Abstract: TH-PO827
A Computable Phenotype for ADPKD
Session Information
- Cystic Kidney Diseases: Clinical
November 07, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1001 Genetic Diseases of the Kidneys: Cystic
Authors
- Kalot, Mohamad A., University of kansas medical center, Kansas city, Missouri, United States
- El alayli, Abdallah, Lebanese American University, Beirut, Lebanon
- Alkhatib, Mohammed N., Kansas University Medical Center, Kansas, Kansas, United States
- McGreal, Kerri A., University of Kansas Medical Center, Kansas City, Kansas, United States
- Yu, Alan S.L., University of Kansas Medical Center, Kansas City, Kansas, United States
- Mustafa, Reem, University of Kansas, Kansas City, Kansas, United States
Background
Autosomal Dominant Polycystic kidney disease (ADPKD) is the most common inherited disease causing of end-stage kidney disease (ESKD). A computable phenotype is an algorithm used to identify a certain set of patients within an electronic medical record system. Developing a computable phenotype that can identify patients with ADPKD will assist researchers in designing studies and clinical trial recruitment within this population.
Methods
We reviewed a random sample of 1000 medical charts from the University of Kansas Medical Center database. The sample was divided into four groups (A, B, C, and D) of 250 patients each. Group A included patients followed in nephrology clinics who had ICD (International Classification of Diseases) 9 or 10 codes for ADPKD, Group B included those with no ICD codes of ADPKD, but with ICD codes of renal cysts. Group C and D had patients who did not attend the nephrology clinic, with and without ICD 9/10 codes for ADPKD respectively. We used the ICD 9 codes 753.12-13, and the ICD 10 codes Q61.2-3 for ADPKD. We used the ICD 9 code 593.2 and the ICD 10 code N28.1 for renal cysts. For all medical records, we extracted family history of PKD, hypertension, glomerular filtration rate, proteinuria, kidney size, number of kidney cysts. Then, we compared the data to internationally accepted diagnostic criteria for ADPKD to determine the diagnosis of ADPKD (reference standard). We calculated test accuracy results for the proposed computable phenotype for ADPKD.
Results
The computable phenotype to identify patients with ADPKD who attended the nephrology clinic has a sensitivity of 98.7% (95% CI 96.4-99.7), a specificity of 84.1% (95% CI 79.5-88.1), a positive predictive value (PPV) of 83.4 % (95% CI 79.43 - 86.72%), and a negative predictive value (NPV) of 98.8% (95% CI 96.4-99.6). For those who did not attend the nephrology clinic the computable phenotype has a sensitivity of 97.1% (95% CI 93.3-99.0), a specificity of 82.0% (95% CI 77.4-86.1), a PPV of 74.0% (95% CI 69.2-78.3), and a NPV of 98.2[RM1] % (95% CI 95.7-99.2).
Conclusion
A computable phenotype using the ICD9 and 10 codes can correctly identify most patients with ADPKD, and can be used by researchers to categorize ADPKD patients’ cohorts with limited inaccuracy.