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Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings

Objective: To validate the use of electronic algorithms based on International Classification of Diseases (ICD)-10 codes to identify outpatient visits for urinary tract infections (UTI), one of the most common reasons for antibiotic prescriptions. Methods: ICD-10 symptom codes (e.g., dysuria) alone...

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Autores principales: Germanos, George, Light, Patrick, Zoorob, Roger, Salemi, Jason, Khan, Fareed, Hansen, Michael, Gupta, Kalpana, Trautner, Barbara, Grigoryan, Larissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558992/
https://www.ncbi.nlm.nih.gov/pubmed/32854205
http://dx.doi.org/10.3390/antibiotics9090536
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author Germanos, George
Light, Patrick
Zoorob, Roger
Salemi, Jason
Khan, Fareed
Hansen, Michael
Gupta, Kalpana
Trautner, Barbara
Grigoryan, Larissa
author_facet Germanos, George
Light, Patrick
Zoorob, Roger
Salemi, Jason
Khan, Fareed
Hansen, Michael
Gupta, Kalpana
Trautner, Barbara
Grigoryan, Larissa
author_sort Germanos, George
collection PubMed
description Objective: To validate the use of electronic algorithms based on International Classification of Diseases (ICD)-10 codes to identify outpatient visits for urinary tract infections (UTI), one of the most common reasons for antibiotic prescriptions. Methods: ICD-10 symptom codes (e.g., dysuria) alone or in addition to UTI diagnosis codes plus prescription of a UTI-relevant antibiotic were used to identify outpatient UTI visits. Chart review (gold standard) was performed by two reviewers to confirm diagnosis of UTI. The positive predictive value (PPV) that the visit was for UTI (based on chart review) was calculated for three different ICD-10 code algorithms using (1) symptoms only, (2) diagnosis only, or (3) both. Results: Of the 1087 visits analyzed, symptom codes only had the lowest PPV for UTI (PPV = 55.4%; 95%CI: 49.3–61.5%). Diagnosis codes alone resulted in a PPV of 85% (PPV = 84.9%; 95%CI: 81.1–88.2%). The highest PPV was obtained by using both symptom and diagnosis codes together to identify visits with UTI (PPV = 96.3%; 95%CI: 94.5–97.9%). Conclusions: ICD-10 diagnosis codes with or without symptom codes reliably identify UTI visits; symptom codes alone are not reliable. ICD-10 based algorithms are a valid method to study UTIs in primary care settings.
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spelling pubmed-75589922020-10-26 Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings Germanos, George Light, Patrick Zoorob, Roger Salemi, Jason Khan, Fareed Hansen, Michael Gupta, Kalpana Trautner, Barbara Grigoryan, Larissa Antibiotics (Basel) Article Objective: To validate the use of electronic algorithms based on International Classification of Diseases (ICD)-10 codes to identify outpatient visits for urinary tract infections (UTI), one of the most common reasons for antibiotic prescriptions. Methods: ICD-10 symptom codes (e.g., dysuria) alone or in addition to UTI diagnosis codes plus prescription of a UTI-relevant antibiotic were used to identify outpatient UTI visits. Chart review (gold standard) was performed by two reviewers to confirm diagnosis of UTI. The positive predictive value (PPV) that the visit was for UTI (based on chart review) was calculated for three different ICD-10 code algorithms using (1) symptoms only, (2) diagnosis only, or (3) both. Results: Of the 1087 visits analyzed, symptom codes only had the lowest PPV for UTI (PPV = 55.4%; 95%CI: 49.3–61.5%). Diagnosis codes alone resulted in a PPV of 85% (PPV = 84.9%; 95%CI: 81.1–88.2%). The highest PPV was obtained by using both symptom and diagnosis codes together to identify visits with UTI (PPV = 96.3%; 95%CI: 94.5–97.9%). Conclusions: ICD-10 diagnosis codes with or without symptom codes reliably identify UTI visits; symptom codes alone are not reliable. ICD-10 based algorithms are a valid method to study UTIs in primary care settings. MDPI 2020-08-25 /pmc/articles/PMC7558992/ /pubmed/32854205 http://dx.doi.org/10.3390/antibiotics9090536 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Germanos, George
Light, Patrick
Zoorob, Roger
Salemi, Jason
Khan, Fareed
Hansen, Michael
Gupta, Kalpana
Trautner, Barbara
Grigoryan, Larissa
Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings
title Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings
title_full Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings
title_fullStr Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings
title_full_unstemmed Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings
title_short Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings
title_sort validating use of electronic health data to identify patients with urinary tract infections in outpatient settings
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558992/
https://www.ncbi.nlm.nih.gov/pubmed/32854205
http://dx.doi.org/10.3390/antibiotics9090536
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