Chronic kidney disease (CKD) is a precursor to end stage kidney disease (ESKD), which requires major intervention in the form of dialysis or transplant. The prevalence of the ESKD in Thailand from 2002 to 2006 was about 220-286/million population throughout the country [1]. CKD tends to increase according to the increased prevalence of diabetes, hypertension, and economic development within a region. Early identification and targeting of individuals with CKD should be encouraged for the purpose of instituting intervention strategies, such as low-protein dietary changes, close monitoring of blood pressure, control of blood sugar levels, health-monitoring programs, education, exercise, and so on[2].

If the risk factors of CKD are known, one should be able to predict the probability of at risk individuals developing CKD, and thus identify at risk populations. Although many previous studies have assessed the risk factors of CKD in general populations, few non-Asian-based studies have constructed prediction scores using cumulative combinations of risk factors [3, 4, 5, 6]. A hospital-based study by Hemmelgarn et al[4] studied subjects of ages 66 years or older, and thus applying the score to general population will result in poor validity. Two community-based observational studies [3, 5] developed and validated a simple algorithm for CKD stage III or higher based on two demographic data and six medical histories. Among those medical histories, few variables (i.e., a history of heart disease, heart failure, and peripheral vascular disease) were not easily assessed in a community-base setting, and once they were assessed, their validity was still questionable, particularly in developing countries where education & knowledge about the diseases are limited. Thus the scores are not qualified as for a concept of developing a simplified prediction score [7–9], in which the scores should not contain many variables and which should be easily and validly measured. Some prediction scores for diabetes had also been used to predict CKD, but discriminative ability was low[6]. We therefore conducted a study to develop and validate a simplified clinical prediction score for estimate risk of developing CKD in the Thai general population. The scores would aid general-practice physicians in identifying individuals who are at risk of having CKD and should have further investigation and management.

We have developed and validated a simplified clinical prediction score for classifying populations into mild, intermediate-low, intermediate-high, and high risks of developing CKD. The score was made easier and simplified using factors that were readily available and simply assessed in clinical practice. The score shows good calibration and discrimination, as it can be seen from a similarity between observed and predicted values, as well as between the C statistic in the derivative and validation phases, respectively.

We mainly focus on estimating risk of developing CKD, not CKD progression. Our study has strengths in research methods as commented in detail in Ingsathit et al[10]. It is derived from a large number of subjects who were stratified-cluster randomly sampling form areas across Thailand. In addition, we have validated the clinical prediction score using a 200-repetition bootstrap technique, which is considered a good technique for internal validation [15–17]. The C statistic of the simplified score was fair in both derivative and bootstrap data (i.e., 0.77 and 0.74), indicating that the score can well discriminate between CKD and non-CKD subjects. Our model is simplified and should be easily to apply in clinical practice because of required variables are routinely measured. The scoring scheme should be able to apply manually by clinicians and then the score is classified into 4 groups without requiring any calculation.

We however have some limitations. Our design was a cross-sectional survey study and thus the temporal sequence between risk factors and CKD was questionable[18]. External validation has not been performed and generalizability of our prediction model is still needed to determine. Given good represent samples across the country, the model might work well in outside the studied areas in Thailand, or in other countries where prevalence of diabetes (~11.9%), hypertension (27.5%), and kidney stone (5.0%) are similar to Thai population. The overly-optimistic predictions in other populations might be less likely because of an optimistic rate from the bootstrap is only 2.9%.

To our knowledge, only a few previous prediction scores [3–5, 19] for kidney diseases were available in prior literature. The study by Hemmelgarn et al [4] had developed a clinical index for rapid progression of kidney function, which was defined as a decline in eGFR of 25% or greater. Five predictors were included in the clinical index, which were age, heart disease, diabetes, gout, and the use of anti-emetic drugs. Comparatively, in our study, only two predictors (i.e., age and diabetes) were similar, but the others were not significantly associated with CKD and thus were not considered. The ability of the score to correctly discriminate those individuals with and without CKD from our study was fair (area under ROC = 0.77) but it was low for this study (area under ROC = 0.59). Bang et al[3] had well developed scores based on a cross-sectional NHANES 1999-2000 & 2001-2002 data. The performance of their scores had also been both internally and externally tested with good and fair performances (i.e., the C statistic were 0.88 and 0.71, respectively). A suggestion of using the score in clinical practice was also discussed. However, applying a score with a required 9 input variables (i.e., age, female, anemia, hypertension, diabetes, history of cardiovascular disease, congestive heart failure, peripheral vascular disease, and proteinuria) in clinical practice might not be as simple as suggested. Data for a history of cardiovascular disease, congestive heart failure, and peripheral vascular disease may not be valid in developing countries where populations have a limited understanding of the diseases they have been diagnosed with. The ability of discrimination for this score in Asians may not be valid since the prevalence of CKD in Caucasian vs. Asian populations is quite different [20], and also lifestyle factors that directly or indirectly influence CKD may be markedly different. Kshirsagar et al[5] conducted a study on a community-based cohorts with ages of 45 years or older in order to create the best fitting and simplified scores for predicting the incidence of CKD (GFR < 60 ml/min/1.73 m²). The study design was better than previous studies in terms of the predictor-CKD causal relationship, which could be assessed for the cohort study. Two prediction score models were proposed, one with, the full score of 10 predictors (i.e., age, white ethnicity, female, anemia, hypertension, diabetes, history of cardiovascular disease, history of heart failure, low HDL, and peripheral vascular disease), and a simplified score with 8 predictors, omitting two variables, ethnicity and HDL. Again, applying either the best fitting or the reduced model may not so simple since so many variables are required to input in the models. Creating a score by rounding up a coefficient to the nearest integer (higher than estimated) was not clearly described in the paper. For instance, estimated coefficients for ages 60-69 and ≥ 70 years of 1.31 and 1.46 respectively were rounded up to be 2 and 3 instead of rounding down to 2 and 2. In addition, coefficients of other variables that were less than 1 (e.g., 0.2-0.6) were rounded up to be equal to a score of 1 and thus given an equal weight of contribution to the total scores. This would raise the question, for example, of whether gender played the same role as hypertension. The clinical prediction scores for diabetes have also been used for predicting CKD[6]. As expected, the discrimination was low, ranging from 0.60 to 0.71, and generalizability was questionable.

We propose a simple clinical prediction score of CKD to aid general practitioners, internists, or nephrologists in identifying CKD in the general population. Practioners should be encouraged to use the score in routine clinical practice in order to make a more concerted effort in the identification and early treatment of CKD. Vigilant monitoring should be planned to prevent the development of CKD and delay higher stages if it happens. In the long run, this prediction score will be of benefit to the country if end-stage kidney disease can be reduced.