1. Conformation Window Size Joint Sensitivity Analysis
Real-world Evidence for
Phenotyping Chronic Diabetic Kidney Disease
Lynn S. Huang1, Xufei Zheng2, Fang-Chi Hsu1, Jianzhao Xu3, Maggie CY Ng3, Donald W. Bowden3, Barry I Freedman4, Jing Su1*
1Department of Biostatistical Sciences; 3Center for Genomics and Personalized Medicine Research; 4Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA; 2School of Computer and Information Science, Southwest University, China; *
Abstract
We developed a novel algorithm to accurately and reliably identify chronic diabetic kidney disease (CKD) stages and onset time using electronic medical records. The phenotyping results demonstrated significant improvement over current diagnosis-based phenotypes and facilitate evidence-based clinical research of the progression CKD.
WFBH Diabetic CKD Phenome
WFBH Diabetic Cohort qualified for CKD study
79,434 patients (62.4% of WFBH diabetic patients)
922,698 serum creatinine records for GFR estimation
PCORnet CDM. Pivotal Greenplum MPP Database on Hadoop
Sensitivity Analysis
Wake Forest Baptist Health (WFBH) EMR
Matured EMR infrastructure for clinical informatics research:
> 1.6 million patients
> 1.4 billion records
> 30-year longitudinal coverage
Common Data Model: PCORnet and Carolinas Collaborative
Transient Window Size
Conformation Window Size
𝜏 1 : Transient Window (day)
𝜏 2 : Confirmation Window (month)
KDIGO 2012
Guidelines
ΔCases (signed log10)
𝜏 1 : Transient Window (day)
ΔCases (signed log10)
1.0
1.5
2.5
3.0
3.5
2.0
Clinical
Practice
Current Size: 36” x 48”
Scale by 1.25 times to Final Size: 45” x 60” because AMIA poster guidelines recommend max height of 5’
AMIA Source:
WFU Source:
Joint Sensitivity Analysis
ΔCases (signed log10)
𝜏 1 : Transient Window (day)
𝜏 2 : Confirmation Window (month)
KDIGO 2012 Derived
𝜏 1 Transient Window: 3 months
Problem: missed 1-month follow-up confirmation evidence
𝜏 2 Confirmation Window: 1 year
Problem: less robust due to annual physical check evidence
EMR-based sensitivity analysis suggested:
𝜏 1 Transient Window: 2 weeks
𝜏 2 Confirmation Window: 18 months
Efficient use of clinical evidence
Robust performance
Performance Comparisons
Daily EMR Data ETL and Dissemination
WakeOne
(EPIC)
TDW
(I2B2)
WF SHRINE
SHRINE
EMR-based CKD Staging
Conclusion
Pragmatic CKD Staging algorithm and tool:
Trade-off between clinical guidelines and real-world data availability
Robust and effective for CKD staging and longitudinal studies
Acknowledgements
Center for Genomics and Personalized Medicine Research Pilot, Wake Forest School of Medicine
The National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR001420)
𝜏 1 Transient Window:
Filter out transient eGFR drops
Acute kidney injury
Surgeries or treatments
Fluctuation
𝜏 2 Confirmation Window:
Evidence for sustained kidney function loss