Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Abstract

The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

Sarcopenia and COVID-19

“…A recent study investigating the prevalence of sarcopenia in the elderly by using machine learning found that (in order of importance), age, arterial hypertension, mini-nutritional assessment, number of chronic diseases and blood sodium level determined the sarcopenia. 11 Physiatrists need to consider advising their patients to continue RAS blockers (especially ACEIs) during the COVID-19 pandemic, 7 and they should also be aware of the RAS inhibitors’ roles in sarcopenia. 12 , 13 The ACEIs can be protective against loss of muscle mass and physical performance.…”

Sarcopenia, Exercise and Quality of Life

“…This special edition of the Journal of Environmental Research and Public Health ( IJERPH ) has brought together eleven research manuscripts [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ] and a brief report [ 12 ]. This special issue, entitled “ Sarcopenia, Exercise and Quality of Life ” gathered 12 manuscripts [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]; four (33.3%) manuscripts are related to the impact of physical activity concerning sarcopenia [ 4 , 6 , 7 , 10 ]. Two manuscripts were used for the Wii Fit ® games as an intervention tool for physical activity in institutionalized elderly patients [ 4 , 6 ].…”

“…In this special issue, three manuscripts (25%) [ 1 , 2 , 5 ] evaluated modern diagnostic methods of sarcopenia, with the aim of more accurately identifying this pathology. Cebria et al [ 2 ] and Guillamon-Escudero et al [ 5 ] used the European Working Group on Sarcopenia in Older People criteria established in 2019 (EWGSOP2).…”

“…Cebria et al [ 2 ] state that the Barthel Index (BI) and Abbreviated Charlson’s Comorbidity Index (ACCI) are the appropriate markers for early diagnosis of sarcopenia. Castillo-Olea et al [ 1 ] use automated practices of clinical sarcopenia diagnosis on 166 elderly patients during six months, evaluating a total of 99 variables. The results of this study show that the new variables considered key by the authors (age, systolic blood pressure, number of chronic diseases and sodium) should be complemented with those classically used for sarcopenia diagnosis when assessing patients with moderate and severe sarcopenia.…”

Is sarcopenia primarily an age‐related or renin–angiotensin system‐related disorder?

“…Sarcopenia is an age-related progressive loss of skeletal muscle mass and function, and associated with many chronic diseases. 2 Recently, Castillo-Olea et al 3 revealed that age, systolic arterial hypertension, mini-nutritional assessment, number of chronic diseases and sodium are the determinants of sarcopenia and may be complementary variables in the assessment of sarcopenia with standard evaluation variables such as hand grip strength, calf circumference and gait speed. Kara et al 4 designed a study to examine a personalized rate of sonographic muscle thickness and evaluated total skeletal muscle mass.…”