We examined predictors of incident chronic kidney disease among individuals in the community and developed a risk score including the predictors: age, hypertension, diabetes, eGFR category and albuminuria. The risk score demonstrates excellent discrimination and calibration, and the model performed well when applied externally to ARIC Study participants. Finally, although derived from an exclusively white population, the risk score performed well in African-Americans.
Improved clinical prediction is a cornerstone of personalized and individualized medicine(30
), and prediction tools such as the Framingham cardiovascular risk score(16
) have helped shape public health policy in the primary prevention of cardiovascular disease(17
). However, despite the identification of several key renal risk factors from prospective studies(13
), similarly useful models for kidney disease prediction do not exist. We are aware of two prior published risk prediction scores for incident chronic kidney disease. The first was derived and validated using data from 14,155 middle-aged and older adults in the community(31
). The final model included 8 variables: age, gender, anemia, hypertension, diabetes mellitus, cardiovascular disease, history of heart failure, and peripheral vascular disease. This risk score had moderate discriminatory power (c-statistic=0.70) and did not contain data on baseline GFR or proteinuria. The second risk score was compromised by poor discriminatory power (c-statistic=0.67) and short follow-up (median 2.2 years)(32
). The present work advances efforts at risk prediction in renal disease, permitting early identification of at-risk individuals with a high degree of accuracy, using a parsimonious set of readily available clinical variables.
There are several potential implications of this work. First, by allowing physicians to determine an individual’s estimated risk for chronic kidney disease, the score may inform clinical counseling and decision-making. For example, a higher chronic kidney disease risk score may weigh against a decision to use a potentially nephrotoxic intervention, favor increased intensity and frequency of follow-up testing and, in equivocal cases, assist in the decision to institute renal primary prevention measures. Use of the score may further serve to raise the profile of kidney disease among the general population, a key goal given current awareness rates of less than 10% in people with chronic kidney disease stage 3(4).
Second, the score may prove useful in the evaluation of new biomarkers of renal risk(33
). A key challenge involves demonstrating that novel biomarkers offer independent, incremental information beyond what is already known based on traditional risk factors. A sophisticated new biomarker may have good independent predictive ability, but should be evaluated by its ability to improve risk prediction beyond the suggested risk equation, which comprises less expensive, traditional factors.
Finally, it is noteworthy that the power of the risk score is predominantly driven by clinical risk factors, based on the fact that the basic clinical model of age, diabetes and hypertension has considerable discriminatory power of itself (Model 1; c-statistic 0.786). As this model requires no prior laboratory testing to be performed, it could be used for focused renal screening, identifying individuals in whom creatinine measurement would be most cost-effective. A formal cost-effectiveness analysis would be required before introducing the score into the public health arena, one of several potential future research directions emerging from this work. Other avenues for future research include an assessment of performance in predicting advanced chronic kidney disease or end-stage kidney disease, a comparison with a genetic risk score comprising recently identified genetic predictors of chronic kidney disease (34
), or an assessment of performance in other ethnic groups, such as Hispanics or Asians.
There are numerous strengths to this study, including the community-based sample with long duration of follow-up, rigorous and detailed assessment of risk factors including measures of baseline renal function and proteinuria, and external validation in the bi-ethnic ARIC Study. The parsimonious list of variables in the final model is also a significant strength, enhancing the score’s utility and applicability. Several limitations should also be acknowledged. Baseline and follow-up creatinine were measured on a single occasion; multiple measurements in observational epidemiology are not feasible. Furthermore, GFR was estimated using the MDRD equation(21
), which may underestimate GFR in both healthy individuals and those with chronic kidney disease (36
). However, a comparison of definitions of incident chronic kidney disease in the setting of epidemiological research demonstrates that the present definition (estimated GFR of <60ml/min/1.73m2
) is the most sensitive(37
), which is desirable in view of the potential application of the risk score for population screening. In addition, the target populations were European- and African-Americans; the generalizability is limited in other ethnicities. Nearly 20% of participants did not return for the eighth exam cycle, potentially biasing our results towards the null. Although family history is a risk factor for kidney disease(38
), too few participants had complete family history data to construct a risk function. Finally, the risk score should not be used as a substitute for established urinalysis screening intervals in people with diabetes, nor for appropriate and timely nephrology referral in cases where persistent proteinuria exists or where progression is rapid.
In conclusion, we have developed a simple risk prediction algorithm that estimates an individual’s 10-year probability of developing chronic kidney disease, permitting risk stratification for chronic kidney disease using clinical factors readily accessible in primary care. The role of this risk score in identifying individuals in the community at high risk of chronic kidney disease warrants further investigation.