Linsheng Lv

A new equation to estimate glomerular filtration rate in Chinese elderly population.

Background We sought to develop a new equation to estimate glomerular filtration rate (GFR) in Chinese elderly population. Methods A total of 668 Chinese elderly participants, including the development cohort (n = 433), the validation cohort (n = 235) were enrolled. The new equation using the generalized additive model, and age, gender, serum creatinine as predictor variables was developed and the performances was compared with the CKD-EPI equation. Results In the validation data set, both bias and precision were improved with the new equation, as compared with the CKD-EPI equation (median difference, −1.5 ml/min/1.73 m2 vs. 7.4 ml/min/1.73 m2 for the new equation and the CKD-EPI equation, [P<0.001]; interquartile range [IQR] for the difference, 16.2 ml/min/1.73 m2 vs. 19.0 ml/min/1.73 m2 [P<0.001]), as were accuracies (15% accuracy, 40.4% vs. 30.6% [P = 0.02]; 30% accuracy, 71.1% vs. 47.2%, [P<0.001]; 50% accuracy, 90.2% vs. 75.7%, [P<0.001]), allowing improvement in GFR categorization (GFR category misclassification rate, 37.4% vs. 53.2% [P = <0.001]). Conclusions A new equation was developed in Chinese elderly population. In the validation data set, the new equation performed better than the original CKD-EPI equation. The new equation needs further external validations. Calibration of the GFR referent standard to a more accurate one should be an useful way to improve the performance of GFR estimating equations.

A New Modified CKD-EPI Equation for Chinese Patients with Type 2 Diabetes

Objective To improve the performance of glomerular filtration rate (GFR) estimating equation in Chinese type 2 diabetic patients by modification of the CKD-EPI equation. Design and patients A total of 1196 subjects were enrolled. Measured GFR was calibrated to the dual plasma sample 99mTc-DTPA-GFR. GFRs estimated by the re-expressed 4-variable MDRD equation, the CKD-EPI equation and the Asian modified CKD-EPI equation were compared in 351 diabetic/non-diabetic pairs. And a new modified CKD-EPI equation was reconstructed in a total of 589 type 2 diabetic patients. Results In terms of both precision and accuracy, GFR estimating equations all achieved better results in the non-diabetic cohort comparing with those in the type 2 diabetic cohort (30% accuracy, P≤0.01 for all comparisons). In the validation data set, the new modified equation showed less bias (median difference, 2.3 ml/min/1.73 m2 for the new modified equation vs. ranged from −3.8 to −7.9 ml/min/1.73 m2 for the other 3 equations [P<0.001 for all comparisons]), as was precision (IQR of the difference, 24.5 ml/min/1.73 m2 vs. ranged from 27.3 to 30.7 ml/min/1.73 m2), leading to a greater accuracy (30% accuracy, 71.4% vs. 55.2% for the re-expressed 4 variable MDRD equation and 61.0% for the Asian modified CKD-EPI equation [P = 0.001 and P = 0.02]). Conclusion A new modified CKD-EPI equation for type 2 diabetic patients was developed and validated. The new modified equation improves the performance of GFR estimation.

A Comparison of the Performances of an Artificial Neural Network and a Regression Model for GFR Estimation

BACKGROUND Accurate estimation of glomerular filtration rate (GFR) is important in clinical practice. Current models derived from regression are limited by the imprecision of GFR estimates. We hypothesized that an artificial neural network (ANN) might improve the precision of GFR estimates. STUDY DESIGN A study of diagnostic test accuracy. SETTING & PARTICIPANTS 1,230 patients with chronic kidney disease were enrolled, including the development cohort (n=581), internal validation cohort (n=278), and external validation cohort (n=371). INDEX TESTS Estimated GFR (eGFR) using a new ANN model and a new regression model using age, sex, and standardized serum creatinine level derived in the development and internal validation cohort, and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 2009 creatinine equation. REFERENCE TEST Measured GFR (mGFR). OTHER MEASUREMENTS GFR was measured using a diethylenetriaminepentaacetic acid renal dynamic imaging method. Serum creatinine was measured with an enzymatic method traceable to isotope-dilution mass spectrometry. RESULTS In the external validation cohort, mean mGFR was 49±27 (SD) mL/min/1.73 m2 and biases (median difference between mGFR and eGFR) for the CKD-EPI, new regression, and new ANN models were 0.4, 1.5, and -0.5 mL/min/1.73 m2, respectively (P<0.001 and P=0.02 compared to CKD-EPI and P<0.001 comparing the new regression and ANN models). Precisions (IQRs for the difference) were 22.6, 14.9, and 15.6 mL/min/1.73 m2, respectively (P<0.001 for both compared to CKD-EPI and P<0.001 comparing the new ANN and new regression models). Accuracies (proportions of eGFRs not deviating >30% from mGFR) were 50.9%, 77.4%, and 78.7%, respectively (P<0.001 for both compared to CKD-EPI and P=0.5 comparing the new ANN and new regression models). LIMITATIONS Different methods for measuring GFR were a source of systematic bias in comparisons of new models to CKD-EPI, and both the derivation and validation cohorts consisted of a group of patients who were referred to the same institution. CONCLUSIONS An ANN model using 3 variables did not perform better than a new regression model. Whether ANN can improve GFR estimation using more variables requires further investigation.

Initiation time of renal replacement therapy on patients with acute kidney injury: A systematic review and meta-analysis of 8179 participants

The early initiation of renal replacement therapy has been recommended for patients with acute renal failure by some studies, but its effects on mortality and renal recovery are unknown. We conducted an updated meta‐analysis to provide quantitative evaluations of the association between the early initiation of renal replacement therapy and mortality for patients with acute kidney injury. After applying inclusion/exclusion criteria, 51 studies, including 10 randomized controlled trials, with a total of 8179 patients were analyzed. Analysis of the included trials showed that patients receiving early renal replacement therapy had a 25% reduction in all‐cause mortality compared to those receiving late renal replacement therapy (risk ratio [RR] 0.75, 95% CI [0.69, 0.82]). We also noted a 30% increase in renal recovery (RR 1.30, 95% CI [1.07, 1.56]), a reduction in hospitalization of 5.84 days (mean difference [MD], 95% CI [–10.27, –1.41]) and a reduction in the duration of mechanical ventilation of 2.33 days (MD, 95% CI [–3.40, –1.26]) in patients assigned to early renal replacement therapy. The early initiation of renal replacement therapy was associated with a decreased risk of all‐cause mortality compared with the late initiation of RRT in patients with acute kidney injury. These findings should be interpreted with caution given the heterogeneity between studies. Further studies are needed to identify the causes of mortality and to assess whether mortality differs by dialysis dose.

Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

Background Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. Methods A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. Results In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network. Conclusions A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations.

Correlation between Serum Lipid Levels and Measured Glomerular Filtration Rate in Chinese Patients with Chronic Kidney Disease.

Introduction Dyslipidemia is often detected in patients with chronic kidney disease (CKD). Previous studies of the relationship between lipid profiles and kidney function have yielded variable results. We aimed to investigate the correlation between serum lipid levels and kidney function evaluated by measured glomerular filtration rate (mGFR) in Chinese patients with CKD. Methods A cross-sectional study was conducted on 2036 Chinese CKD patients who had mGFR. Linear regression analysis was performed to evaluate the correlation between different serum lipid levels and mGFR, while logistic regression analysis was used to investigate the association between CKD stages and the risk of different types of dyslipidemia. Results The mean age was 55 years and the mean mGFR was 63 mL/min/1.73m2. After adjusting for some confounders (age, gender, body mass index, a history of diabetes, fasting glucose, a history of hypertension, systolic blood pressure, diastolic blood pressure, smoking status, hemoglobin, serum potassium, serum albumin, and serum uric acid), serum triglyceride level showed a negative correlation with mGFR (β = -0.006, P = 0.006) in linear regression analysis, and CKD stages were positively related to the risk of hypertriglyceridemia (odds ratios were 1.329, 1.868, 2.514 and P were 0.046, < 0.001, < 0.001 for CKD stage 2, 3, 4/5, respectively) in logistic regression anlysis. Conclusions Serum triglyceride level is independently association with mGFR. Patients with reduced kidney function are more likely to have higher serum triglyceride levels. Further longitudinal, multicenter and well-conducted studies are needed to provide more evidence.

Estimation of glomerular filtration rate by a radial basis function neural network in patients with type-2 diabetes mellitus

BackgroundAccurate and precise estimates of glomerular filtration rate (GFR) are essential for clinical assessments, and many methods of estimation are available. We developed a radial basis function (RBF) network and assessed the performance of this method in the estimation of the GFRs of 207 patients with type-2 diabetes and CKD.MethodsStandard GFR (sGFR) was determined by 99mTc-DTPA renal dynamic imaging and GFR was also estimated by the 6-variable MDRD equation and the 4-variable MDRD equation.ResultsBland-Altman analysis indicated that estimates from the RBF network were more precise than those from the other two methods for some groups of patients. However, the median difference of RBF network estimates from sGFR was greater than those from the other two estimates, indicating greater bias. For patients with stage I/II CKD, the median absolute difference of the RBF network estimate from sGFR was significantly lower, and the P50 of the RBF network estimate (n = 56, 87.5%) was significantly higher than that of the MDRD-4 estimate (n = 49, 76.6%) (p < 0.0167), indicating that the RBF network estimate provided greater accuracy for these patients.ConclusionsIn patients with type-2 diabetes mellitus, estimation of GFR by our RBF network provided better precision and accuracy for some groups of patients than the estimation by the traditional MDRD equations. However, the RBF network estimates of GFR tended to have greater bias and higher than those indicated by sGFR determined by 99mTc-DTPA renal dynamic imaging.