Frontiers in medicine
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Frontiers in medicine · Jan 2020
Skeletal Muscle Mass Index Is Positively Associated With Bone Mineral Density in Hemodialysis Patients.
Background: Patients with chronic kidney disease (CKD) are at risk for bone loss and sarcopenia because of associated mineral and bone disorders (MBD), malnutrition, and chronic inflammation. Both osteoporosis and sarcopenia are associated with a poor prognosis; however, few studies have evaluated the relationship between muscle mass and bone mineral density (BMD) in hemodialysis (HD) patients. The present study examined the association between skeletal muscle mass index (SMI) and BMD in the lumbar spine and femoral neck in HD patients. ⋯ In multivariate analysis, SMI (standardized coefficient: 0.578) was the only independent factor that affected the lumbar spine BMD; the independent factors that affected the femoral neck BMD were SMI (standardized coefficient: 0.468), ucOC (standardized coefficient: -0.366) and sex (standardized coefficient: 0.231). Conclusion: SMI was independently associated with the BMD in the lumbar spine and femoral neck in HD patients. The preservation of skeletal muscle mass could be important to prevent BMD decrease in HD patients, in addition to the management of CKD-MBD.
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Frontiers in medicine · Jan 2020
Effect of a Multimodal Movement Intervention in Patients With Neurogenic Claudication Based on Lumbar Spinal Stenosis and/or Degenerative Spondylolisthesis-A Pilot Study.
Chronic low-back pain is a major individual, social, and economic burden. The impairment ranges from deterioration of gait, limited mobility, to psychosocial distress. Due to this complexity, the demand for multimodal treatments is huge. ⋯ For the subsequent study, further kinematic and cognitive parameters should be analyzed, and the number of participants has to be increased. Clinical Trial Registration: German Clinical Trial Register (ID: DRKS00021026/URL: https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00021026).
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Frontiers in medicine · Jan 2020
A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). ⋯ The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837-0.846) and 0.803 (95% CI: 0.798-0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653-0.667) and SIC scores (0.752; 95% CI: 0.747-0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable. Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.
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Frontiers in medicine · Jan 2020
A Systematic Review and Meta-Analysis of Machine Perfusion vs. Static Cold Storage of Liver Allografts on Liver Transplantation Outcomes: The Future Direction of Graft Preservation.
Background: Machine perfusion (MP) and static cold storage (CS) are two prevalent methods for liver allograft preservation. However, the preferred method remains controversial. Aim: To conduct a meta-analysis on the impact of MP preservation on liver transplant outcome. ⋯ Conclusions: Machine perfusion is superior to CS on improving short-term outcomes for human liver transplantation, with a less clear effect in the longer term. Hypothermic machine perfusion but not NMP conducted significantly protective effects on EAD and biliary complications. Further RCTs are warranted to confirm MP's superiority and applications.