Estimation of Appendicular Skeletal Muscle Mass for Women Aged 60-70 Years Using a Machine Learning Approach

Development and Validation of Estimation Equations for Appendicular Skeletal Muscle Mass in Chinese Community-Dwelling Older Adults

Purpose

This study aimed to establish equations for estimating muscle mass through anthropometric parameters or together with physical function parameters in the community-dwelling older adults, providing a simple way of muscle mass assessment.

Methods

In this cross-sectional descriptive study, a total of 1537 older adults were recruited from the community and accepted the measurements of height, weight, upper arm and calf circumferences, grip strength, and walking speed. Body composition including appendicular skeletal muscle mass (ASM) was measured using bioelectrical impedance analysis (BIA). Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop equations in the development group. Thereafter, Pearson correlation coefficients, Bland-Altman plots, paired t-test, intraclass correlation coefficient (ICC) and paired-samples t-tests were used to assess the validity of the equations.

Results

All parameters were significantly correlated with ASM (r = 0.195~0.795, P < 0.001) except for the age in the validation group (P = 0.746). The most optimal anthropometric equation was: [adjusted R2 = 0.911, standard error of the estimate (SEE) = 1.311, P < 0.001]. Comparatively speaking, this equation showed high correlation coefficient (r = 0.951, P < 0.001) and ICC (ICC = 0.950, P < 0.001). No significant differences were found between BIA-measured ASM and the estimated ASM. The Bland-Altman plot showed that the mean difference between the estimated ASM and BIA-measured ASM was 0 kg and the limits of agreement of ASM was -2.70~2.60 kg. Furthermore, inclusion of physical function did not significantly improve the adjusted R2 and SEE.

Conclusion

The anthropometric equation offers a practical alternative simple and dependable method for estimating ASM in community-dwelling older adults.

Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review

BACKGROUND

Optimal sizing for phakic intraocular lens (EVO-ICL with KS-AquaPort) implantation plays an important role in preventing postoperative complications. We aimed to formulate optimal lens sizing using ocular biometric parameters measured with a Heidelberg anterior segment optical coherence tomography (AS-OCT) device.

METHODS

We retrospectively analyzed 892 eyes of 471 healthy subjects treated with an intraocular collamer lens (ICL) and assigned them to either the development (80%) or validation (20%) set. We built vault prediction models using the development set via classic linear regression methods as well as partial least squares and least absolute shrinkage and selection operator (LASSO) regression techniques. We evaluated prediction abilities based on the Bayesian information criterion (BIC) to select the best prediction model. The performance was measured using Pearson's correlation coefficient and the mean squared error (MAE) between the achieved and predicted results.

RESULTS

Measurements of aqueous depth (AQD), anterior chamber volume, anterior chamber angle (ACA) distance, spur-to-spur distance, crystalline lens thickness (LT), and white-to-white distance from ANTERION were highly associated with the ICL vault. The LASSO model using the AQD, ACA distance, and LT showed the best BIC results for postoperative ICL vault prediction. In the validation dataset, the LASSO model showed the strongest correlation (r = 0.582, P < 0.001) and the lowest MAE (104.7 μm).

CONCLUSION

This is the first study to develop a postoperative ICL vault prediction and lens-sizing model based on the ANTERION. As the measurements from ANTERION and other AS-OCT devices are not interchangeable, ANTERION may be used for optimal ICL sizing using our formula. Because our model was developed based on the East Asian population, further studies are needed to explore the role of this prediction model in different populations.

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

Background

Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.

Objective

The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.

Methods

For this cross-sectional study, 733 patients with hypertension (aged 30-85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.

Results

Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.

Conclusion

The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.