Journal article
A Computable Phenotype for Autosomal Dominant Polycystic Kidney Disease
Kidney360, Vol.2(11), pp.1728-1733
11/25/2021
DOI: 10.34067/KID.0000852021
PMCID: PMC8785841
PMID: 35372997
Abstract
A computable phenotype is an algorithm used to identify a group of patients within an electronic medical record system. Developing a computable phenotype that can accurately identify patients with autosomal dominant polycystic kidney disease (ADPKD) will assist researchers in defining patients eligible to participate in clinical trials and other studies. Our objective was to assess the accuracy of a computable phenotype using International Classification of Diseases 9th and 10th revision (ICD-9/10) codes to identify patients with ADPKD.
We reviewed four random samples of approximately 250 patients on the basis of ICD-9/10 codes from the EHR from the Kansas University Medical Center database: patients followed in nephrology clinics who had ICD-9/10 codes for ADPKD (Neph+), patients seen in nephrology clinics without ICD codes for ADPKD (Neph-), patients who were not followed in nephrology clinics with ICD codes for ADPKD (No Neph+), and patients not seen in nephrology clinics without ICD codes for ADPKD (No Neph-). We reviewed the charts and determined ADPKD status on the basis of internationally accepted diagnostic criteria for ADPKD.
The computable phenotype to identify patients with ADPKD who attended nephrology clinics has a sensitivity of 99% (95% confidence interval [95% CI], 96.4 to 99.7) and a specificity of 84% (95% CI, 79.5 to 88.1). For those who did not attend nephrology clinics, the sensitivity was 97% (95% CI, 93.3 to 99.0), and a specificity was 82% (95% CI, 77.4 to 86.1).
A computable phenotype using the ICD-9/10 codes can correctly identify most patients with ADPKD, and can be utilized by researchers to screen health care records for cohorts of patients with ADPKD with acceptable accuracy.
Details
- Title: Subtitle
- A Computable Phenotype for Autosomal Dominant Polycystic Kidney Disease
- Creators
- Mohamad A Kalot - University at Buffalo, State University of New YorkAbdallah El Alayli - University of Kansas Medical CenterMohammad Al Khatib - Hamad Medical CorporationNedaa Husainat - Department of Internal Medicine, St. Mary's Hospital, St. Louis, Missouri.Kerri McGreal - University of Kansas Medical CenterDiana I Jalal - University of IowaAlan S L Yu - University of Kansas Medical CenterReem A Mustafa - University of Kansas Medical Center
- Resource Type
- Journal article
- Publication Details
- Kidney360, Vol.2(11), pp.1728-1733
- DOI
- 10.34067/KID.0000852021
- PMID
- 35372997
- PMCID
- PMC8785841
- ISSN
- 2641-7650
- eISSN
- 2641-7650
- Grant note
- P30 DK106912 / NIDDK NIH HHS U54 DK126126 / NIDDK NIH HHS
- Language
- English
- Date published
- 11/25/2021
- Academic Unit
- Nephrology; Internal Medicine
- Record Identifier
- 9984362685302771
Metrics
27 Record Views