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693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification

BACKGROUND: Studies on infective endocarditis (IE) have relied on International Classification of Diseases (ICD) codes to identify cases but few have validated this method which may be prone to misclassification. Examination of clinical narrative data could offer greater accuracy and richness. METHO...

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Autores principales: Nina Kim, H, Gupta, Ayushi, Lan, Kristine F, Stewart, Jenell C, Dhanireddy, Shireesha, Corcorran, Maria A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8644334/
http://dx.doi.org/10.1093/ofid/ofab466.890
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author Nina Kim, H
Gupta, Ayushi
Lan, Kristine F
Stewart, Jenell C
Dhanireddy, Shireesha
Corcorran, Maria A
author_facet Nina Kim, H
Gupta, Ayushi
Lan, Kristine F
Stewart, Jenell C
Dhanireddy, Shireesha
Corcorran, Maria A
author_sort Nina Kim, H
collection PubMed
description BACKGROUND: Studies on infective endocarditis (IE) have relied on International Classification of Diseases (ICD) codes to identify cases but few have validated this method which may be prone to misclassification. Examination of clinical narrative data could offer greater accuracy and richness. METHODS: We evaluated two algorithms for IE identification from 7/1/2015 to 7/31/2019: (1) a standard query of ICD codes for IE (ICD-9: 424.9, 424.91, 424.99, 421.0, 421.1, 421.9, 112.81, 036.42 and ICD-10: I38, I39, I33, I33.9, B37.6 and A39.51) with or without procedure codes for echocardiogram (93303-93356) and (2) a key word, pattern-based text query of discharge summaries (DS) that selected on the term “endocarditis” in fields headed by “Discharge Diagnosis” or “Admission Diagnosis” or similar. Further coding extracted the nature and type of valve and the organism responsible for the IE if present in DS. All identified cases were chart reviewed using pre-specified criteria for true IE. Positive predictive value (PPV) was calculated as the total number of verified cases over the algorithm-selected cases. Sensitivity was the total number of algorithm-matched cases over a final list of 166 independently identified true IE cases from ID and Cardiology services. Specificity was defined using 119 pre-adjudicated non-cases minus the number of algorithm-matched cases over 119. RESULTS: The ICD-based query identified 612 individuals from July 2015 to July 2019 who had a hospital billing code for infective endocarditis; of these, 534 also had an echocardiogram. The DS query identified 387 cases. PPV for the DS query was 84.5% (95% confidence interval [CI] 80.6%, 87.8%) compared with 72.4% (95% CI 68.7%, 75.8%) for ICD only and 75.8% (95% CI 72.0%, 79.3%) for ICD + echo queries. Sensitivity was 75.9% for the DS query and 86.8-93.4% for the ICD queries. Specificity was high for all queries >94%. The DS query also yielded valve data (prosthetic, tricuspid, pulmonic, aortic or mitral) in 60% and microbiologic data in 73% of identified cases with an accuracy of 94% and 90% respectively when assessed by chart review. Table 1. Test Characteristics of Three Electronic Health Record Queries for Infective Endocarditis [Image: see text] CONCLUSION: Compared to traditional ICD-based queries, text-based queries of discharge summaries have the potential to improve precision of IE case ascertainment and extract key clinical variables. DISCLOSURES: All Authors: No reported disclosures
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spelling pubmed-86443342021-12-06 693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification Nina Kim, H Gupta, Ayushi Lan, Kristine F Stewart, Jenell C Dhanireddy, Shireesha Corcorran, Maria A Open Forum Infect Dis Poster Abstracts BACKGROUND: Studies on infective endocarditis (IE) have relied on International Classification of Diseases (ICD) codes to identify cases but few have validated this method which may be prone to misclassification. Examination of clinical narrative data could offer greater accuracy and richness. METHODS: We evaluated two algorithms for IE identification from 7/1/2015 to 7/31/2019: (1) a standard query of ICD codes for IE (ICD-9: 424.9, 424.91, 424.99, 421.0, 421.1, 421.9, 112.81, 036.42 and ICD-10: I38, I39, I33, I33.9, B37.6 and A39.51) with or without procedure codes for echocardiogram (93303-93356) and (2) a key word, pattern-based text query of discharge summaries (DS) that selected on the term “endocarditis” in fields headed by “Discharge Diagnosis” or “Admission Diagnosis” or similar. Further coding extracted the nature and type of valve and the organism responsible for the IE if present in DS. All identified cases were chart reviewed using pre-specified criteria for true IE. Positive predictive value (PPV) was calculated as the total number of verified cases over the algorithm-selected cases. Sensitivity was the total number of algorithm-matched cases over a final list of 166 independently identified true IE cases from ID and Cardiology services. Specificity was defined using 119 pre-adjudicated non-cases minus the number of algorithm-matched cases over 119. RESULTS: The ICD-based query identified 612 individuals from July 2015 to July 2019 who had a hospital billing code for infective endocarditis; of these, 534 also had an echocardiogram. The DS query identified 387 cases. PPV for the DS query was 84.5% (95% confidence interval [CI] 80.6%, 87.8%) compared with 72.4% (95% CI 68.7%, 75.8%) for ICD only and 75.8% (95% CI 72.0%, 79.3%) for ICD + echo queries. Sensitivity was 75.9% for the DS query and 86.8-93.4% for the ICD queries. Specificity was high for all queries >94%. The DS query also yielded valve data (prosthetic, tricuspid, pulmonic, aortic or mitral) in 60% and microbiologic data in 73% of identified cases with an accuracy of 94% and 90% respectively when assessed by chart review. Table 1. Test Characteristics of Three Electronic Health Record Queries for Infective Endocarditis [Image: see text] CONCLUSION: Compared to traditional ICD-based queries, text-based queries of discharge summaries have the potential to improve precision of IE case ascertainment and extract key clinical variables. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2021-12-04 /pmc/articles/PMC8644334/ http://dx.doi.org/10.1093/ofid/ofab466.890 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Poster Abstracts
Nina Kim, H
Gupta, Ayushi
Lan, Kristine F
Stewart, Jenell C
Dhanireddy, Shireesha
Corcorran, Maria A
693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification
title 693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification
title_full 693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification
title_fullStr 693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification
title_full_unstemmed 693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification
title_short 693. Performance of ICD Code Versus Discharge Summary based Query for Endocarditis Cohort Identification
title_sort 693. performance of icd code versus discharge summary based query for endocarditis cohort identification
topic Poster Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8644334/
http://dx.doi.org/10.1093/ofid/ofab466.890
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