Development and Validation of a Nomogram for Predicting Sarcopenia in Community-Dwelling Older Adults

A predictive nomogram based on triglyceride glucose index to body mass index ratio for low appendicular skeletal muscle mass

The aim of this study was to investigate risk factors, develop, and assess the predictive nomogram for low appendicular skeletal muscle mass index (ASMI) in middle-aged and elderly populations. A total of 3,209 inpatients were divided into a Training Set (n = 2,407) and a Validation Set (n = 802). A nomogram was developed using R software for internal validation, and external validation was performed using the Validation Set. Gender (male), age, height, weight, triglyceride levels, alanine aminotransferase levels, alcohol consumption, and the triglyceride-glucose index to body-mass index ratio (TyG/BMI) were identified as predictors for the nomogram of low ASMI. In the Training Set, Q1-Q4 subgroups were performed for TyG/BMI, and logistic regression analysis showed that a TyG/BMI ratio greater than 0.37 was significantly associated with an increased risk of developing low ASMI (P < 0.001), with an area under the receiver operating characteristic curve (AUC) of 0.879 for the nomogram. In the Validation Set, the nomogram also demonstrated excellent calibration and discrimination, with an AUC of 0.881. Decision curve analysis (DCA) indicated excellent clinical utility of the nomogram. The study innovatively used TyG/BMI to predict low ASMI, which can reduce the impact of obesity on the diagnosis of sarcopenia. The nomogram can be effectively used to screen for possible sarcopenia in community settings. Due to the cross-sectional study design and unable to obtain complete data on the assessment of muscle strength, the predictive efficacy of our nomogram model requires further confirmation through external validation by large, multicenter prospective studies on sarcopenia population.

Development and validation of a nomogram for predicting high-burnout risk in nurses

AIM

To develop a predictive model for high-burnout of nurses.

DESIGN

A cross-sectional study.

METHODS

This study was conducted using an online survey. Data were collected by the Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioural, health-related, and occupational variables. Participants were randomly divided into a development set and a validation set. In the development set, multivariate logistic regression analysis was conducted to identify factors associated with high-burnout risk, and a nomogram was constructed based on significant contributing factors. The discrimination, calibration, and clinical practicability of the nomogram were evaluated in both the development and validation sets using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis, respectively. Data analysis was performed using Stata 16.0 software.

RESULTS

A total of 2750 nurses from 23 provinces of mainland China responded, with 1925 participants (70%) in a development set and 825 participants (30%) in a validation set. Workplace violence, shift work, working time per week, depression, stress, self-reported health, and drinking were significant contributors to high-burnout risk and a nomogram was developed using these factors. The ROC curve analysis demonstrated that the area under the curve of the model was 0.808 in the development set and 0.790 in the validation set. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets.

CONCLUSION

This study has developed and validated a predictive nomogram for identifying high-burnout in nurses.

RELEVANCE TO CLINICAL PRACTICE

The nomogram conducted by our study will assist nursing managers in identifying at-high-risk nurses and understanding related factors, helping them implement interventions early and purposefully.

REPORTING METHOD

The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Construction and validation of a presenteeism prediction model for ICU nurses in China

Background

Presenteeism, also known as impaired health productivity, refers to the condition of impaired productivity of an individual due to physiological or mental health problems. ICU, as a place of intensive care for patients with acute and critical illnesses, nurses have long faced the nature of work with high loads, high pressures, and high intensities, which makes them a high prevalence group of presenteeism. Presenteeism not only affects the physical and mental health and work wellbeing of nurses but also reduces the quality of nursing services and affects the life safety of patients, such as increasing the risk of falls during hospitalization, increasing the risk of medication errors, and prolonging the hospitalization time of patients. Therefore, early identification and targeted interventions are crucial to reduce presenteeism among ICU nurses.

Objective

This study aimed to construct and validate a predictive model for presenteeism among ICU nurses.

Design

A cross-sectional study.

Methods

1,225 ICU nurses were convened from January to April 2023 from 25 tertiary and secondary hospitals in Sichuan Province, China. ICU nurses were randomly divided into a development set (n = 859) and a validation set (n = 366) according to a 7:3 ratio. Univariate and multifactorial logistic regression analyses were used to determine the influencing factors for presenteeism, and R software was used to construct a column-line graph prediction model. The differentiation and calibration of the predictive model were evaluated by the area under the curve of subjects' work characteristics (ROC) and the Hosmer-Leme-show test, and the clinical decision curve evaluated the clinical validity of the predictive model.

Results

The presenteeism rate of ICU nurses in the development set was 76.8%. Multifactorial logistic regression analysis showed that independent factors affecting ICU nurses' presenteeism included income per month, physical health status, job satisfaction, perceived work stress, perceived social support, transformational leadership, and occupational coping self-efficacy. In the development set and validation set, the area under the ROC curve was 0.821 and 0.786, respectively; the sensitivity and specificity were 80.6, 69.8 and 80.9%, 65.1%, respectively; the Hosmer-Lemeshow goodness-of-fit was χ 2 = 8.076 (p = 0.426) and χ 2 = 5.134 (p = 0.743), respectively, and the model had relatively good discrimination and consistency. The clinical decision curve showed that the model had good clinical validity.

Conclusion

The predictive model of presenteeism risk for ICU nurses constructed in this study has good predictive ability. The model can effectively identify ICU nurses with high presenteeism and provide a reference basis for developing targeted interventions to reduce presenteeism among ICU nurses.

A sarcopenia prediction model based on the calf maximum muscle circumference measured by ultrasound

BACKGROUND

The correlation between calf circumference（CC）and sarcopenia has been demonstrated, but the correlation between calf maximum muscle circumference (CMMC) measured by ultrasound and sarcopenia has not been reported. We aims to construct a predictive model for sarcopenia based on CMMC in hospitalized older patients.

METHODS

This was a retrospective controlled study of patients > 60 years of age hospitalized in the geriatric department of Hunan Provincial People's Hospital. The patients were thoroughly evaluated by questionnaires, laboratory, and ultrasound examinations, including measuring muscle thickness and calf muscle maximum circumference using ultrasound. Patients were categorized into sarcopenia and non-sarcopenia groups according to the consensus for diagnosis of sarcopenia recommended by the Asian Working Group on Sarcopenia 2019 (AWGS2). Independent predictors of sarcopenia were identified by univariate and multivariate logistic regression analyses, and a predictive model was developed and simplified. The prediction performance of the models was assessed using sensitivity, specificity, and area under the curve (AUC) and compared with independent predictors.

RESULTS

We found that patient age, albumin level (ALB), brachioradialis muscle thickness (BRMT), gastrocnemius lateral head muscle thickness (Glh MT), and calf maximum muscle circumference (CMMC) were independent predictors of sarcopenia in hospitalized older patients. The prediction model was established and simplified to Logistic P = -4.5 + 1.4 × age + 1.3 × ALB + 1.6 × BR MT + 3.7 × CMMC + 1.8 × Glh MT, and the best cut-off value of the model was 0.485. The sensitivity, specificity, and AUC of the model were 0.884 (0.807-0.962), 0.837 (0.762-0.911), and 0.927 (0.890-0.963), respectively. The kappa coefficient between this model and the diagnostic criteria recommended by AWGS2 was 0.709.

CONCLUSION

We constructed a sarcopenia prediction model with five variables: age, ALB level, BR MT, Glh MT, and CMMC. The model could quickly predict sarcopenia in older hospitalized patients.

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.

Development and validation of a nomogram for predicting lung cancer based on acoustic-clinical features

Objective

Lung cancer-with its global prevalence and critical need for early diagnosis and treatment-is the focus of our study. This study aimed to develop a nomogram based on acoustic-clinical features-a tool that could significantly enhance the clinical prediction of lung cancer.

Methods

We reviewed the voice data and clinical information of 350 individuals: 189 pathologically confirmed lung cancer patients and 161 non-lung cancer patients, which included 77 patients with benign pulmonary lesions and 84 healthy volunteers. First, acoustic features were extracted from all participants, and optimal features were selected by least absolute shrinkage and selection operator (LASSO) regression. Subsequently, by integrating acoustic features and clinical features, a nomogram for predicting lung cancer was developed using a multivariate logistic regression model. The performance of the nomogram was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration curve. The clinical utility was estimated by decision curve analysis (DCA) to confirm the predictive value of the nomogram. Furthermore, the nomogram model was compared with predictive models that were developed using six additional machine-learning (ML) methods.

Results

Our acoustic-clinical nomogram model demonstrated a strong discriminative ability, with AUCs of 0.774 (95% confidence interval [CI], 0.716-0.832) and 0.714 (95% CI: 0.616-0.811) in the training and test sets, respectively. The nomogram achieved an accuracy of 0.642, a sensitivity of 0.673, and a specificity of 0.611 in the test set. The calibration curve showed excellent agreement between the predicted and actual values, and the DCA curve underscored the clinical usefulness of our nomogram. Notably, our nomogram model outperformed other models in terms of AUC, accuracy, and specificity.

Conclusion

The acoustic-clinical nomogram developed in this study demonstrates robust discrimination, calibration, and clinical application value. This nomogram, a unique contribution to the field, provides a reliable tool for predicting lung cancer.

Machine learning-based prediction of sarcopenia in community-dwelling middle-aged and older adults: findings from the CHARLS

BACKGROUND

Sarcopenia is a prominent issue among aging populations and associated with poor health outcomes. This study aimed to examine the predictive value of questionnaire and biomarker data for sarcopenia, and to further develop a user-friendly calculator for community-dwelling middle-aged and older adults.

METHODS

We used two waves (2011 and 2013) of the China Health and Retirement Longitudinal Study (CHARLS) to predict sarcopenia, defined by the Asian Working Group for Sarcopenia 2019 criteria. We restricted the analytical sample to adults aged 45 or above (N = 2934). Five machine learning models were used to construct Q-based (only questionnaire variables), Bio-based (only biomarker variables), and combined (questionnaire plus biomarker variables) models. Area under the receiver operating characteristic curve (AUROC) was used for performance assessment. Temporal external validation was performed based on two datasets from CHARLS. Important predictors were identified by Shapley values and coefficients.

RESULTS

Extreme gradient boosting (XGBoost), considering both questionnaire and biomarker characteristics, emerged as the optimal model, and its AUROC was 0.759 (95% CI: 0.747-0.771) at a decision threshold of 0.20 on the test set. Models also performed well on the external datasets. We found that cognitive function was the most important predictor in both Q-based and combined models, and blood urea nitrogen was the most important predictor in the Bio-based model. Other key predictors included education, haematocrit, total cholesterol, drinking, number of chronic diseases, and instrumental activities of daily living score.

CONCLUSIONS

Our findings offer a potential for early screening and targeted prevention of sarcopenia among middle-aged and older adults in the community setting.

Construction and Evaluation of a Predictive Nomogram for Identifying Premature Failure of Arteriovenous Fistulas in Elderly Diabetic Patients

Background

This research aimed to identify risk factors contributing to premature maturation of arteriovenous fistulas (AVF) in elderly diabetic patients and develop a clinical prediction model.

Methods

We conducted a retrospective review of 548 geriatric diabetic patients who underwent AVF creation for maintenance hemodialysis (MHD) at Baoding No 1 Central Hospital between January 2011 and December 2023. Patients were divided into mature (386) and immature (162) groups based on AVF maturation status. Univariate logistic regression analysis and the least absolute shrinkage and selection operator were used to identify independent risk factors, including D-dimer levels, low-density lipoprotein cholesterol levels, internal radial meridian, radial artery plaque presence, and cephalic vein indwelling needle use history. A predictive nomogram was developed specifically for immature AVF in elderly diabetic patients. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

Among elderly patients with diabetes mellitus, the incidence of immature AVF was 29.56%, affecting 162 of 548 individuals. The five-variable model demonstrated an AUROC value of 0.922, with a 95% confidence interval (CI) of 0.870 to 0.947 in the training dataset, and an AUROC of 0.912, accompanied by a 95% CI of 0.880 to 0.935 in the internal validation dataset. The calibration curve, derived from 1000 bootstrap samples, showed good agreement between predicted and observed outcomes. Additionally, both the DCA and CIC exhibited favorable clinical utility and net benefits.

Conclusions

The nomogram prediction model, based on independent risk factors, serves as a valuable tool for accurate prognosis and has potential to aid in establishing and preserving hemodialysis access in elderly diabetic patients, ultimately optimizing their healthcare outcomes.

Handgrip strength and body mass index exhibit good predictive value for sarcopenia in patients on peritoneal dialysis

Aim

The diagnosis of sarcopenia in patients on peritoneal dialysis (PD) in clinics is limited owing to its relatively complicated process and the need for expensive assessment equipment. This study aimed to develop and validate sex-specific nomogram models based on body mass index (BMI), handgrip strength, and other routine follow-up examination indicators to predict sarcopenia in patients on PD.

Methods

From March 2023 to February 2024, 699 eligible patients were recruited from the PD centers of two tertiary hospitals in southeastern China. Routine follow-up examination indicators such as age, BMI, biochemical indicators, dialysis adequacy, handgrip strength, and five-repetition sit-to-stand test, were used as potential predictive variables. Multivariate logistic regression analyses were used to separately determine the predictive factors for men and women. Nomogram models were constructed based on the results of the multivariate analyses, which were internally validated using a bootstrap re-sampling method (n = 2000). Predictive performance was validated using a receiver operating characteristic (ROC) curve.

Results

The prevalence of sarcopenia in Chinese patients on PD was 13.92%. The nomogram models based on multivariate analyses revealed both handgrip strength and BMI as independent predictors of sarcopenia in men and women on PD. The bootstrap-corrected area under the ROC curves of the models was 0.924 (95% CI: 0.888-0.959) and 0.936 (95% CI, 0.906-0.966) for men and women, respectively. The calibration curves of both models demonstrated high consistency between the observed and anticipated values.

Conclusion

The two nomogram models based on BMI and handgrip strength demonstrated good predictive ability for sarcopenia in male and female patients on PD. Subsequently, these may be used as convenient and inexpensive methods for the early detection and timely management of sarcopenia in patients on PD.

Development and validation of a predicative model for identifying sarcopenia in Chinese adults using nutrition indicators (AHLC)

Background

Sarcopenia, a condition characterized by low muscle mass, plays a critical role in the health of older adults. Early identification of individuals at risk is essential to prevent sarcopenia-related complications. This study aimed to develop a predictive model using readily available clinical nutrition indicators to facilitate early detection.

Methods

A total of 1,002 participants were categorized into two groups: 819 with normal skeletal muscle mass (SMM) and 183 with low muscle mass (sarcopenia). A predictive model was developed for sarcopenia risk via multivariate logistic regression, and its performance was assessed using four analyses: receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), a nomogram chart, and external validation. These methods were used to evaluate the model's discriminative ability and clinical applicability.

Results

In the low-SMM group, more females (55.73% vs. 40.42%) and older individuals (median 61 vs. 55 years) were observed. These patients had lower albumin (41.00 vs. 42.50 g/L) and lymphocyte levels (1.60 vs. 2.02 × 109/L) but higher HDL (1.45 vs. 1.16 mmol/L) and calcium levels (2.24 vs. 2.20 mmol/L) (all p < 0.001). Using LASSO regression, we developed a nutritional AHLC (albumin + HDL cholesterol + lymphocytes + calcium) model for sarcopenia risk prediction. AUROC and DCA analyses, as well as nomogram charts and external validation, confirmed the robustness and clinical relevance of the AHLC model for predicting sarcopenia.

Conclusion

Our study employs serum nutrition indicators to aid clinicians in promoting healthier aging. The AHLC model stands out for weight-independent evaluations. This novel approach could assess sarcopenia risk in the Chinese population, thereby enhancing aging and quality of life.

Development and Validation of a Risk Prediction Model for Sarcopenia in Chinese Older Patients with Type 2 Diabetes Mellitus

Purpose

Sarcopenia is a common prevalent age-related disorder among older patients with type 2 diabetes mellitus (T2DM). This study aimed to develop and validate a nomogram model to assess the risk of incident sarcopenia among older patients with T2DM.

Patients and methods

A total of 1434 older patients (≥ 60 years) diagnosed with T2DM between May 2020 and November 2023 were recruited. The study cohort was randomly divided into a training set (n = 1006) and a validation set (n = 428) at the ratio of 7:3. The best-matching predictors of sarcopenia were incorporated into the nomogram model. The accuracy and applicability of the nomogram model were measured by using the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA).

Results

571 out of 1434 participants (39.8%) had sarcopenia. Nine best-matching factors, including age, body mass index (BMI), diabetic duration, glycated hemoglobin A1c (HbA1c), 25 (OH)Vitamin D, nephropathy, neuropathy, nutrition status, and osteoporosis were selected to construct the nomogram prediction model. The AUC values for training and validation sets were 0.800 (95% CI = 0.773-0.828) and 0.796 (95% CI = 0.755-0.838), respectively. Furthermore, the agreement between predicted and actual clinical probability of sarcopenia was demonstrated by calibration curves, the Hosmer-Lemeshow test (P > 0.05), and DCA.

Conclusion

Sarcopenia was prevalent among older patients with T2DM. A visual nomogram prediction model was verified effectively to evaluate incident sarcopenia in older patients with T2DM, allowing targeted interventions to be implemented timely to combat sarcopenia in geriatric population with T2DM.

Development and Validation of PRE-SARC (PREdiction of SARCopenia Risk in Community Older Adults) Sarcopenia Prediction Model

OBJECTIVE

Reliable identification of high-risk older adults who are likely to develop sarcopenia is essential to implement targeted preventive measures and follow-up. However, no sarcopenia prediction model is currently available for community use. Our objective was to develop and validate a risk prediction model for calculating the 1-year absolute risk of developing sarcopenia in an aging population.

METHODS

One prospective population-based cohort of non-sarcopenic individuals aged 60 years or older were used for the development of a sarcopenia risk prediction model and model validation. Sarcopenia was defined according to the 2019 Asian Working Group for Sarcopenia consensus. Stepwise logistic regression was used to identify risk factors for sarcopenia incidence within a 1-year follow-up. Model performance was evaluated using the area under the receiver operating characteristics curve (AUROC) and calibration plot, respectively.

RESULTS

The development cohort included 1042 older adults, among whom 87 participants developed sarcopenia during a 1-year follow-up. The PRE-SARC (PREdiction of SARCopenia Risk in community older adults) model can accurately predict the 1-year risk of sarcopenia by using 7 easily accessible community-based predictors. The PRE-SARC model performed well in predicting sarcopenia, with an AUROC of 87% (95% CI, 0.83-0.90) and good calibration. Internal validation showed minimal optimism, with an adjusted AUROC of 0.85. The prediction score was categorized into 4 risk groups: low (0%-10%), moderate (>10%-20%), high (>20%-40%), and very high (>40%). The PRE-SARC model has been incorporated into an online risk calculator, which is freely accessible for daily clinical applications (https://sarcopeniariskprediction.shinyapps.io/dynnomapp/).

CONCLUSIONS

In community-dwelling individuals, the PRE-SARC model can accurately predict 1-year sarcopenia incidence. This model serves as a readily available and free accessible tool to identify older adults at high risk of sarcopenia, thereby facilitating personalized early preventive approaches and optimizing the utilization of health care resources.

A nomogram to predict the risk of cognitive impairment in patients with depressive disorder

This study was to describe the cognitive function status in patients with depressive disorder and to construct a nomogram model to predict the risk factors of cognitive impairment in these patients. From October 2019 to February 2021, a total of 141 patients with depressive disorder completed the survey in two hospitals. The Montreal cognitive assessment (MoCA) was used with a cutoff score of 26 to differentiate cognitive impairment. Univariable and multivariable logistic regression analyses were conducted to identify independent risk factors. A nomogram was then constructed based on the results of the multivariable logistic regression analysis. The patients had an average MoCA score of 23.99 ± 3.02. The multivariable logistic regression analysis revealed that age (OR: 1.096, 95% CI: 1.042-1.153, p < 0.001), education (OR: 0.065, 95% CI: 0.016-0.263, p < 0.001), depression severity (OR: 1.878, 95% CI: 1.021-3.456, p = 0.043), and sleep quality (OR: 2.454, 95% CI: 1.400-4.301, p = 0.002) were independent risk factors for cognitive impairment in patients with depressive disorder. The area under receiver operating characteristic (ROC) curves was 0.868 (95% CI: 0.807-0.929), indicating good discriminability of the model. The calibration curve of the model and the Hosmer-Lemeshow test (p = 0.571) demonstrated a well-fitted model with high calibration. Age, education, depression severity, and sleep quality were found to be significant predictors of cognitive function. A nomogram model was developed to predict cognitive impairment in patients with depressive disorder, providing a solid foundation for clinical interventions.

Development and validation of a predictive model for the risk of sarcopenia in the older adults in China

BACKGROUND

Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults.

METHODS

This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators.

RESULTS

3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75-0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73-0.79). Hosmer-Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties.

CONCLUSIONS

The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.

A Nomogram for Predicting the Infectious Disease-specific Health Literacy of Older Adults in China

PURPOSE

To identify the predictors of infectious disease-specific health literacy (IDSHL), and establish an easy-to-apply nomogram to predict the IDSHL of older adults.

METHODS

This cross-sectional study included 380 older adults who completed the IDSHL, self-rated health, socio-demographic and other questionnaires. Logistic regression was used to identify the IDSHL predictors. Nomogram was used to construct a predictive model.

RESULTS

Up to 70.1% of older adults had limited IDSHL. Age, education, place of residence, self-rated health, and Internet access were the important influencing factors of IDSHL. The established nomogram model showed high accuracy (receiver operating characteristic curve: 0.848).

CONCLUSIONS

The IDSHL of Chinese older adults was significantly deficient. The constructed nomogram is an intuitive tool for IDSHL prediction that can not only contribute toward rapid screening of high-risk older adults with limited IDSHL but also provide guidance for healthcare providers to develop prevention strategies for infectious diseases.

Sarcopenia in peripheral arterial disease: Establishing and validating a predictive nomogram based on clinical and computed tomography angiography indicators

Purpose

To establish, validate, and clinically evaluate a nomogram for predicting the risk of sarcopenia in patients with peripheral arterial disease (PAD) based on clinical and lower extremity computed tomography angiography (LE-CTA) imaging characteristics.

Methods

Clinical data and CTA imaging features from 281 PAD patients treated between January 1, 2019, and May 1, 2023, at two hospitals were retrospectively analyzed using binary logistic regression to identify the independent risk factors for sarcopenia. These identified risk factors were used to develop a predictive nomogram. The nomogram's effectiveness was assessed through various metrics, including the receiver operating characteristic (ROC) curve, area under the curve (AUC), concordance index (C-index), Hosmer-Lemeshow (HL) test, and calibration curve. Its clinical utility was demonstrated using decision curve analysis (DCA).

Results

Several key independent risk factors for sarcopenia in PAD patients were identified, namely age, body mass index (BMI), history of coronary heart disease (CHD), and white blood cell (WBC) count, as well as the severity of luminal stenosis (P < 0.05). The discriminative ability of the nomogram was supported by the C-index and an AUC of 0.810 (95% confidence interval: 0.757-0.862). A robust concordance between predicted and observed outcomes was reflected by the calibration curve. The HL test further affirmed the model's calibration with a P-value of 0.40. The DCA curve validated the nomogram's favorable clinical utility. Lastly, the model underwent internal validation.

Conclusions

A simple nomogram based on five independent factors, namely age, BMI, history of CHD, WBC count, and the severity of luminal stenosis, was developed to assist clinicians in estimating sarcopenia risk among PAD patients. This tool boasts impressive predictive capabilities and broad utility, significantly aiding clinicians in identifying high-risk individuals and enhancing the prognosis of PAD patients.

Higher remnant cholesterol is associated with an increased risk of amnestic mild cognitive impairment: a community-based cross-sectional study

Background and aims

Amnestic mild cognitive impairment (aMCI) is the most common subtype of MCI, which carries a significantly high risk of transitioning to Alzheimer's disease. Recently, increasing attention has been given to remnant cholesterol (RC), a non-traditional and previously overlooked risk factor. The aim of this study was to explore the association between plasma RC levels and aMCI.

Methods

Data were obtained from Brain Health Cognitive Management Team in Wuhan (https://hbtcm.66nao.com/admin/). A total of 1,007 community-dwelling elders were recruited for this project. Based on ten tools including general demographic data, cognitive screening and some exclusion scales, these participants were divided into the aMCI (n = 401) and normal cognitive groups (n = 606). Physical examinations were conducted on all participants, with clinical indicators such as blood pressure, blood sugar, and blood lipids collected.

Results

The aMCI group had significantly higher RC levels compared to the normal cognitive group (0.64 ± 0.431 vs. 0.52 ± 0.447 mmol/L, p < 0.05). Binary logistics regression revealed that occupation (P<0.001, OR = 0.533, 95%CI: 0.423-0.673) and RC (p = 0.014, OR = 1.477, 95% CI:1.081-2.018) were associated factors for aMCI. Partial correlation analysis, after controlling for occupation, showed a significant negative correlation between RC levels and MoCA scores (r = 0.059, p = 0.046), as well as Naming scores (r = 0.070, p = 0.026). ROC curve analysis demonstrated that RC levels had an independent predictive efficacy in predicting aMCI (AUC = 0.580, 95%CI: 0.544 ~ 0.615, P < 0.001).

Conclusion

Higher RC levels were identified as an independent indicator for aMCI, particularly in the naming cognitive domain among older individuals. Further longitudinal studies are necessary to validate the predictive efficacy of RC.

The association between sedentary behaviour and sarcopenia in older adults: a systematic review and meta-analysis

BACKGROUND

Sedentary behaviour is considered to contribute to sarcopenia when combined with physical inactivity. Whether sedentary behaviour is independently associated with sarcopenia remains controversial. The aim of this study is to explore the association between sedentary behaviour and sarcopenia in older adults in community and long-term care facility settings.

METHODS

Eight electronic databases including MEDLINE, PsycINFO, Wanfang were searched from inception until August 2023. The review included cross-sectional and longitudinal studies concerning the association between sedentary behaviour and sarcopenia among participants over 60 years old. Evidence was pooled by both random-effects meta-analysis and narrative synthesis. Subgroup analyses explored variation according to adjustment of physical activity, settings, and measurements of sedentary behaviour and sarcopenia. Quality assessment for individual studies was performed with the Joanna Briggs Institute (JBI) Critical Appraisal Checklist.

RESULTS

Seventeen articles (16 cross-sectional studies and 1 longitudinal study) of 25,788 participants from community or long-term care facility settings were included. The overall quality of the included studies was rated high. Meta-analysis of 14 cross-sectional studies showed that sedentary behaviour was independently positively associated with sarcopenia: pooled odd ratio 1.36 (95% confidence interval, 1.18-1.58). The independent positive association remained in subgroup analyses by adjustment of physical activity, settings, and measurements of sedentary behaviour and sarcopenia. The narrative analysis corroborated the findings of the meta-analysis and provided additional evidence suggesting that interruptions in sedentary periods were linked to a decreased likelihood of developing sarcopenia.

CONCLUSIONS

The findings support the hypothesis that sedentary behaviour is independently positively associated with sarcopenia in older adults, providing vital indications for the development of strategies to prevent sarcopenia.

SYSTEMATIC REVIEW REGISTRATION

The systematic review protocol has been registered with the PROSPERO database (CRD42022311399).

Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis

BACKGROUND

Muscle mass is important in determining patients' nutritional status. However, measurement of muscle mass requires special equipment that is inconvenient for clinical use. We aimed to develop and validate a nomogram model for predicting low muscle mass in patients undergoing hemodialysis (HD).

METHODS

A total of 346 patients undergoing HD were enrolled and randomly divided into a 70% training set and a 30% validation set. The training set was used to develop the nomogram model, and the validation set was used to validate the developed model. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, a calibration curve, and the Hosmer-Lemeshow test. A decision curve analysis (DCA) was used to evaluate the clinical practicality of the nomogram model.

RESULTS

Age, sex, body mass index (BMI), handgrip strength (HGS), and gait speed (GS) were included in the nomogram for predicting low skeletal muscle mass index (LSMI). The diagnostic nomogram model exhibited good discrimination with an area under the ROC curve (AUC) of 0.906 (95% CI, 0.862-0.940) in the training set and 0.917 (95% CI, 0.846-0.962) in the validation set. The calibration analysis also showed excellent results. The nomogram demonstrated a high net benefit in the clinical decision curve for both sets.

CONCLUSIONS

The prediction model included age, sex, BMI, HGS, and GS, and it can successfully predict the presence of LSMI in patients undergoing HD. This nomogram provides an accurate visual tool for medical staff for prediction, early intervention, and graded management.

Factors Associated with Sarcopenia among Elderly Individuals Residing in Community and Nursing Home Settings: A Systematic Review with a Meta-Analysis

To investigate the factors associated with sarcopenia in elderly individuals residing in nursing homes and community settings, we conducted a systematic search of databases, including MEDLINE, EMBASE, PubMed, Web of Science and Cochrane, up to May 2023. We incorporated a total of 70 studies into our analysis. Our findings revealed that the prevalence of sarcopenia in nursing homes ranged from 25% to 73.7%, while in community settings, it varied from 5.2% to 62.7%. The factors associated with sarcopenia in both nursing homes and community settings included male gender, BMI, malnutrition, and osteoarthritis. In community settings, these factors comprised age, poor nutrition status, small calf circumference, smoking, physical inactivity, cognitive impairment, diabetes, depression and heart disease. Currently, both the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGS) standards are widely utilized in nursing homes and community settings, with the EWGSOP standard being more applicable to nursing homes. Identifying factors associated with sarcopenia is of paramount significance, particularly considering that some of them can be modified and managed. Further research is warranted to investigate the impact of preventive measures on these factors in the management of sarcopenia among elderly individuals residing in nursing homes and community settings.

A nomogram to predict the risk of sarcopenia in older people

The burden of sarcopenia is increasing worldwide. However, most cases of sarcopenia are undiagnosed due to the lack of simple screening tools. This study aimed to develop and validate an individualized and simple nomogram for predicting sarcopenia in older adults. A total of 180 medical examination populations aged ≥60 years were enrolled in this study. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus. The primary data were randomly divided into training and validation sets. Univariate logistic regression analysis was performed to select the risk factors of sarcopenia, which were subjected to the least absolute shrinkage and selection operator for feature selection. A nomogram was established using multivariate logistic regression analysis by incorporating the features selected in the least absolute shrinkage and selection operator regression model. The discrimination and calibration of the predictive model were verified by the concordance index, receiver operating characteristic curve, and calibration curve. In this study, 55 cases of sarcopenia were available. Risk predictors included age, albumin, blood urea nitrogen, grip strength, and calf circumference. The model had good discrimination and calibration capabilities. concordance index was 0.92 (95% confidence interval: 0.84-1.00), and the area under the receiver operating characteristic curve was 0.92 (95% confidence interval: 0.83-1.00) in the validation set. The Hosmer-Lemeshow test had a P value of .94. The predictive model in this study will be a clinically useful tool for predicting the risk of sarcopenia, and it will facilitate earlier detection and therapeutic intervention for sarcopenia.

A Nomogram for Optimizing Sarcopenia Screening in Community-dwelling Older Adults: AB3C Model

OBJECTIVES

Sarcopenia is associated with significantly higher mortality risk, and earlier detection of sarcopenia has remarkable public health benefits. However, the model that predicts sarcopenia in the community has yet to be well identified. The study aimed to develop a nomogram for predicting the risk of sarcopenia and compare the performance with 3 sarcopenia screen models in community-dwelling older adults in China.

DESIGN

Cross-sectional study.

SETTING AND PARTICIPANTS

A total of 966 community-dwelling older adults.

METHODS

A total of 966 community-dwelling older adults were enrolled in the study, with 678 participants grouped into the Training Set and 288 participants grouped into the Validation Set according to a 7:3 randomization. Predictors were identified in the Training Set by univariate and multivariate logistic regression and then combined into a nomogram to predict the risk of sarcopenia. The performance of this nomogram was assessed by calibration, discrimination, and clinical utility.

RESULTS

Age, body mass index, calf circumference, congestive heart failure, and chronic obstructive pulmonary disease were demonstrated to be predictors for sarcopenia. The nomogram (named as AB3C model) that was constructed based on these predictors showed excellent calibration and discrimination in the Training Set with an area under the receiver operating characteristic curve (AUC) of 0.930. The nomogram also showed perfect calibration and discrimination in the Validation Set with an AUC of 0.897. The clinical utility of the nomogram was supported by decision curve analysis. Comparing the performance with 3 sarcopenia screen models (SARC-F, Ishii, and Calf circumference), the AB3C model outperformed the other models regarding sensitivity and AUC.

CONCLUSIONS AND IMPLICATIONS

AB3C model, an easy-to-apply and cost-effective nomogram, was developed to predict the risk of sarcopenia, which may contribute to optimizing sarcopenia screening in community settings.

A nomogram for screening sarcopenia in Chinese type 2 diabetes mellitus patients

OBJECTIVE

Diabetes is an important risk factor for sarcopenia and contributes to poor prognosis; it is important for clinicians to identify sarcopenia early in the population with type 2 diabetes mellitus. Our aim was to establish a clinical screening model of sarcopenia in Chinese patients with type 2 diabetes mellitus.

METHODS

A cross-sectional study was conducted involving 1131 hospitalized patients (62.67 ± 11.25 years) with type 2 diabetes mellitus, including 560 men and 571 women. All muscle/fat parameters were measured by dual energy X-ray absorptiometry and the clinical correlation with sarcopenia was evaluated. The least absolute shrinkage and selection operator was used to select optimal variables and build a nomogram chart of the sarcopenic screening model for patients with type 2 diabetes mellitus, respectively. The area under the receiver operating characteristic curve (AUC), the calibration curve, the analysis of the decision curve, the clinical impact curve, and external validations were used to evaluate discriminative ability and clinical applicability.

RESULTS

The prevalence of sarcopenia in patients with type 2 diabetes mellitus was 30.06 % (340/1131). Compared to the non-sarcopenic group, the sarcopenic group was older, more likely to be men, and had a higher heart rate and lower body mass index (BMI), waist-hip ratio (WHR), upper limb muscle mass, lower limb muscle mass and fat paraments (all P < 0.05). Five independent variables (age, sex, BMI, WHR and heart rate) were selected to construct a nomogram prediction model. The AUC was 0.907 (95 % CI: 0.890-0.925). The calibration curve, decision curve analysis, and clinical impact curves showed a wide range of nomograms with good clinical applicability under threshold probability. Additionally, internal validation showed a good AUC of 0.908 (95 % CI: 0.886-0.928) in the training set and 0.904 (95 % CI: 0.868-0.941) in the testing set, as well as an accuracy of 93.2 % for the screening of sarcopenia in the external validation set.

CONCLUSIONS

Age, sex, BMI, WHR, and heart rate were used to detect sarcopenia in patients with type 2 diabetes mellitus. The novel screening model is an accurate, easy-to-implement and low-cost tool for early identification of sarcopenia in Chinese patients with type 2 diabetes mellitus.

Development of a risk prediction nomogram for sarcopenia in hemodialysis patients

Background

Sarcopenia is associated with various adverse outcomes in hemodialysis patients. However, current tools for assessing and diagnosing sarcopenia have limited applicability. In this study, we aimed to develop a simple and reliable nomogram to predict the risk of sarcopenia in hemodialysis patients that could assist physicians identify high-risk patients early.

Methods

A total of 615 patients undergoing hemodialysis at the First Affiliated Hospital College of Medicine Zhejiang University between March to June 2021 were included. They were randomly divided into either the development cohort (n = 369) or the validation cohort (n = 246). Multivariable logistic regression analysis was used to screen statistically significant variables for constructing the risk prediction nomogram for Sarcopenia. The line plots were drawn to evaluate the effectiveness of the nomogram in three aspects, namely differentiation, calibration, and clinical net benefit, and were further validated by the Bootstrap method.

Results

The study finally included five clinical factors to construct the nomogram, including age, C-reactive protein, serum phosphorus, body mass index, and mid-upper arm muscle circumference, and constructed a nomogram. The area under the ROC curve of the line chart model was 0.869, with a sensitivity and specificity of 77% sensitivity and 83%, the Youden index was 0.60, and the internal verification C-statistic was 0.783.

Conclusions

This study developed and validated a nomogram model to predict the risk of sarcopenia in hemodialysis patients, which can be used for early identification and timely intervention in high-risk groups.

The effect of denture-wearing on physical activity is associated with cognitive impairment in the elderly: A cross-sectional study based on the CHARLS database

BACKGROUND

Currently, only a few studies have examined the link between dental health, cognitive impairment, and physical activity. The current study examined the relationship between denture use and physical activity in elderly patients with different cognitive abilities.

METHODS

The study data was sourced from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, which included information on denture use and amount of daily physical activity undertaken by older persons. Physical activity was categorized into three levels using the International Physical Activity General Questionnaire and the International Physical Activity Scale (IPAQ) rubric. The relationship between denture use and physical activity in middle-aged and older persons with varying degrees of cognitive functioning was studied using logistic regression models.

RESULTS

A total of 5,892 older people with varying cognitive abilities were included. Denture use was linked to physical activity in the cognitively healthy 60 + age group (p = 0.004). Denture use was positively related with moderate physical activity in the population (odds ratio, OR: 1.336, 95% confidence interval: 1.173-1.520, p < 0.001), according to a multivariate logistic regression analysis, a finding that was supported by the calibration curve. Furthermore, the moderate physical activity group was more likely to wear dentures than the mild physical activity group among age-adjusted cognitively unimpaired middle-aged and older persons (OR: 1.213, 95% CI: 1.053-1.397, p < 0.01). In a fully adjusted logistic regression model, moderate physical activity population had increased ORs of 1.163 (95% CI: 1.008-1.341, p < 0.05) of dentures and vigorous physical activity population had not increased ORs of 1.016 (95% CI: 0.853-1.210, p > 0.05), compared with mild physical activity population.

CONCLUSION

This findings revealed that wearing dentures affects physical activity differently in older persons with different cognitive conditions. In cognitively unimpaired older adults, wearing dentures was associated with an active and appropriate physical activity status.

Association of Eating Behavior, Nutritional Risk, and Frailty with Sarcopenia in Taiwanese Rural Community-Dwelling Elders: A Cross-Sectional Study

This cross-sectional study assessed the association of eating behavior, nutritional risk, and frailty with sarcopenia in 208 community-dwelling individuals aged ≥65 years who were recruited from random rural community care centers in Chiayi County, Taiwan. The participants’ eating behavior was categorized into six categories. The gait speed (GS), grip strength, and appendicular skeletal muscle mass (ASM) were assessed based on these three parameters, which revealed that 50.9% of the participants had sarcopenia. In an adjusted model, water intake (odds ratio (OR) = 0.99, p = 0.044), dairy product intake (OR = 0.42, p = 0.049), body mass index (BMI) (OR = 0.77, p = 0.019), and marital status with widowed (OR = 0.31, p = 0.005) were significantly associated with sarcopenia. After eight steps of eliminating the least significant independent variable, age (p = 0.002), sex (p = 0.000), marital status with widowed (p = 0.001), water intake (p < 0.018), dairy product intake (p < 0.019), and BMI (p = 0.005) were found to be indispensable predictors of sarcopenia. The logistic regression model with these six indispensable variables had a predictive value of 75.8%. Longitudinal analyses are warranted to examine whether eating behavior is a risk factor for sarcopenia onset.