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1195 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e256 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than lower secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2175 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1831 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary or vocational training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \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\u003e408 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003e2049 (83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1786 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e263 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePermanent address (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1271 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1046 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e225 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e534 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e482 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1268 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1064 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e528 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e270 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e397 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychiatric problems (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory-related disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2014 (96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e329 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthritis or rheumatism (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1184 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e981 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e809 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e677 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e617 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e566 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e567 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e490 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial activities (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e662 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e599 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost every week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot regularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e333 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor sleep quality (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely or none of the time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e994 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e919 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome or a little of the time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e352 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally or a moderate amount of the time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e381 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost or all of the time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e675 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e505 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e838 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e626 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1940 (84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1734 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e206 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e213 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e881 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e793 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1130 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e987 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e421 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e762 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e681 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1168 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1019 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e520 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e394 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1091 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e842 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249 (69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLASSO and logistic regression of patients with CVD\u003c/h2\u003e \u003cp\u003eUsing LASSO regression analysis and 10-fold cross-validation, 29 candidate characteristics were narrowed down to 9 potential candidate predictive predictors based on 2457 patients with cardiovascular disease among socio-demographic, behavioral, and health factors \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e to determine the best predictor of the model. Stepwise regression scores further screened seven candidate predictors. Finally, multiple logistic regression was used to establish the prediction model. The variance Inflation Factor (VIF) test reveals VIF values for all variables below 2. These results show no collinearity of the variables in the model and confirm a good fit. The predictive model included variables with a p-value less than 0.05 in multivariate logistic regression, and ultimately, gender, pain, grip strength, age, vision, ADL, and depression were significantly associated with frailty in patients with cardiovascular disease \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \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 prediction model with multivariate logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eGender\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.53,3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56, 3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrip strength\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estrong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47, 2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04, 1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80, 1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22, 2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29, 0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.65, 3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eC-Index in the Array Based on Training Set and Validation Set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-Index (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\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\u003eTraining set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.859 (0.836\u0026ndash;0.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTesting set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.887 (0.855\u0026ndash;0.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe proposed prediction model was illustrated using a nomogram, providing a quantitative tool for predicting the risk of frailty in patients with cardiovascular disease \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePredictive model evaluation\u003c/h2\u003e \u003cp\u003eBy detecting the occurrence of frailty in patients with cardiovascular disease in the training and validation sets, the AUC value was calculated to evaluate the discrimination of the prediction model. As shown in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, the AUC value of the prediction model in the training set was 0.859 (95%CI\u0026thinsp;=\u0026thinsp;0.836\u0026ndash;0.882), the specificity was 0.734, and the sensitivity was 0.837. In the validation cohort, the AUC value was 0.860 (95%CI\u0026thinsp;=\u0026thinsp;0.827\u0026ndash;0.894), the specificity was 0.749, and the sensitivity was 0.826. The C-index of the training set was 0.859 (95%CI\u0026thinsp;=\u0026thinsp;0.836\u0026ndash;0.882), reflecting a relatively good discriminative ability. Similarly, the C-index of the validation set was 0.887(95%CI\u0026thinsp;=\u0026thinsp;0.855\u0026ndash;0.919), demonstrating satisfactory prediction results \u003cb\u003e(Table\u0026nbsp;3).\u003c/b\u003e The nomogram model established in this study has good discrimination ability and predictive value and can correctly identify frail and non-frail patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCalibration charts and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the nomogram (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated that the model fit well). The test results showed that the model had a good fit for both the training set (χ2\u0026thinsp;=\u0026thinsp;12.586, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.1269) and the validation set (χ2\u0026thinsp;=\u0026thinsp;11.322, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.1841). The calibration plots of the training and validation sets based on the multivariate logistic regression model are shown in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The calibration curve of the nomogram showed high agreement between predicted and actual frailty probabilities in the training \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e and validation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e sets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe DCA method was used to evaluate the clinical validity of the model, and the results are shown in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. From the decision curve, the net benefit of the prediction model for the internal validation set was significantly higher than the two extreme cases, indicating the nomogram model's superior net benefit and prediction accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study thoroughly considered the demographic and clinical characteristics proposed in previous studies that may influence frailty in older adults with cardiovascular disease [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe incidence of frailty in cardiovascular disease patients was 14.7%, and the incidence of frailty was higher than that in the general population. Based on these factors, the prediction model of frailty risk in the elderly was established, which is helpful for early intervention of high-risk patients and the formulation of measures to prevent frailty.\u003c/p\u003e \u003cp\u003eThe pathogenesis of frailty is complex and associated with multiple factors. This study revealed that gender was a predictor of frailty in patients with cardiovascular disease. The findings indicated that females with cardiovascular disease were more prone to developing frailty compared to males, aligning with previous research [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Women are more vulnerable to frailty due to physiological factors such as menopause and cardiovascular/metabolic changes, including insulin resistance, inflammation, and repetitive somatic weight gain[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Muscular atrophy, physical inactivity, and functional disability are more prevalent in older women than in men. Moreover, women may benefit from different types or intensities of exercise interventions, while men may benefit more from nutritional support [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, advancing age was identified as another significant risk factor for frailty in our study. Aging is associated with an increased prevalence of various health issues, including chronic conditions like diabetes, cardiovascular disease, and cancer. Age-related changes weaken the immune system, rendering individuals more susceptible to infections and diseases. Age emerges as an independent risk factor, heightening vulnerability to diverse health conditions and diseases. To maintain health as we age, we must adopt preventive measures such as regular exercise, a balanced diet, and routine physical examinations.\u003c/p\u003e \u003cp\u003eOur predictive model revealed an association between pain and frailty, indicating a higher likelihood of frailty in patients with cardiovascular disease experiencing pain. This research aligns with a comprehensive review of both human studies and animal models, affirming the connection between pain and frailty and establishing pain as a predictive factor for frailty [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Longitudinal studies have previously found an association between baseline pain and the incidence of vulnerability during follow-up. This research suggest persistent pain is a risk factor for vulnerability [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of pain may contribute to or expedite the onset of frailty in the elderly by impeding mobility and physical activity, fostering depression, encouraging social isolation, diminishing nutrient intake, and exacerbating co-morbidities[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These changes may make older adults vulnerable and less likely to adapt effectively to physiological stress [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Conversely, alterations in pain perception and the exacerbation of pain due to neurological, skeletal, immune, and endocrine changes can lead to frailty [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, routinely assessing pain in patients with cardiovascular disease can empower healthcare providers to stratify risks and devise interventions with a positive impact on reducing frailty and other adverse health outcomes.\u003c/p\u003e \u003cp\u003eThis study also found that grip strength was an independent predictor of frailty. Patients with cardiovascular disease have lower maximum grip strength of the main hand and are more likely to have weakness. The force measured grip strength applied when squeezing the dynamometer. This method reflects the capacity of hand and forearm muscles to generate power, offering insights into upper body strength and overall health[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This study also found that grip strength was an independent predictor of frailty. Patients with cardiovascular disease have lower maximum grip strength of the main hand and are more likely to have weakness. The force measured grip strength applied when squeezing the dynamometer. This method reflects the capacity of hand and forearm muscles to generate power, offering insights into upper body strength and overall health.\u003c/p\u003e \u003cp\u003eThe current study also revealed associations between frailty in patients with cardiovascular disease and vision and daily living activities (ADL). A previous large cohort study of Chinese older adults found that those with visual problems, distance vision impairment, near vision impairment, or glaucoma were more likely to develop frailty during follow-up[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. More recent longitudinal studies demonstrated that vision impairment was associated with a higher incidence of frailty in older adults[\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Vision impairment reflected chronological and biological aging[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and was associated with an increased risk of a range of comorbidities including cardiovascular disease [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], diabetes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], hypertension [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] and depression [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These systemic disorders are known to be associated with frailty. Cardiovascular disease patients with impaired ADL are more likely to have frailty. A previous cross-sectional study also confirmed the relationship between functional loss and frailty and confirmed that the ADL score predicts frailty [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Older adults with impairments in the ability to perform activities of daily living are more likely to suffer falls and fractures, which may lead to decreased muscle strength and bone mineral density, predisposing the patient to sarcopenia and osteoporosis and thereby increasing the risk of frailty [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Recognizing the impact of ADL limitations and implementing appropriate interventions can effectively mitigate these risks and enhance overall well-being.\u003c/p\u003e \u003cp\u003eThe present study also found that depression was associated with frailty in patients with CVD. It has been proven that depression and frailty share the same pathophysiological mechanism [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Depression is not merely a mood disorder but also a significant independent risk factor for vulnerability. Its impact on physical health, cognitive function, social support, self-care, and suicide risk makes it imperative to prioritize adequate mental health services. Integrated care models that emphasize comprehensive assessment and treatment of depression can help mitigate its negative consequences and enhance overall well-being. By recognizing and addressing depression as an independent risk factor, we can promote resilience and reduce vulnerability among individuals experiencing this mental health condition.\u003c/p\u003e \u003cp\u003eNomograms are widely employed as prediction models in various clinical research fields. These quantitative analysis diagrams depict the functional relationships between variables using planar coordinates connected by line segments. They serve to predict the probability of a clinical outcome event by summing up the scores of each predictor, providing a tangible and intuitive tool for risk assessment [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Nomograms for predicting frailty in patients with cardiovascular disease based on population data have not been reported. In this study, we found that gender, age, pain, grip strength, visual acuity, ADL, and depression were the main predictors of frailty in patients with CVD. The prediction model based on these seven frailty development factors has good identification, correction, and clinical validity, indicating that the prediction model is of certain value for effectively identifying individuals at high risk of frailty due to cardiovascular disease. The risk nomogram can quantify the risk ratio in the form of a score, and the probability of a patient's occurrence of a certain outcome can be obtained through simple calculation. Moreover, it can provide personalized risk assessments for each individual with high correlation and accuracy. Therefore, establishing a predictive model for physical frailty in patients with cardiovascular disease is a new achievement of this study.\u003c/p\u003e \u003cp\u003eOur study has limitations. Firstly, some potential predictors were not provided in the CHARLS database, including dietary habits, blood indicators, and cardiovascular disease complications. Second, whether the results of this study can be generalized to other regions and countries needs to be further verified using external cohort data. Third, this study was retrospective and did not follow patients with cardiovascular disease. Therefore, more data from patients with long-term follow-up needs to be analyzed to refine the existing nomogram model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established and validated a nomogram model for predicting physical frailty in patients with cardiovascular disease. Our nomogram model, combining gender, age, pain, grip strength, ADL, visual acuity, and depression, was internally validated as a useful tool for risk assessment. Developing this predictive model would be valuable for screening cardiovascular disease patients at high risk of frailty.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003eThe authors acknowledged the CHARLS for contributing the data used in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Science and Technology Project of Suzhou (SKY2023122) and Interdisciplinary Basic Frontier Innovation Program of Suzhou Medical College of Soochow University (YXY2304042, YXY2304056).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval of the research protocol:\u003c/strong\u003e The protocol of CHARLS was approved by the Ethical Review Committee of Peking University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u0026nbsp;\u003c/strong\u003eAll subjects enrolled signed informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval date of Registry and the Registration No. of the study/trial:\u003c/strong\u003e This study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoogendijk, E.O., et al., \u003cem\u003eFrailty: implications for clinical practice and public health\u003c/em\u003e. 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Ageing Res Rev, 2017. 36: p. 78\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Predictive model, Frailty, Cardiovascular Disease, Cardiovascular Disease patients","lastPublishedDoi":"10.21203/rs.3.rs-3866769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3866769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular disease (CVD) and frailty frequently coexist in older populations, resulting in a synergistic impact on health outcomes. This study aims to develop a prediction model for the risk of frailty among patients with cardiovascular disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing data from the China Health and Retirement Longitudinal Study (CHARLS), a total of 2,457 patients with cardiovascular disease (CVD) in 2011 (n\u0026thinsp;=\u0026thinsp;1,470) and 2015 (n\u0026thinsp;=\u0026thinsp;987) were randomly divided into training set (n\u0026thinsp;=\u0026thinsp;1,719) and validation set (n\u0026thinsp;=\u0026thinsp;738) at a ratio of 7:3. LASSO regression analysis was used conducted to determine identify the predictor variables with the most significant influence on the model. Stepwise regression analysis and logistic regression model were used to analyze the risk factors of frailty in patients with cardiovascular disease. The prediction model was established by constructing a nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by the concordance index (C-index) and calibration curve. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 360 patients (17.2%) had frailty symptoms. Among the 29 independent variables, it was found that gender, age, pain, grip strength, vision, activities of daily living (ADL), and depression were significantly associated with the risk of frailty in CVD patients. Using these factors to construct a nomogram model, the model has good consistency and accuracy. The AUC values of the prediction model and the internal validation set were 0.859 (95%CI 0.836\u0026ndash;0.882) and 0.860 (95%CI 0.827\u0026ndash;0.894), respectively. The C-index of the prediction model and the internal validation set were 0.859 (95%CI 0.836\u0026ndash;0.882) and 0.887 (95%CI 0.855\u0026ndash;0.919), respectively. The Hosmer-Lemeshow test showed that the model's predicted probabilities were in reasonably good agreement with the actual observations. The calibration curve showed that the Nomogram model was consistent with the observed values. The robust predictive performance of the nomogram was confirmed by Decision Curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study established and validated a nomogram model, combining gender, age, pain, grip strength, ADL, visual acuity, and depression for predicting physical frailty in patients with cardiovascular disease. Developing this predictive model would be valuable for screening cardiovascular disease patients with a high risk of frailty.\u003c/p\u003e","manuscriptTitle":"Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 21:06:35","doi":"10.21203/rs.3.rs-3866769/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d10d2f1-0dd6-435b-ba3c-9ab75f25ef41","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-01T13:23:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 21:06:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3866769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3866769","identity":"rs-3866769","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}