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Open Access Highly Accessed Research article

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

Ammarin Thakkinstian1*, Atiporn Ingsathit1, Amnart Chaiprasert2, Sasivimol Rattanasiri1, Pornpen Sangthawan3, Pongsathorn Gojaseni4, Kriwiporn Kiattisunthorn5, Leena Ongaiyooth5 and Prapaipim Thirakhupt6

Author Affiliations

1 Section for Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

2 Division of Nephrology, Department of Medicine, Phramongkutklao Hospital, Bangkok, Thailand

3 Division of Nephrology, Department of Medicine, Faculty of Medicine, Prince of Songkla University, Songkla, Thailand

4 Division of Nephrology, Department of Medicine, Bhumibol Adulyadej Hospital, Bangkok, Thailand

5 Division of Nephrology, Department of Medicine, Faculty of Medicine, Siriraj Medical School and Hospital, Mahidol University, Bangkok, Thailand

6 Division of Nephrology, Department of Pediatrics, Phramongkutklao Hospital, Bangkok, Thailand

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BMC Nephrology 2011, 12:45  doi:10.1186/1471-2369-12-45

Published: 26 September 2011

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.