Creatinine generation rate can detect sarcopenia in patients with hemodialysis

Mae, Yukari; Takata, Tomoaki; Yamada, Kentaro; Hamada, Shintaro; Yamamoto, Michinori; Iyama, Takuji; Isomoto, Hajime

doi:10.1007/s10157-021-02142-4

Cited by 10 publications

(10 citation statements)

References 30 publications

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“…Hence, the use of a combination of these two indicators may bring new breakthroughs. The creatinine generation rate calculated from pre-dialysis and post-dialysis blood examinations positively correlates with skeletal muscle mass [ 44 ]. However, this study only used data directly extracted from the medical system and did not conduct related research due to limited time and effort.…”

Section: Discussionmentioning

confidence: 99%

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

et al. 2023

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Background Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, which is strongly associated with their long-term mortality. The diagnosis and treatment of sarcopenia, especially possible sarcopenia for MHD patients are of great importance. This study aims to use machine learning and medical data to develop two simple sarcopenia identification assistant tools for MHD patients and focuses on sex specificity. Methods Data were retrospectively collected from patients undergoing MHD and included patients’ basic information, body measurement results and laboratory findings. The 2019 consensus update by Asian working group for sarcopenia was used to assess whether a MHD patient had sarcopenia. Finally, 140 male (58 with possible sarcopenia or sarcopenia) and 102 female (65 with possible sarcopenia or sarcopenia) patients’ data were collected. Participants were divided into sarcopenia and control groups for each sex to develop binary classifiers. After statistical analysis and feature selection, stratified shuffle split and Synthetic Minority Oversampling Technique were conducted and voting classifiers were developed. Results After eliminating handgrip strength, 6-m walk, and skeletal muscle index, the best three features for sarcopenia identification of male patients are age, fasting blood glucose, and parathyroid hormone. Meanwhile, age, arm without vascular access, total bilirubin, and post-dialysis creatinine are the best four features for females. After abandoning models with overfitting or bad performance, voting classifiers achieved good sarcopenia classification performance for both sexes (For males: sensitivity: 77.50% ± 11.21%, specificity: 83.13% ± 9.70%, F1 score: 77.32% ± 5.36%, the area under the receiver operating characteristic curves (AUC): 87.40% ± 4.41%. For females: sensitivity: 76.15% ± 13.95%, specificity: 71.25% ± 15.86%, F1 score: 78.04% ± 8.85%, AUC: 77.69% ± 7.92%). Conclusions Two simple sex-specific sarcopenia identification tools for MHD patients were developed. They performed well on the case finding of sarcopenia, especially possible sarcopenia.

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Section: Discussionmentioning

confidence: 99%

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

et al. 2023

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“…Our results also showed that a high level of Scr was a protective factor for sarcopenia in patients on MHD ( P =0.029), consistent with the findings of Lin et al [ 30 ]. Mae et al [ 31 ] found that the Scr production rate can predict the occurrence of sarcopenia in MHD patients. Scr is a product of skeletal muscle metabolism in the human body, which is mainly excreted by glomerular filtration.…”

Section: Discussionmentioning

confidence: 99%

Development of a Practical Screening Tool to Predict Sarcopenia in Patients on Maintenance Hemodialysis

Chen

Zhang

et al. 2022

Med Sci Monit

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Background Sarcopenia is a common complication in maintenance hemodialysis (MHD) and can increase patient hospitalization and mortality. No simple and reliable tools to identify sarcopenia exist. We aimed to develop a screening tool to predict MHD patients at high risk for sarcopenia. Material/Methods This cross-sectional study included 589 and 216 MHD patients for training and validation sets, respectively. We used diagnostic criteria developed by the Asian Working Group on Sarcopenia to screen for sarcopenia. The risk prediction model was established by univariate and multivariate logistic regression analyses. We used the area under the receiver operating characteristic curve (AUROC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA) to evaluate the model’s discrimination ability, calibration ability, and clinical utility. Results The incidence of sarcopenia was 17.1% in the training set and 18.1% in the validation set. We constructed prediction models applying age, body mass index, calf circumference, and serum creatinine and plotted a nomogram. The training set model had an AUROC of 0.922, sensitivity of 85.1%, specificity of 85.9%, and chi-square value (Hosmer-Lemeshow test) of 5.603 ( P >0.05); the DCA diagram showed that when the threshold probability was 0 to 0.95, the model predicted a net benefit for sarcopenia in MHD patients. The validation set model had an AUROC of 0.913, sensitivity of 94.3%, specificity of 82.9%, and chi-square value (Hosmer-Lemeshow test) of 9.822 ( P >0.05). Conclusions The screening tool has good discrimination ability, calibration ability, and clinical utility. It could help to identify MHD patients at a high risk for sarcopenia.

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“…Because age and gender are independent determinants of CGR, CGR is usually adjusted for age and gender using a previously reported equation (i.e., %CGR) [ 32 ]. A recent study demonstrated that the cut-off value of %CGR for detecting low muscle mass volume (SMI less than 7.0 kg/m 2 in men and less than 5.7 kg/m 2 in women) was 109.83, with a sensitivity of 68% and a specificity of 88% in Japanese HD patients [ 33 ]. The ratio of serum creatinine to cystatin C is also demonstrated to be useful in predicting skeletal muscle mass and strength in patients with non-dialysis CKD [ 34 ].…”

Section: Nutritional Assessment Methods For Dialysis Patientsmentioning

confidence: 99%

Recent Advances in the Nutritional Screening, Assessment, and Treatment of Japanese Patients on Hemodialysis

Ishida

Kato

2023

JCM

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Patients on hemodialysis (HD) have a higher rate of protein-energy wasting (PEW) due to lower dietary intake of energy and protein (particularly on dialysis days) and greater loss of many nutrients in the dialysate effluent than other patients. The most well-known method of nutritional screening is the subjective global assessment. Moreover, the Global Leadership Initiative on MalnutIrition has developed the first internationally standardized method for diagnosing malnutrition; however, its use in patients on HD has not been established. In contrast, the nutritional risk index for Japanese patients on HD has recently been developed as a screening tool for malnutrition in patients on HD, based on the modified PEW criteria. These tools are beneficial for screening nutritional disorders, enabling registered dietitians to assess patients’ dietary intake on dialysis and non-dialysis days and provide advice on dietary intake, especially immediately after dialysis cessation. Oral supplementation with enteral nutrients containing whey protein may also be administered when needed. In patients that experience adverse effects from oral supplementation, intradialytic parenteral nutrition (IDPN) should be combined with moderate dietary intake because IDPN alone cannot provide sufficient nutrition.

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Creatinine generation rate can detect sarcopenia in patients with hemodialysis

Cited by 10 publications

References 30 publications

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

Development of a Practical Screening Tool to Predict Sarcopenia in Patients on Maintenance Hemodialysis

Recent Advances in the Nutritional Screening, Assessment, and Treatment of Japanese Patients on Hemodialysis

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