AIMC Topic: Creatinine

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Deep learning body-composition analysis of clinically acquired CT-scans estimates creatinine excretion with high accuracy in patients and healthy individuals.

Scientific reports
Assessment of daily creatinine production and excretion plays a crucial role in the estimation of renal function. Creatinine excretion is estimated by creatinine excretion equations and implicitly in eGFR equations like MDRD and CKD-EPI. These equati...

Surgical Selection of T1 Stage Renal Tumor Resection Based on Imaging MAP Score under Smart Medical Care.

Computational intelligence and neuroscience
Smart medical uses the medical information platform and the current technological means to enable the process of sharing information between medical staff and medical equipment. The combination of current technology and the medical field has become t...

Robotic versus hand-assisted laparoscopic living donor nephrectomy: comparison of two minimally invasive techniques in kidney transplantation.

Journal of robotic surgery
Robot-assisted donor nephrectomy (RDN) is increasingly used due to its advantages such as its precision and reduced learning curve when compared to laparoscopic techniques. Concerns remain among surgeons regarding possible longer warm ischemia time. ...

Contribution of phosphorus and PTH to the development of cardiac hypertrophy and fibrosis in an experimental model of chronic renal failure.

Nefrologia
BACKGROUND AND OBJECTIVE: Adequate serum phosphorus levels in patients with chronic kidney disease is essential for their clinical management. However, the control of hyperphosphatemia is difficult because is normally associated with increases in ser...

Estimation of Baseline Serum Creatinine with Machine Learning.

American journal of nephrology
INTRODUCTION: Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine.

Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis.

International journal of medical informatics
INTRODUCTION: We aimed to assess whether machine learning models are superior at predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional prediction model.

A novel approach to dry weight adjustments for dialysis patients using machine learning.

PloS one
BACKGROUND AND AIMS: Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis pa...

Prediction of vancomycin dose on high-dimensional data using machine learning techniques.

Expert review of clinical pharmacology
OBJECTIVES: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of ind...

Comparison of plasma neutrophil gelatinase-associated lipocalin (NGAL) levels after robot-assisted laparoscopic and retropubic radical prostatectomy: an observational study.

Brazilian journal of anesthesiology (Elsevier)
BACKGROUND AND OBJECTIVES: Patients undergoing radical prostatectomy are at increased risk of Acute Kidney Injury (AKI) because of intraoperative bleeding, obstructive uropathy, and older age. Neutrophil Gelatinase-Associated Lipocalin (NGAL) may bec...