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Abstract: TH-PO1060

Identifying CKD of Unknown Etiology in Sri Lanka

Session Information

Category: CKD (Non-Dialysis)

  • 1901 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention


  • Anand, Shuchi, Stanford University, Palo Alto, California, United States
  • Montez-Rath, Maria E., Stanford University School of Medicine, Palo Alto, California, United States
  • Adasooriya, Dinuka Madhushan, Teaching Hospital, Kandy, Bamunakotuwa, Sri Lanka
  • Ratnatunga, Neelakanthi Vajira, Faculty of Medicine university of Peradeniya Sri Lanka, Kandy, Sri Lanka
  • Kambham, Neeraja, Stanford University, Palo Alto, California, United States
  • Badurdeen, Zeid, University of Peradeniya, Sri Lanka, Kandy, Sri Lanka
  • Schensul, Stephen L., University of Connecticut School of Medicine, Farmington, Connecticut, United States
  • Vlahos, Penny, University of Connecticut, Groton, Connecticut, United States
  • Haider, Lali (Lalarukh), University of CT Health Center, West Hartford, Connecticut, United States
  • Bhalla, Vivek, Stanford University, Palo Alto, California, United States
  • Levin, Adeera, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada
  • Chertow, Glenn Matthew, Stanford University School of Medicine, Palo Alto, California, United States
  • Nanayakkara, Nishantha, Teaching Hospital, Kandy, Bamunakotuwa, Sri Lanka

A kidney disease of unknown cause is common in Sri Lanka’s lowland region. No evidence exists to inform an approach to a non-invasive clinical diagnosis.


In a prospective study to determine whether non-invasive measures can identify CKDu, we surveyed 600 new patients coming to nephrology clinic in a hospital servicing endemic regions over one year. 87 underwent kidney biopsy; 43 (49%) had a pathology diagnosis of tubulointerstitial disease. Using logistic regression, we tested the association of nine pre-selected factors with likelihood of tubulointerstitial nephritis on biopsy. We used bootstrap validation to calculate the model validated AUC. We tested a Full model and five parsimonious models.


AUC for the Full model to predict CKDu was 0.82. A parsimonious model with age, serum albumin, and urine dipstick for protein had an AUC of 0.84 and bootstrap calibration slope of 0.84 (Table 1); with PPV 82.9% and NPV 80.4%. Patients with diabetes or hypertension recommended for kidney biopsy did not experience lower odds of a CKDu diagnosis.


We developed a standardized approach relying on non-invasive measures to identify probable cases of CKDu in Sri Lanka. Such an approach can strengthen CKDu surveillance, geographic mapping and rigorous investigations into cause via case-control studies.

PredictorsFull modelParsimonious model
Urine dipstick negative for protein**
Age (cubic spline)**
Serum albumin**
No pyuria* 
Urine dipstick negative * Serum albumin**
C statistic0.890.87
Specificity 79.684.1
Positive predictive value80.082.9
Negative Value83.380.4
Bootstrap Validated C statistic0.820.84
Calibration slope0.590.84


  • Clinical Revenue Support