Renal changes and estimation of glomerular filtration rate using different equations in morbidly obese Egyptian patients

Effect of Sleeve Gastrectomy Versus One Anastomosis Gastric Bypass on Postoperative Renal Function and the Urinary Monocyte Chemoattractant Protein-1 (MCP-1) Level

INTRODUCTION

Bariatric surgery has been the most effective treatment modality for morbid obesity that reduces associated comorbidities and improves quality of life. This study aims at evaluating and comparing the impact of two types of bariatric surgery-laparoscopic sleeve gastrectomy (LSG) and one anastomosis gastric bypass (OAGB)-on renal functions and urinary monocyte chemoattractant protein-1 (MPC-1) levels in morbidly obese patients 3 months after surgery.

METHODS

This is a prospective study of 40 morbidly obese patients who underwent bariatric surgery. Two types of bariatric surgery were done-laparoscopic sleeve gastrectomy (LSG) (26 patients) and laparoscopic one anastomosis gastric bypass (OAGB) (14 patients). The outcomes of the two procedures were compared in terms of renal function parameters and the level of urinary MCP-1.

RESULTS

There were no statistically significant differences in the mean postoperative urinary MCP-1 (73.53 ± 21.25, 75.43 ± 26.17, P > 0.5), microalbuminuria (8.83 ± 6.26, 10.02 ± 8.6, P > 0.05), urinary creatinine (109.21 ± 43.22, 99.19 ± 48.65, P > 0.05), MCP1/Cr ratio (0.78 ± 0.36, 1.01 ± 0.70, P > 0.05), eGFR (100.32 ± 9.54, 104.39 ± 9.54, P > 0.05) in the cases who had either LSG operation or OAGB operation.

CONCLUSION

Bariatric surgery improves all indicators of kidney malfunction and reduces the level of urinary MCP-1. Both laparoscopic sleeve gastrectomy (LSG) and laparoscopic one anastomosis gastric bypass (OAGB) cause similar improvement of the renal function and reduction of urinary MCP-1 level.

Development of a multimodal kidney age prediction based on automatic segmentation CT image in patients with normal renal function

Background

For decades, description of renal function has been of interest to clinicians and researchers. Serum creatinine (Scr) and estimated glomerular filtration rate (eGFR) are familiar but also limited in many circumstances. Meanwhile, the physiological volumes of the kidney cortex and medulla are presumed to change with age and have been proven to change with decreasing kidney function.

Methods

We recruited 182 patients with normal Scr levels between October 2021 and February 2022 in Peking Union Medical College Hospital (PUMCH) with demographic and clinical data. A 3D U-Net architecture is used for both cortex and medullary separation, and volume calculation. In addition, we included patients with the same inclusion criteria but with diabetes (PUMCH-DM test set) and diabetic nephropathy (PUMCH-DN test set) for internal comparison to verify the possible clinical value of "kidney age" (K-AGE).

Results

The PUMCH training set included 146 participants with a mean age of 47.5 ± 7.4 years and mean Scr 63.5 ± 12.3 μmol/L. The PUMCH test set included 36 participants with a mean age of 47.1 ± 7.9 years and mean Scr 66.9 ± 13.0 μmol/L. The multimodal method predicted K-AGE approximately close to the patient's actual physiological age, with 92% prediction within the 95% confidential interval. The mean absolute error increases with disease progression (PUMCH 5.00, PUMCH-DM 6.99, PUMCH-DN 9.32).

Conclusion

We established a machine learning model for predicting the K-AGE, which offered the possibility of evaluating the whole kidney health in normal kidney aging and in disease conditions.

Construct a classification decision tree model to select the optimal equation for estimating glomerular filtration rate and estimate it more accurately

Chronic kidney disease (CKD) has become a worldwide public health problem and accurate assessment of renal function in CKD patients is important for the treatment. Although the glomerular filtration rate (GFR) can accurately evaluate the renal function, the procedure of measurement is complicated. Therefore, endogenous markers are often chosen to estimate GFR indirectly. However, the accuracy of the equations for estimating GFR is not optimistic. To estimate GFR more precisely, we constructed a classification decision tree model to select the most befitting GFR estimation equation for CKD patients. By searching the HIS system of the First Affiliated Hospital of Zhejiang Chinese Medicine University for all CKD patients who visited the hospital from December 1, 2018 to December 1, 2021 and underwent Gate's method of ^99mTc-DTPA renal dynamic imaging to detect GFR, we eventually collected 518 eligible subjects, who were randomly divided into a training set (70%, 362) and a test set (30%, 156). Then, we used the training set data to build a classification decision tree model that would choose the most accurate equation from the four equations of BIS-2, CKD-EPI(CysC), CKD-EPI(Cr-CysC) and Ruijin, and the equation was selected by the model to estimate GFR. Next, we utilized the test set data to verify our tree model, and compared the GFR estimated by the tree model with other 13 equations. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Bland-Altman plot were used to evaluate the accuracy of the estimates by different methods. A classification decision tree model, including BSA, BMI, 24-hour Urine protein quantity, diabetic nephropathy, age and RASi, was eventually retrieved. In the test set, the RMSE and MAE of GFR estimated by the classification decision tree model were 12.2 and 8.5 respectively, which were lower than other GFR estimation equations. According to Bland-Altman plot of patients in the test set, the eGFR was calculated based on this model and had the smallest degree of variation. We applied the classification decision tree model to select an appropriate GFR estimation equation for CKD patients, and the final GFR estimation was based on the model selection results, which provided us with greater accuracy in GFR estimation.