A simplified clinical prediction score of chronic kidney disease: a cross-sectional-survey study

Knowing the risk factors of CKD should be able to identify at risk populations. We thus aimed to develop and validate a simplified clinical prediction score capable of indicating those at risk.

A community-based cross-sectional survey study was conducted. Ten provinces and 20 districts were stratified-cluster randomly selected across four regions in Thailand and Bangkok. The outcome of interest was chronic kidney disease stage I to V versus non-CKD. Logistic regression was applied to assess the risk factors. Scoring was created using odds ratios of significant variables. The ROC curve analysis was used to calibrate the cut-off of the scores. Bootstrap was applied to internally validate the performance of this prediction score.

Three-thousand, four-hundred and fifty-nine subjects were included to derive the prediction scores. Four (i.e., age, diabetes, hypertension, and history of kidney stones) were significantly associated with the CKD. Total scores ranged from 4 to 16 and the score discrimination was 77.0%. The scores of 4-5, 6-8, 9-11, and ≥ 12 correspond to low, intermediate-low, intermediate-high, and high probabilities of CKD with the likelihood ratio positive (LR+) of 1, 2.5 (95% CI: 2.2-2.7), 4.9 (95% CI: 3.9 - 6.3), and 7.5 (95% CI: 5.6 - 10.1), respectively. Internal validity was performed using 200 repetitions of a bootstrap technique. Calibration was assessed and the difference between observed and predicted values was 0.045. The concordance C statistic of the derivative and validated models were similar, i.e., 0.770 and 0.741.

A simplified clinical prediction score for estimating risk of having CKD was created. The prediction score may be useful in identifying and classifying at riskpatients. However, further external validation is needed to confirm this.