Abstract: TH-PO545
CKD Phenotype Validation Using Electronic Health Records (EHR): A Pilot Study
Session Information
- CKD: Health Services, Disparities, Prevention
November 02, 2017 | Location: Hall H, Morial Convention Center
Abstract Time: 10:00 AM - 10:00 AM
Category: Chronic Kidney Disease (Non-Dialysis)
- 307 CKD: Health Services, Disparities, Prevention
Authors
- Stanifer, John W., Duke University, Durham, North Carolina, United States
- Cameron, C. Blake, Duke University Medical Center, Durham, North Carolina, United States
- Beasley, Cherry M, The University of North Carolina at Pembroke, Pembroke, North Carolina, United States
- Wang, Daphne W, Duke University, Durham, North Carolina, United States
- Bhavsar, Nrupen Anjan, Duke University School of Medicine , Durham, North Carolina, United States
- Boulware, L. Ebony, Duke University School of Medicine , Durham, North Carolina, United States
- Diamantidis, Clarissa Jonas, Duke University School of Medicine , Durham, North Carolina, United States
Background
Few studies have assessed the accuracy of EHR-based methods for detecting CKD, and phenotypes validated from EHR data abstraction alone are inadequate.
Methods
In a rural healthcare system we piloted a novel EHR-based CKD detection phenotype. Individuals were assigned to one of six mutually exclusive groups according to expected likelihood of CKD from available EHR data. Group 1 had individuals with persistently (>90 days) reduced eGFR (<60ml/min/1.73m2) or albuminuria (≥30mg/g) or an ICD diagnosis of ESRD or kidney replaced by transplant; (2) individuals with 1 reduced eGFR or albuminuria and a comorbidity index ≥4, adopted from the Screening for Occult Renal Disease prediction tool; (3) individuals with 1 reduced eGFR value or albuminuria and comorbidity index <4; (4) individuals with no kidney function measurements and comorbidity index ≥4; (5) individuals with no kidney function measurements and comorbidity index <4; (6) individuals with normal eGFR and no albuminuria. As a reference standard for validation, we randomly sampled individuals from each group and assessed for CKD through measured assessments, with CKD defined by KDIGO 2012 criteria or self-reported transplant or dialysis. We calculated sensitivity and specificity and used ROC curves to assess performance of the CKD phenotype.
Results
Of 59,848 adults in the EHR, 4036 (7%) were classified group 1; 1690 (3%) group 2; 3007 (5%) group 3; 4726 (8%) group 4; 39,349 group 5 (66%); and 7040 (12%) group 6. For validation, we enrolled 110 individuals, of whom 75 (68%) had CKD based on the reference standard. The CKD phenotype showed a range of sensitivity and specificity corresponding with the CKD phenotype groups (table). The ROC curve area was 0.82 (95% CI 0.74-0.90).
Conclusion
Our CKD phenotype showed ability to detect CKD in a small sample identified from the EHR. Additional studies are needed to validate the CKD detection phenotype in other settings and larger samples.
CKD phenotype performance
| Group | Sensitivity | Specificity | Likelihood Ratio |
| ≤6 | 100% | 0% | 1.0 |
| ≤5 | 100% | 9% | 1.1 |
| ≤4 | 99% | 17% | 1.2 |
| ≤3 | 96% | 37% | 1.5 |
| ≤2 | 84% | 54% | 1.8 |
| 1 | 79% | 83% | 4.6 |
Funding
- Private Foundation Support