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Methods Inf Med 2023; 62(05/06): 183-192
DOI: 10.1055/a-2165-5552
Original Article

Performance Characteristics of a Rule-Based Electronic Health Record Algorithm to Identify Patients with Gross and Microscopic Hematuria

Jasmine Kashkoush
1   Department of Urology, Geisinger, Danville, Pennsylvania, United States
,
Mudit Gupta
2   Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania, United States
,
Matthew A. Meissner
1   Department of Urology, Geisinger, Danville, Pennsylvania, United States
,
Matthew E. Nielsen
3   Department of Urology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States
4   Department of Epidemiology, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, North Carolina, United States
5   Department of Health Policy & Management, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, North Carolina, United States
,
H. Lester Kirchner
6   Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, United States
,
Tullika Garg
6   Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, United States
7   Department of Urology, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
› Author Affiliations Funding Geisinger Clinic Research Fund. SRC S-80
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Abstract

Background Two million patients per year are referred to urologists for hematuria, or blood in the urine. The American Urological Association recently adopted a risk-stratified hematuria evaluation guideline to limit multi-phase computed tomography to individuals at highest risk of occult malignancy.

Objectives To understand population-level hematuria evaluations, we developed an algorithm to accurately identify hematuria cases from electronic health records (EHRs).

Methods We used International Classification of Diseases (ICD)-9/ICD-10 diagnosis codes, urine color, and urine microscopy values to identify hematuria cases and to differentiate between gross and microscopic hematuria. Using an iterative process, we refined the ICD-9 algorithm on a gold standard, chart-reviewed cohort of 3,094 hematuria cases, and the ICD-10 algorithm on a 300 patient cohort. We applied the algorithm to Geisinger patients ≥35 years (n = 539,516) and determined performance by conducting chart review (n = 500).

Results After applying the hematuria algorithm, we identified 51,500 hematuria cases and 488,016 clean controls. Of the hematuria cases, 11,435 were categorized as gross, 26,658 as microscopic, 12,562 as indeterminate, and 845 were uncategorized. The positive predictive value (PPV) of identifying hematuria cases using the algorithm was 100% and the negative predictive value (NPV) was 99%. The gross hematuria algorithm had a PPV of 100% and NPV of 99%. The microscopic hematuria algorithm had lower PPV of 78% and NPV of 100%.

Conclusion We developed an algorithm utilizing diagnosis codes and urine laboratory values to accurately identify hematuria and categorize as gross or microscopic in EHRs. Applying the algorithm will help researchers to understand patterns of care for this common condition.

Keywords

electronic health records - laboratory values - outpatient - urology

Supplementary Material



Publication History

Received: 09 May 2022

Accepted: 31 August 2023

Accepted Manuscript online:
04 September 2023

Article published online:
06 October 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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