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Yi J, Hahn S, Oh K, Lee YH. Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion. Sci Rep 2024; 14:2769. [PMID: 38307965 PMCID: PMC10837421 DOI: 10.1038/s41598-024-52614-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/21/2024] [Indexed: 02/04/2024] Open
Abstract
This study aimed to develop and evaluate a sarcopenia prediction model by fusing numerical features from shear-wave elastography (SWE) and gray-scale ultrasonography (GSU) examinations, using the rectus femoris muscle (RF) and categorical/numerical features related to clinical information. Both cohorts (development, 70 healthy subjects; evaluation, 81 patients) underwent ultrasonography (SWE and GSU) and computed tomography. Sarcopenia was determined using skeletal muscle index calculated from the computed tomography. Clinical and ultrasonography measurements were used to predict sarcopenia based on a linear regression model with the least absolute shrinkage and selection operator (LASSO) regularization. Furthermore, clinical and ultrasonography features were combined at the feature and score levels to improve sarcopenia prediction performance. The accuracies of LASSO were 70.57 ± 5.00-81.54 ± 4.83 (clinical) and 69.00 ± 4.52-69.73 ± 5.47 (ultrasonography). Feature-level fusion of clinical and ultrasonography (accuracy, 70.29 ± 6.63 and 83.55 ± 4.32) showed similar performance with clinical features. Score-level fusion by AdaBoost showed the best performance (accuracy, 73.43 ± 6.57-83.17 ± 5.51) in the development and evaluation cohorts, respectively. This study might suggest the potential of machine learning fusion techniques to enhance the accuracy of sarcopenia prediction models and improve clinical decision-making in patients with sarcopenia.
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Affiliation(s)
- Jisook Yi
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Seok Hahn
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Kangrok Oh
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea.
| | - Young Han Lee
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea.
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Ozgur S, Altinok YA, Bozkurt D, Saraç ZF, Akçiçek SF. Performance Evaluation of Machine Learning Algorithms for Sarcopenia Diagnosis in Older Adults. Healthcare (Basel) 2023; 11:2699. [PMID: 37830737 PMCID: PMC10572141 DOI: 10.3390/healthcare11192699] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Sarcopenia is a progressive and generalized skeletal muscle disorder. Early diagnosis is necessary to reduce the adverse effects and consequences of sarcopenia, which can help prevent and manage it in a timely manner. The aim of this study was to identify the important risk factors for sarcopenia diagnosis and compare the performance of machine learning (ML) algorithms in the early detection of potential sarcopenia. METHODS A cross-sectional design was employed for this study, involving 160 participants aged 65 years and over who resided in a community. ML algorithms were applied by selecting 11 features-sex, age, BMI, presence of hypertension, presence of diabetes mellitus, SARC-F score, MNA score, calf circumference (CC), gait speed, handgrip strength (HS), and mid-upper arm circumference (MUAC)-from a pool of 107 clinical variables. The results of the three best-performing algorithms were presented. RESULTS The highest accuracy values were achieved by the ALL (male + female) model using LightGBM (0.931), random forest (RF; 0.927), and XGBoost (0.922) algorithms. In the female model, the support vector machine (SVM; 0.939), RF (0.923), and k-nearest neighbors (KNN; 0.917) algorithms performed the best. Regarding variable importance in the ALL model, the last HS, sex, BMI, and MUAC variables had the highest values. In the female model, these variables were HS, age, MUAC, and BMI, respectively. CONCLUSIONS Machine learning algorithms have the ability to extract valuable insights from data structures, enabling accurate predictions for the early detection of sarcopenia. These predictions can assist clinicians in the context of predictive, preventive, and personalized medicine (PPPM).
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Affiliation(s)
- Su Ozgur
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ege University, 35040 Izmir, Turkey
- Translational Pulmonary Research Center—EgeSAM, Ege University, 35040 Izmir, Turkey
| | - Yasemin Atik Altinok
- Department of Pediatric Endocrinology, Faculty of Medicine, Ege University, 35040 Izmir, Turkey;
| | - Devrim Bozkurt
- Department of Internal Medicine, Faculty of Medicine, Ege University, 35040 Izmir, Turkey;
| | - Zeliha Fulden Saraç
- Division of Geriatrics, Department of Internal Medicine, Faculty of Medicine, Ege University, 35040 Izmir, Turkey; (Z.F.S.); (S.F.A.)
| | - Selahattin Fehmi Akçiçek
- Division of Geriatrics, Department of Internal Medicine, Faculty of Medicine, Ege University, 35040 Izmir, Turkey; (Z.F.S.); (S.F.A.)
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Wu J, Lin S, Guan J, Wu X, Ding M, Shen S. Prediction of the sarcopenia in peritoneal dialysis using simple clinical information: A machine learning-based model. Semin Dial 2023; 36:390-398. [PMID: 36890621 DOI: 10.1111/sdi.13131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/31/2022] [Accepted: 11/04/2022] [Indexed: 03/10/2023]
Abstract
INTRODUCTION Sarcopenia is associated with significant cardiovascular risk, and death in patients undergoing peritoneal dialysis (PD). Three tools are used for diagnosing sarcopenia. The evaluation of muscle mass requires dual energy X-ray absorptiometry (DXA) or computed tomography (CT), which is labor-intensive and relatively expensive. This study aimed to use simple clinical information to develop a machine learning (ML)-based prediction model of PD sarcopenia. METHODS According to the newly revised Asian Working Group for Sarcopenia (AWGS2019), patients were subjected to complete sarcopenia screening, including appendicular skeletal muscle mass, grip strength, and five-time chair stand time test. Simple clinical information such as general information, dialysis-related indices, irisin and other laboratory indices, and bioelectrical impedance analysis (BIA) data were collected. All data were randomly split into training (70%) and testing (30%) sets. Difference, correlation, univariate, and multivariate analyses were used to identify core features significantly associated with PD sarcopenia. RESULT 12 core features (C), namely, grip strength, body mass index (BMI), total body water value, irisin, extracellular water/total body water, fat-free mass index, phase angle, albumin/globulin, blood phosphorus, total cholesterol, triglyceride, and prealbumin were excavated for model construction. Two ML models, the neural network (NN), and support vector machine (SVM) were selected with tenfold cross-validation to determine the optimal parameter. The C-SVM model showed a higher area under the curve (AUC) of 0.82 (95% confidence interval [CI]: 0.67-1.00), with a highest specificity of 0.96, sensitivity of 0.91, positive predictive value (PPV) of 0.96, and negative predictive value (NPV) of 0.91. CONCLUSION The ML model effectively predicted PD sarcopenia and has clinical potential to be used as a convenient sarcopenia screening tool.
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Affiliation(s)
- Jiaying Wu
- Department of Nephrology, Shaoxing University School of Medicine, Shaoxing, Zhejiang, China
| | - Shuangxiang Lin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jichao Guan
- Department of Nephrology, The First Affiliated Hospital of Shaoxing University, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Xiujuan Wu
- Department of Nephrology, The First Affiliated Hospital of Shaoxing University, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Miaojia Ding
- Department of Nephrology, Shaoxing University School of Medicine, Shaoxing, Zhejiang, China
| | - Shuijuan Shen
- Department of Nephrology, The First Affiliated Hospital of Shaoxing University, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
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Huang YC, Cheng YC, Jhou MJ, Chen M, Lu CJ. Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme-A Post Hoc Analysis. J Pers Med 2022; 12:756. [PMID: 35629177 PMCID: PMC9146635 DOI: 10.3390/jpm12050756] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/29/2022] [Accepted: 05/03/2022] [Indexed: 02/06/2023] Open
Abstract
Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques-naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)-were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.
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Affiliation(s)
- Yung-Chuan Huang
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan;
| | - Yu-Chen Cheng
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan;
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
| | - Mingchih Chen
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan; (Y.-C.H.); (M.-J.J.); (M.C.)
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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Feng L, Gao Q, Hu K, Wu M, Wang Z, Chen F, Mei F, Zhao L, Ma B. Prevalence and Risk Factors of Sarcopenia in Patients With Diabetes: A Meta-analysis. J Clin Endocrinol Metab 2022; 107:1470-1483. [PMID: 34904651 DOI: 10.1210/clinem/dgab884] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT The prevalence of sarcopenia in patients with diabetes is 3 times higher than that in patients without diabetes and is associated with a poor prognosis. OBJECTIVE To investigate the global pooled prevalence and risk factors of sarcopenia in patients with diabetes. DATA SOURCES Relevant studies published until November 30, 2020, were identified from the PubMed, Embase, Web of Science, WanFang, CNKI, VIP, and CBM databases. STUDY SELECTION Participants with age ≥ 18 years with clinically diagnosed diabetes. Sex and diabetes type were not restricted. DATA EXTRACTION The data were extracted by 2 reviewers independently using a standard data collection form. DATA SYNTHESIS The pooled prevalence of sarcopenia in patients with diabetes was 18% (95% CI, 16-20); subgroup analysis showed that sarcopenia was more prevalent in males than in females, as well as being more prevalent in Asia than in South America and Oceania. Age (odds ratio [OR], 1.10), glycated hemoglobin (HbA1c) (OR = 1.16), visceral fat area (VFA) (OR = 1.03), diabetic nephropathy (OR = 2.54), duration of diabetes (OR = 1.06), and high-sensitivity C-reactive protein (hs-CRP) (OR = 1.33) were risk factors for sarcopenia in patients with diabetes. CONCLUSIONS Sarcopenia was more prevalent in patients with diabetes. Age, HbA1c, VFA, diabetic nephropathy, duration of diabetes, and hs-CRP were the probable risk factors. In the future, medical staff should not only pay attention to the early screening of sarcopenia in high-risk groups, but also provide information on its prevention.
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Affiliation(s)
- Liyuan Feng
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Qianqian Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Kaiyan Hu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, P.R. China
| | - Mei Wu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Zhe Wang
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Fei Chen
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Fan Mei
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Li Zhao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
| | - Bin Ma
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, P.R. China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, P.R. China
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Ikezawa K, Hirose M, Maruyama T, Yuji K, Yabe Y, Kanamori T, Kaide N, Tsuchiya Y, Hara S, Suzuki H. Effect of early nutritional initiation on post-cerebral infarction discharge destination: A propensity-matched analysis using machine learning. Nutr Diet 2021; 79:247-254. [PMID: 34927343 DOI: 10.1111/1747-0080.12718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/11/2021] [Accepted: 11/15/2021] [Indexed: 11/26/2022]
Abstract
AIM Malnutrition is associated with poor outcomes in cerebral infarction patients, with research indicating that early nutritional initiation may improve the short-term prognosis of patients. However, evidence supported by big data is lacking. Here, to determine the effect of nutritional initiation during the first 3 days after hospital admission on home discharge rates, propensity score matching was conducted in patients with acute cerebral infarction. METHODS This retrospective observational study, using the Diagnosis Procedure Combination anonymised database in Japan, included 41 477 ischaemic cerebral infarction patients hospitalised between 2016 and 2019. The patients were divided into two groups: those who received oral or enteral nutrition during the first 3 days of hospital admission (early nutrition group, n = 37 318) and those who did not (control group, n = 4159). One-to-one pair-matching was performed using propensity scores calculated via extreme gradient boosting to limit the confounding variables of the two groups. RESULTS After propensity score matching, 3541 pairs of patients were selected. The dependence of home discharge rates on early nutrition was significant (p < 0.05), and the effectiveness of early nutrition for home discharge showed an odds ratio of 1.79 (95% confidence interval of 1.59-2.03 in Fisher's exact test). CONCLUSIONS Our findings revealed that early nutritional initiation during the first 3 days of admission resulted in higher home discharge rates.
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Affiliation(s)
- Kazuto Ikezawa
- Division of Gastroenterology, Tsukuba Memorial Hospital, Tsukuba, Japan
| | - Mitsuaki Hirose
- Department of Gastroenterology, Institute of Clinical Medicine, University of Tsukuba, Tsukuba, Japan
| | | | - Koichiro Yuji
- The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoshito Yabe
- Department of Nutrition, Tsukuba Memorial Hospital, Tsukuba, Japan
| | | | | | | | | | - Hideo Suzuki
- Department of Gastroenterology, Institute of Clinical Medicine, University of Tsukuba, Tsukuba, Japan
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A Machine Learning Approach to Predicting Diabetes Complications. Healthcare (Basel) 2021; 9:healthcare9121712. [PMID: 34946438 PMCID: PMC8702133 DOI: 10.3390/healthcare9121712] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/23/2021] [Accepted: 12/04/2021] [Indexed: 11/17/2022] Open
Abstract
Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each complication was as follows: 428-metabolic syndrome, 836-dyslipidemia, 223-neuropathy, 233-nephropathy, 240-diabetic foot, 586-hypertension, 498-obesity, 228-retinopathy. Repeated stratified k-fold cross-validation (with k = 10 and a total of 10 repetitions) was employed for a better estimation of the performance. Accuracy and F1-score were used to evaluate the models' performance reaching a maximum of 97.8% and 97.7% for accuracy and F1-scores, respectively. Moreover, by comparing the performance achieved using different attributes' sets, it was found that by using a selected number of features, we can still build adequate classifiers.
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Yoon HG, Oh D, Noh JM, Cho WK, Sun JM, Kim HK, Zo JI, Shim YM, Kim K. Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer. J Cachexia Sarcopenia Muscle 2021; 12:1144-1152. [PMID: 34145771 PMCID: PMC8517349 DOI: 10.1002/jcsm.12747] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/12/2021] [Accepted: 06/08/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Excessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results. METHODS We randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut-off value to define the excessive skeletal muscle loss. RESULTS The five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively. CONCLUSIONS Machine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.
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Affiliation(s)
- Han Gyul Yoon
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dongryul Oh
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jae Myoung Noh
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Kyung Cho
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong-Mu Sun
- Department of Internal Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hong Kwan Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jae Ill Zo
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young Mog Shim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
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Clinical Predictors of Prolonged Hospital Stay in Patients with Myasthenia Gravis: A Study Using Machine Learning Algorithms. J Clin Med 2021; 10:jcm10194393. [PMID: 34640412 PMCID: PMC8509494 DOI: 10.3390/jcm10194393] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 01/03/2023] Open
Abstract
Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients.
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Circulating Mediators of Apoptosis and Inflammation in Aging; Physical Exercise Intervention. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18063165. [PMID: 33808526 PMCID: PMC8003155 DOI: 10.3390/ijerph18063165] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/13/2021] [Accepted: 03/17/2021] [Indexed: 12/27/2022]
Abstract
Sarcopenia is an age-related loss of skeletal muscle mass caused by many cellular mechanisms and also by lifestyle factors such as low daily physical activity. In addition, it has been shown that sarcopenia may be associated with inflammation and cognitive impairment in old age. Regular exercise is key in reducing inflammation and preventing sarcopenia and diseases related to cognitive impairment. The study was designed to assess the impact of exercise training on circulating apoptotic and inflammatory markers of sarcopenia in older adults. Eighty older adults aged 70.5 ± 5.8 years were randomized to the physically active group who participated in a 10-month Tai-Chi training session (TC, n = 40) and the control group who participated in health education sessions (HE, n = 40). Tai-Chi training caused a significant decrease in fat mass (FM) by 3.02 ± 3.99%, but an increase in appendicular skeletal muscle mass index (ASMI) by 1.76 ± 3.17% and gait speed by 9.07 ± 11.45%. Tai-Chi training elevated the plasma levels of C-reactive protein (CRP), tumor necrosis factor (TNFα), and tumor necrosis receptor factor II (TNFRII), and decreased caspases 8 and 9. Despite the increase in TNFα, apoptosis was not initiated, i.e., the cell-free DNA level did not change in the TC group. The study demonstrated that Tai-Chi training significantly reduced the symptoms of sarcopenia through the changes in body composition and physical performance, and improvements in cytokine-related mechanisms of apoptosis.
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Maeda-Gutiérrez V, Galván-Tejada CE, Cruz M, Valladares-Salgado A, Galván-Tejada JI, Gamboa-Rosales H, García-Hernández A, Luna-García H, Gonzalez-Curiel I, Martínez-Acuña M. Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach. Healthcare (Basel) 2021; 9:138. [PMID: 33535510 PMCID: PMC7912731 DOI: 10.3390/healthcare9020138] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/23/2021] [Accepted: 01/25/2021] [Indexed: 12/05/2022] Open
Abstract
The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.
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Affiliation(s)
- Valeria Maeda-Gutiérrez
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI. Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Mexico City 06720, Mexico; (M.C.); (A.V.-S.)
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI. Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Mexico City 06720, Mexico; (M.C.); (A.V.-S.)
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Alejandra García-Hernández
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Irma Gonzalez-Curiel
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (I.G.-C.); (M.M.-A.)
| | - Mónica Martínez-Acuña
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (I.G.-C.); (M.M.-A.)
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Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17061917. [PMID: 32183494 PMCID: PMC7143671 DOI: 10.3390/ijerph17061917] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/07/2020] [Accepted: 03/13/2020] [Indexed: 12/17/2022]
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.
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