Risk scores to predict decreased glomerular filtration rate at 10 years in an Asian general population

Risk Prediction Score for Chronic Kidney Disease in Healthy Adults and Adults With Type 2 Diabetes: Systematic Review

INTRODUCTION

Chronic kidney disease (CKD) is an important public health problem. In 2017, the global prevalence was estimated at 9.1%. Appropriate tools to predict the risk of developing CKD are necessary to prevent its progression. Type 2 diabetes is a leading cause of CKD; screening the population living with the disease is a cost-effective solution to prevent CKD. The aim of our study was to identify the existing prediction scores and their diagnostic accuracy for detecting CKD in apparently healthy populations and populations with type 2 diabetes.

METHODS

We conducted an electronic search in databases, including Medline/PubMed, Embase, Health Evidence, and others. For the inclusion criteria we considered studies with a risk predictive score in healthy populations and populations with type 2 diabetes. We extracted information about the models, variables, and diagnostic accuracy, such as area under the receiver operating characteristic curve (AUC), C statistic, or sensitivity and specificity.

RESULTS

We screened 2,359 records and included 13 studies for healthy population, 7 studies for patients with type 2 diabetes, and 1 for both populations. We identified 12 models for patients with type 2 diabetes; the range of C statistic was from 0.56 to 0.81, and the range of AUC was from 0.71 to 0.83. For healthy populations, we identified 36 models with the range of C statistics from 0.65 to 0.91, and the range of AUC from 0.63 to 0.91.

CONCLUSION

This review identified models with good discriminatory performance and methodologic quality, but they need more validation in populations other than those studied. This review did not identify risk models with variables comparable between them to enable conducting a meta-analysis.

Prediction of incident chronic kidney disease in a population with normal renal function and normo-proteinuria

Regarding the irreversible clinical course of chronic kidney disease, identifying high-risk subjects susceptible to Chronic Kidney Disease (CKD) has an important clinical implication. Previous studies have developed risk prediction models identifying high-risk individuals within a group, including those who may have experienced minor renal damage, to provide an opportunity for initiating therapies or interventions at earlier stages of CKD. To date, there were no other studies developed a prediction model with quantitative risk factors to detect the earliest stage of CKD that individuals with normal renal function in the general population may experience. We derived 11,495,668 individuals with an estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m2 and normo-proteinuria, who underwent health screening ≥2 times between 2009 and 2016 from the prospective nationwide registry cohort. The primary outcome was the incident CKD, defined by an eGFR <60 mL/min/1.73 m2. Sex-specific multivariate Cox regression models predicting the 8-year incident CKD risk were developed. The performance of developed models was assessed using Harrell's C and the area under the receiver operating characteristics curve (AUROC) with 10-fold cross-validation. Both men and women, who met the definition of incident CKD, were older and had more medical treatment history in hypertension and diabetes. Harrell's C and AUROC of the developed prediction models were 0.82 and 0.83 for men and 0.79 and 0.80 for women. This study developed sex-specific prediction equations with reasonable performance in a population with normal renal function.

Chronic kidney disease risk prediction scores assessment and development in Mexican adult population

Background

Chronic kidney disease (CKD) is a major public health problem, with considerable growth in prevalence and mortality in recent years. Screening of CKD at primary care is crucial for the implementation of prevention strategies. The aims of this study are to assess CKD risk prediction scores and to develop a risk prediction score for the Mexican adult population.

Methods

Data from the Mexican National Health and Nutrition Survey 2016 was utilized and 3463 participants ≥ 20 years old were included. Reduced renal function with Glomerular filtration rate and/or the presence of albuminuria was defined as CKD. Multiple logistic regression models were performed for the creation of a training and validation model. Additionally, several models were validated in our Mexican population.

Results

The developed training model included sex, age, body mass index, fast plasma glucose, systolic blood pressure, and triglycerides, as did the validation model. The area under the curve (AUC) was 0.78 (95% CI: 0.72, 0.79) for training model, and 0.76 (95% CI: 0.71, 0.80) in validation model for Mexican adult population. Age, female gender, presence of diabetes and hypertension, elevated systolic and diastolic blood pressure, serum and urinary creatinine, and higher HbA1c were significantly associated with the prevalent chronic kidney disease. Previous CKD risk predictive models were evaluated with a representative sample of the Mexican adult population, their AUC was between 0.61 and 0.78.

Conclusion

The designed CKD risk predictive model satisfactorily predicts using simple and common variables in primary medical care. This model could have multiple benefits; such as, the identification of the population at risk, and prevention of CKD.

Systematic review of diagnostic and prognostic models of chronic kidney disease in low-income and middle-income countries

OBJECTIVE

To summarise available chronic kidney disease (CKD) diagnostic and prognostic models in low-income and middle-income countries (LMICs).

METHOD

Systematic review (Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines). We searched Medline, EMBASE, Global Health (these three through OVID), Scopus and Web of Science from inception to 9 April 2021, 17 April 2021 and 18 April 2021, respectively. We first screened titles and abstracts, and then studied in detail the selected reports; both phases were conducted by two reviewers independently. We followed the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies recommendations and used the Prediction model Risk Of Bias ASsessment Tool for risk of bias assessment.

RESULTS

The search retrieved 14 845 results, 11 reports were studied in detail and 9 (n=61 134) were included in the qualitative analysis. The proportion of women in the study population varied between 24.5% and 76.6%, and the mean age ranged between 41.8 and 57.7 years. Prevalence of undiagnosed CKD ranged between 1.1% and 29.7%. Age, diabetes mellitus and sex were the most common predictors in the diagnostic and prognostic models. Outcome definition varied greatly, mostly consisting of urinary albumin-to-creatinine ratio and estimated glomerular filtration rate. The highest performance metric was the negative predictive value. All studies exhibited high risk of bias, and some had methodological limitations.

CONCLUSION

There is no strong evidence to support the use of a CKD diagnostic or prognostic model throughout LMIC. The development, validation and implementation of risk scores must be a research and public health priority in LMIC to enhance CKD screening to improve timely diagnosis.

Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study

AIMS

To develop a nomogram for incident chronic kidney disease (CKD) risk evaluation among community residents with high cardiovascular disease (CVD) risk.

METHODS

In this retrospective cohort study, 5730 non-CKD residents with high CVD risk participating the National Basic Public Health Service between January 2015 and December 2020 in Guangzhou were included. Endpoint was incident CKD defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m² during the follow-up period. The entire cohorts were randomly (2:1) assigned to a development cohort and a validation cohort. Predictors of incident CKD were selected by multivariable Cox regression and stepwise approach. A nomogram based on these predictors was developed and evaluated with concordance index (C-index) and area under curve (AUC).

RESULTS

During the median follow-up period of 4.22 years, the incidence of CKD was 19.09% (n=1094) in the entire cohort, 19.03% (727 patients) in the development cohort and 19.21% (367 patients) in the validation cohort. Age, body mass index, eGFR 60-89 mL/min/1.73 m², diabetes and hypertension were selected as predictors. The nomogram demonstrated a good discriminative power with C-index of 0.778 and 0.785 in the development and validation cohort. The 3-year, 4-year and 5-year AUCs were 0.817, 0.814 and 0.834 in the development cohort, and 0.830, 0.847 and 0.839 in the validation cohort.

CONCLUSION

Our nomogram based on five readily available predictors is a reliable tool to identify high-CVD risk patients at risk of incident CKD. This prediction model may help improving the healthcare strategies in primary care.

Scoring model to predict risk of chronic kidney disease in Chinese health screening examinees with type 2 diabetes

PURPOSE

As health screening continues to increase in China, there is an opportunity to integrate a large number of demographic as well as subjective and objective clinical data into risk prediction modeling. The aim of this study was to develop and validate a prediction model for chronic kidney disease (CKD) in Chinese health screening examinees with type 2 diabetes mellitus (T2DM).

METHODS

We conducted a retrospective cohort study consisting of 2051 Chinese T2DM patients between 35 and 78 years old who were enrolled in the XY3CKD Follow-up Program between 2009 and 2010. All participants were randomly assigned into a derivation set or a validation set at a 2:1 ratio. Cox proportional hazards regression model was selected for the analysis of risk factors for the development of the proposed risk model of CKD. We established a prediction model with a scoring system following the steps proposed by the Framingham Heart Study.

RESULTS

The mean follow-up was 8.52 years, with a total of 315 (23.20%) and 189 (27.27%) incident CKD cases in the derivation set and validation set, respectively. We identified the following risk factors: age, gender, body mass index, duration of type 2 diabetes, variation of fasting blood glucose, stroke, and hypertension. The points were summed to obtain individual scores (from 0 to 15). The areas under the curve of 3-, 5- and 10-year CKD risks were 0.843, 0.799 and 0.780 in the derivation set and 0.871, 0.803 and 0.785 in the validation set, respectively.

CONCLUSIONS

The proposed scoring system is a promising tool for further application of assisting Chinese medical staff for early prevention of T2DM complications among health screening examinees.

Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study

OBJECTIVES

To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes.

DESIGN

Systematic review and external validation.

DATA SOURCES

PubMed and Embase.

ELIGIBILITY CRITERIA

Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes.

METHODS

Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic).

RESULTS

41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons.

CONCLUSIONS

This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020192831.

Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

OBJECTIVES

The objective of this study was to investigate predictors and develop risk equations for stage-3 chronic kidney disease (CKD) in Thai patients with type 2 diabetes mellitus (DM).

METHODS

A retrospective cohort study was conducted in patients with type 2 DM. The outcome was the development of stage-3 CKD. The data set was randomly split into training and validation data sets. Cox proportional hazard regression was used for model development. Discrimination (Harrell's C statistic) and calibration (the Hosmer-Lemeshow chi-square test and survival probability curve) were applied to evaluate model performance.

RESULTS

In total, 2178 type 2 DM patients without stage-3 CKD, visiting the hospital from January 1, 2008, to December 31, 2017, were recruited, with median follow-up time of 1.29 years (interquartile range, 0.5-2.5 years); 385 (17.68%) subjects had developed stage-3 CKD. The final predictors included age, male sex, urinary albumin to creatinine ratio, estimated glomerular filtration rate, and hemoglobin A1c. Two 3-year stage-3 CKD risk models, model 1 (laboratory model) and model 2 (simplified model), had the C statistic in validation data sets of 0.890 and 0.812, respectively.

CONCLUSIONS

Two 3-year stage-3 CKD risk models were developed for Thai patients with type 2 DM. Both models have good discrimination and calibration. These stage-3 CKD prediction models could equip health providers with tools for clinical management and supporting patient education.

Risk scores for predicting incident chronic kidney disease among rural Chinese people: a village-based cohort study

Background

Few chronic kidney disease (CKD) risk prediction models have been investigated in low- and middle-income areas worldwide. We developed new risk scores for predicting incident CKD in low- and middle-income rural Chinese populations.

Methods

Data from the Handan Eye Study, which was a village-based cohort study and conducted from 2006 to 2013, were utilized as part of this analysis. The present study utilized data generated from 3266 participants who were ≥ 30 years of age. Two risk models for predicting incident CKD were derived using two-thirds of the sample cohort (selected randomly) using stepwise logistic regression, and were subsequently validated using data from the final third of the sample cohort. In addition, two simple point systems for incident CKD were generated according to the procedures described in the Framingham Study. CKD was defined as reduced renal function (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m²) or the presence of albuminuria (urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g).

Results

The Simple Risk Score included waist circumference, systolic blood pressure (SBP), diabetes, sex, and education. The Best-fit Risk Score included urinary albumin-to-creatinine ratio, SBP, C-reactive protein, triglyceride, sex, education, and diabetes. In the validation sample, the areas under the receiver operating curve of the Simple Risk Score and Best-fit Risk Score were 0.717 (95% CI, 0.689–0.744) and 0.721 (95% CI, 0.693–0.748), respectively; the discrimination difference between the score systems was not significant (P = 0.455). The Simple Risk Score had a higher Youden index, sensitivity, and negative predictive value, with an optimal cutoff value of 14.

Conclusions

Our Simple Risk Score for predicting incident CKD in a low- and middle-income rural Chinese population will help identify individuals at risk for developing incident CKD.

A risk scoring system for the decreased glomerular filtration rate in Chinese general population

Objective

The aim of this study was to establish a risk scoring system for the decreased glomerular filtration rate (GFR) in Chinese general population.

Methods

Totally 781 participants who underwent a health checkup in The First Affiliated Hospital of Nanjing Medical University from January to September 2017 were involved in the study. Significant variables chosen by multivariable logistic regression analysis were allocated the integral scores in proportion to its odds ratio (OR), and then the risk of decreased GFR was assessed based on the scores.

Results

The people with abnormal homocysteine (Hcy) level (OR: 1.534, 95% confidential interval [CI]: 1.075‐2.190, P = .018), males (OR: 2.054, 95%CI: 1.365‐3.092, P < .001), and those at the age of 46‐52 years (OR: 2.943, 95%CI: 1.546‐5.605, P = .001), 52‐59 years (OR: 3.664, 95%CI: 1.937‐6.931, P < .001) and ≥59 years (OR: 13.452, 95%CI: 7.339‐24.657, P < .001) were subjected to GFR reduction. These three variables were allocated the integral scores in proportion to its OR, and four risk categories were divided according to the scores. The prevalence of the decreased GFR in people with low (score 0‐4, n = 8), below the average (score 4‐6, n = 37), above the average (score 6‐13, n = 47), and high risks (score ≥ 13, n = 103) was 5.26%, 16.89%, 22.93% and 50.24%, respectively, and this prevalence raised with the increase of scores (P < .001).

Conclusions

A risk scoring system is developed in this study, which may offer a specific risk stratification for GFR reduction in Chinese general population.