Risk prediction in chronic kidney disease

Predictive Value of Kidney Failure Risk Equation and Neutrophil Gelatinase-Associated Lipocalin for Chronic Kidney Disease Progression in Chinese Population - A Retrospective Study

Objective

To analyze the independent associations of the Kidney Failure Risk Equation (KFRE) and neutrophil gelatinase-associated lipocalin (NGAL) with end-stage renal disease (ESRD) among patients with chronic kidney disease (CKD) stages 3-5 in China and evaluate their predictive values for ESRD.

Patients and Methods

A total of 716 patients with CKD stages 3-5 at the time of the initial renal medicine referral were retrospectively enrolled, and the study outcome was the observed incidence of ESRD at 2 years after the initial referral. Baseline characteristics were collected, and relevant laboratory indexes, including neutrophil gelatinase-associated lipocalin (NGAL), were detected. The binary logistic regression model was used to analyze the independent associations, and the receiver operating characteristic (ROC) curve was used to assess the predictive values.

Results

The 2-year incidence of ESRD was 20.5% (147/716). The 4-variable KFRE, 8-variable KFRE and NGAL were independently associated with ESRD after adjusting for potential confounding factors. The AUCs of the 4-variable KFRE, 8-variable KFRE and NGAL for predicting ESRD among patients with CKD stages 3-5 were 0.711 [standard error (SE): 0.026, 95% confidence interval (CI): 0.662-0.761], 0.725 (SE: 0.025, 95% CI: 0.677-0.774) and 0.736 (SE: 0.024, 95% CI: 0.686-0.785), respectively. The AUC of the 4-variable KFRE plus NGAL was significantly higher than those of the 4-variable KFRE and NGAL alone (0.900 vs 0.711, Z = 6.297, P < 0.001; 0.900 vs 0.736, Z = 5.795, P < 0.001), and the AUC of the 8-variable KFRE plus NGAL was also significantly higher than those of the 8-variable KFRE and NGAL alone (0.911 vs 0.725, Z = 6.491, P < 0.001; 0.911 vs 0.736, Z = 6.298, P < 0.001).

Conclusion

The KFRE was able to independently predict progression of CKD stage 3-5 to ESRD in Chinese population. The addition of NGAL to the KFRE was able to elevate the predictive value when applied in predicting 2-year ESRD.

Development and validation of interpretable machine learning models to predict glomerular filtration rate in chronic kidney disease Colombian patients

BACKGROUND

ML predictive models have shown their capability to improve risk prediction and assist medical decision-making, nevertheless, there is a lack of accuracy systems to early identify future rapid CKD progressors in Colombia and even in South America.

OBJECTIVE

The purpose of this study was to develop a series of interpretable machine learning models that predict GFR at 6-months, 9-months, and 12-months.

STUDY DESIGN AND SETTING

Over 29,000 CKD patients stage 1 to 3b (estimated GFR, <60 mL/min/1.73 m2) with an average of 3-year follow-up data were included. We used the machine learning extreme gradient boosting (XGBoost) to build three models to predict the next eGFR. Models were internally and externally validated. In addition, we included SHapley Additive exPlanation (SHAP) values to offer interpretable global and local prediction models.

RESULTS

All models showed a good performance in development and external validation. However, the 6-months XGBoost prediction model showed the best performance in internal (MAE average = 6.07; RSME = 78.87), and in external validation (MAE average = 6.45, RSME = 18.94). The top 3 most influential features that pushed the predicted eGFR value to lower values were the interpolated values for eGFR and creatinine, and eGFR at baseline.

CONCLUSION

In the current study we have developed and validated machine learning models to predict the next eGFR value at different intervals. Furthermore, we attempted to approach the need for prediction explanation by offering transparent predictions.

Hyperuricemia - consequences of not initiating therapy. Benefits and drawbacks of treatment

Hyperuricemia, characterized by elevated levels of uric acid in the body, is associated with several health risks, including gout, urolithiasis and cardiovascular disease. Although treatment options are available, they can lead to hypersensitivity reactions, particularly with allopurinol therapy. This paper provides a comprehensive review of the consequences of hyperuricemia, the need for treatment and the potential adverse effects of allopurinol, illustrated by a case study. The study highlights the importance of careful consideration before initiating therapy, particularly in patients with comorbidities and concomitant medication. It emphasizes the need for vigilant monitoring and individualized treatment approaches to reduce adverse effects. In addition, genetic factors, particularly HLA-B*5801, play an important role in determining susceptibility to allopurinol hypersensitivity reactions. This paper highlights the importance of informed decision making in the management of hyperuricemia to optimize patient outcomes while minimizing the risks associated with treatment.

A 4-Variable Model to Predict Cardio-Kidney Events and Mortality in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort (CRIC) Study

INTRODUCTION

Current prognostic models for chronic kidney disease (CKD) are complex and were designed to predict a single outcome. We aimed to develop and validate a simple and parsimonious prognostic model to predict cardio-kidney events and mortality.

METHODS

Patients from the CRIC Study (n = 3,718) were randomly divided into derivation (n = 2,478) and validation (n = 1,240) cohorts. Twenty-nine candidate variables were preselected. Multivariable Cox regression models were developed using stepwise selection for various cardio-kidney endpoints, namely, (i) the primary composite outcome of 50% decline in estimated glomerular filtration rate (eGFR) from baseline, end-stage renal disease, or cardiovascular (CV) mortality; (ii) hospitalization for heart failure (HHF) or CV mortality; (iii) 3-point major CV endpoints (3P-MACE); (iv) all-cause death.

RESULTS

During a median follow-up of 9 years, the primary outcome occurred in 977 patients of the derivation cohort and 501 patients of the validation cohort. Log-transformed N-terminal pro-B-type natriuretic peptide (NT-proBNP), log-transformed high-sensitive cardiac troponin T (hs-cTnT), log-transformed albuminuria, and eGFR were the dominant predictors. The primary outcome risk score discriminated well (c-statistic = 0.83) with a proportion of events of 11.4% in the lowest tertile of risk and 91.5% in the highest tertile at 10 years. The risk model presented good discrimination for HHF or CV mortality, 3P-MACE, and all-cause death (c-statistics = 0.80, 0.75, and 0.75, respectively). The 4-variable risk model achieved similar c-statistics for all tested outcomes in the validation cohort. The discrimination of the 4-variable risk model was mostly superior to that of published models.

CONCLUSION

The combination of NT-proBNP, hs-cTnT, albuminuria, and eGFR in a single 4-variable model provides a unique individual prognostic assessment of multiple cardio-kidney outcomes in CKD.

Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry

BACKGROUND

Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking.

METHODS

Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses.

RESULTS

The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched.

CONCLUSIONS

The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.

Improved equations to estimate GFR in Chinese children with chronic kidney disease

BACKGROUND

There is currently no specific equation for estimating glomerular filtration rate (GFR) in Chinese children with chronic kidney disease (CKD). The commonly used equations are less robust than expected; we therefore sought to derive more appropriate equations for GFR estimation.

METHODS

A total of 751 Chinese children with CKD were divided into 2 groups, training group (n = 501) and validation group (n = 250). In the training group, a univariate linear regression model was used to calculate predictability of variables associated with GFR. Residuals were compared to determine multivariate predictability of GFR in the equation. Standard regression techniques for Gaussian data were used to determine coefficients of GFR-estimating equations after logarithmic transformation of measured GFR (iGFR), height/serum creatinine (height/Scr), cystatin C, blood urea nitrogen (BUN), and height. These were compared with other well-known equations using the validation group.

RESULTS

Median ^99mTc-DTPA GFR was 90.1 (interquartile range: 67.3-108.6) mL/min/1.73 m² in training dataset. Our CKD equation, eGFR (mL/min/1.73 m²) = 91.021 [height(m)/Scr(mg/dL)/2.7]^0.443 [1.2/Cystatin C(mg/L)]^0.335 [13.7/BUN (mg/dL)]^-0.095 [ 0.991^male] [height(m)/1.4]^0.275, was derived. This was further tested in the validation group, with percentages of eGFR values within 30% and 15% of iGFR (P30 and P15) of 76.00% and 48.40%, respectively. For centres with no access to cystatin C, a creatinine-based equation, eGFR (mL/min/1.73 m²) = 89.674 [height(m)/Scr(mg/dL)/2.7]^0.579 [ 1.007^male] [height(m)/1.4]^0.187, was derived, with P30 and P15 73.60% and 49.20%, respectively. These were significantly higher compared to other well-known equations (p < 0.05).

CONCLUSION

We developed equations for GFR estimation in Chinese children with CKD based on Scr, BUN and cystatin C. These are more accurate than commonly used equations in this population.

Meta-analysis of the association between sclerostin level and adverse clinical outcomes in patients undergoing maintenance haemodialysis

BACKGROUND

Studies regarding the relationship of sclerostin (Scl) with clinical outcomes in patients undergoing maintenance haemodialysis have yielded controversial findings. This meta-analysis was performed to investigate the predictive role of Scl in this patient population.

METHODS

Several electronic medical databases (e.g. PubMed, Embase, Web of Science and Cochrane Library) were searched for eligible studies through December 20, 2019. Summary hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated based on Scr level (high or low) using a random or fixed effects model.

RESULTS

From among 641 initially screened publications, 16 eligible studies were included in this meta-analysis. A high Scl level was not associated with cardiovascular events [HR = 0.8 (95% CI, 0.42-1.35)] or all-cause mortality [HR = 0.93 (95% CI, 0.56-1.54)]. There was high heterogeneity, but no evidence of publication bias. Interestingly, a high Scl level was associated with reduced cardiovascular events [HR = 0.44 (95% CI, 0.29-0.69)] in the subgroup by shorter follow-up period or all-cause mortality [pooled HR = 0.58 (95% CI, 0.36-0.91)] by shorter dialysis vintage.

CONCLUSION

This meta-analysis indicated that a high Scl level did not predict total clinical outcomes in patients undergoing maintenance haemodialysis despite survival benefits in the subgroups. The predictive role of Scl in these patients should be further evaluated in large prospective studies.

A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility

BACKGROUND

The Kidney Failure Risk Equation (KFRE) predicts the 2- and 5-year risk of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD) stages 3a-5. Its predictive performance in advanced CKD and in specific disease aetiologies requires further exploration. This study validates the 4- and 8-variable KFREs in an advanced CKD population in the United Kingdom by evaluating discrimination, calibration and clinical utility.

METHODS

Patients enrolled in the Salford Kidney Study who were referred to the Advanced Kidney Care Service (AKCS) clinic at Salford Royal NHS Foundation Trust between 2011 and 2018 were included. The 4- and 8-variable KFREs were calculated on the first AKCS visit and the observed events of ESRD (dialysis or pre-emptive transplantation) within 2- and 5-years were the primary outcome. The area under the receiver operator characteristic curve (AUC) and calibration plots were used to evaluate discrimination and calibration respectively in the whole cohort and in specific disease aetiologies: diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, autosomal dominant polycystic kidney disease (ADPKD) and other diseases. Clinical utility was assessed with decision curve analyses, comparing the net benefit of using the KFREs against estimated glomerular filtration rate (eGFR) cut-offs of < 20 ml/min/1.73m2 and < 15 ml/min/1.73m2 to guide further treatment.

RESULTS

A total of 743 patients comprised the 2-year analysis and 613 patients were in the 5-year analysis. Discrimination was good in the whole cohort: the 4-variable KFRE had an AUC of 0.796 (95% confidence interval [CI] 0.762-0.831) for predicting ESRD at 2-years and 0.773 (95% CI 0.736-0.810) at 5-years, and there was good-to-excellent discrimination across disease aetiologies. Calibration plots revealed underestimation of risk at 2-years and overestimation of risk at 5-years, especially in high-risk patients. There was, however, underestimation of risk in patients with ADPKD for all KFRE calculations. The predictive accuracy was similar between the 4- and 8-variable KFREs. Finally, compared to eGFR-based thresholds, the KFRE was the optimal tool to guide further care based on decision curve analyses.

CONCLUSIONS

The 4- and 8-variable KFREs demonstrate adequate discrimination and calibration for predicting ESRD in an advanced CKD population and, importantly, can provide better clinical utility than using an eGFR-based strategy to inform decision-making.

A Paradigm to Discover Biomarkers Associated With Chronic Kidney Disease Progression

Biomarker discovery in the field of risk prediction in chronic kidney disease (CKD) embraces the prospect of improving our ability to risk stratify future adverse outcomes and thereby guide patient care in a new era of personalised medicine. However, many studies that report biomarkers predictive of CKD progression share a key methodological limitation: failure to characterise patients' renal progression precisely. This weakens any observable association between a biomarker and an outcome poorly defined by a patient's change in renal function over time. In this commentary, we discuss the need for a better approach in this research arena and describe a compelling strategy that has the advantage of offering robust and meaningful biomarker exploration relevant to CKD progression.

Predictive factors of rapid linear renal progression and mortality in patients with chronic kidney disease

BACKGROUND

Risk factors predictive of rapid linear chronic kidney disease (CKD) progression and its associations with end-stage renal disease (ESRD) and mortality requires further exploration, particularly as patients with linear estimated glomerular filtration rate (eGFR) trajectory represent a clear paradigm for understanding true CKD progression.

METHODS

A linear regression slope was applied to all outpatient eGFR values for patients in the Salford Kidney Study who had ≥2 years follow-up, ≥4 eGFR values and baseline CKD stages 3a-4. An eGFR slope (ΔeGFR) of ≤ - 4 ml/min/1.73m2/yr defined rapid progressors, whereas - 0.5 to + 0.5 ml/min/1.73m2/yr defined stable patients. Binary logistic regression was utilised to explore variables associated with rapid progression and Cox proportional hazards model to determine predictors for mortality prior to ESRD.

RESULTS

There were 157 rapid progressors (median ΔeGFR - 5.93 ml/min/1.73m2/yr) and 179 stable patients (median ΔeGFR - 0.03 ml/min/1.73m2/yr). Over 5 years, rapid progressors had an annual rate of mortality or ESRD of 47 per 100 patients compared with 6 per 100 stable patients. Factors associated with rapid progression included younger age, female gender, higher diastolic pressure, higher total cholesterol:high density lipoprotein ratio, lower albumin, lower haemoglobin and a urine protein:creatinine ratio of > 50 g/mol. The latter three factors were also predictive of mortality prior to ESRD, along with older age, smoking, peripheral vascular disease and heart failure.

CONCLUSIONS

There is a heterogenous interplay of risk factors associated with rapid linear CKD progression and mortality in patients with CKD. Furthermore, rapid progressors have high rates of adverse outcomes and require close specialist monitoring.