Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)

Moa Jung; Hae Yeon Park; Geun Young Park; Jong In Lee; Youngkook Kim; Yeo Hyung Kim; Seong Hoon Lim; Yeun Jie Yoo; Sun Im

doi:10.3390/antibiotics12040740

Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)

Moa Jung, Hae Yeon Park, Geun Young Park, Jong In Lee, Youngkook Kim, Yeo Hyung Kim, Seong Hoon Lim, Yeun Jie Yoo, Sun Im

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

This study analyzed a digitized database of electronic medical records (EMRs) to identify risk factors for post-stroke infections. The sample included 41,236 patients hospitalized with a first stroke diagnosis (ICD-10 codes I60, I61, I63, and I64) between January 2011 and December 2020. Logistic regression analysis was performed to examine the effect of clinical variables on post-stroke infection. Multivariable analysis revealed that post-stroke infection was associated with the male sex (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.49–2.15), brain surgery (OR: 7.89; 95% CI: 6.27–9.92), mechanical ventilation (OR: 18.26; 95% CI: 8.49–44.32), enteral tube feeding (OR: 3.65; 95% CI: 2.98–4.47), and functional activity level (modified Barthel index: OR: 0.98; 95% CI: 0.98–0.98). In addition, exposure to steroids (OR: 2.22; 95% CI: 1.60–3.06) and acid-suppressant drugs (OR: 1.44; 95% CI: 1.15–1.81) increased the risk of infection. On the basis of the findings from this multicenter study, it is crucial to carefully evaluate the balance between the potential benefits of acid-suppressant drugs or corticosteroids and the increased risk of infection in patients at high risk for post-stroke infection.

Original language	English
Article number	740
Journal	Antibiotics
Volume	12
Issue number	4
DOIs	https://doi.org/10.3390/antibiotics12040740
State	Published - Apr 2023

Bibliographical note

Funding Information:
This work was supported by the Institute of Clinical Medicine Research of Bucheon St. Mary’s Hospital, Research Fund (2022), grant number BCMC22BD06; and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT), grant number 2020R1F1A1065814.

Funding Information:
Statistical consultation was supported by the Department of Mathematics, The Catholic University of Korea.

Publisher Copyright:
© 2023 by the authors.

Keywords

big data
electronic health record
functional level (modified Barthel)
infection
pneumonia
risk factors
steroids
stroke
urinary tract infection

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/antibiotics12040740

Cite this

@article{7181322fcad04112a7c4878014dc671a,

title = "Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)",

abstract = "This study analyzed a digitized database of electronic medical records (EMRs) to identify risk factors for post-stroke infections. The sample included 41,236 patients hospitalized with a first stroke diagnosis (ICD-10 codes I60, I61, I63, and I64) between January 2011 and December 2020. Logistic regression analysis was performed to examine the effect of clinical variables on post-stroke infection. Multivariable analysis revealed that post-stroke infection was associated with the male sex (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.49–2.15), brain surgery (OR: 7.89; 95% CI: 6.27–9.92), mechanical ventilation (OR: 18.26; 95% CI: 8.49–44.32), enteral tube feeding (OR: 3.65; 95% CI: 2.98–4.47), and functional activity level (modified Barthel index: OR: 0.98; 95% CI: 0.98–0.98). In addition, exposure to steroids (OR: 2.22; 95% CI: 1.60–3.06) and acid-suppressant drugs (OR: 1.44; 95% CI: 1.15–1.81) increased the risk of infection. On the basis of the findings from this multicenter study, it is crucial to carefully evaluate the balance between the potential benefits of acid-suppressant drugs or corticosteroids and the increased risk of infection in patients at high risk for post-stroke infection.",

keywords = "big data, electronic health record, functional level (modified Barthel), infection, pneumonia, risk factors, steroids, stroke, urinary tract infection",

author = "Moa Jung and Park, {Hae Yeon} and Park, {Geun Young} and Lee, {Jong In} and Youngkook Kim and Kim, {Yeo Hyung} and Lim, {Seong Hoon} and Yoo, {Yeun Jie} and Sun Im",

note = "Funding Information: This work was supported by the Institute of Clinical Medicine Research of Bucheon St. Mary{\textquoteright}s Hospital, Research Fund (2022), grant number BCMC22BD06; and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT), grant number 2020R1F1A1065814. Funding Information: Statistical consultation was supported by the Department of Mathematics, The Catholic University of Korea. Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = apr,

doi = "10.3390/antibiotics12040740",

language = "English",

volume = "12",

journal = "Antibiotics",

issn = "2079-6382",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "4",

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T1 - Post-Stroke Infections

T2 - Insights from Big Data Using Clinical Data Warehouse (CDW)

AU - Jung, Moa

AU - Park, Hae Yeon

AU - Park, Geun Young

AU - Lee, Jong In

AU - Kim, Youngkook

AU - Kim, Yeo Hyung

AU - Lim, Seong Hoon

AU - Yoo, Yeun Jie

AU - Im, Sun

N1 - Funding Information: This work was supported by the Institute of Clinical Medicine Research of Bucheon St. Mary’s Hospital, Research Fund (2022), grant number BCMC22BD06; and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT), grant number 2020R1F1A1065814. Funding Information: Statistical consultation was supported by the Department of Mathematics, The Catholic University of Korea. Publisher Copyright: © 2023 by the authors.

PY - 2023/4

Y1 - 2023/4

N2 - This study analyzed a digitized database of electronic medical records (EMRs) to identify risk factors for post-stroke infections. The sample included 41,236 patients hospitalized with a first stroke diagnosis (ICD-10 codes I60, I61, I63, and I64) between January 2011 and December 2020. Logistic regression analysis was performed to examine the effect of clinical variables on post-stroke infection. Multivariable analysis revealed that post-stroke infection was associated with the male sex (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.49–2.15), brain surgery (OR: 7.89; 95% CI: 6.27–9.92), mechanical ventilation (OR: 18.26; 95% CI: 8.49–44.32), enteral tube feeding (OR: 3.65; 95% CI: 2.98–4.47), and functional activity level (modified Barthel index: OR: 0.98; 95% CI: 0.98–0.98). In addition, exposure to steroids (OR: 2.22; 95% CI: 1.60–3.06) and acid-suppressant drugs (OR: 1.44; 95% CI: 1.15–1.81) increased the risk of infection. On the basis of the findings from this multicenter study, it is crucial to carefully evaluate the balance between the potential benefits of acid-suppressant drugs or corticosteroids and the increased risk of infection in patients at high risk for post-stroke infection.

AB - This study analyzed a digitized database of electronic medical records (EMRs) to identify risk factors for post-stroke infections. The sample included 41,236 patients hospitalized with a first stroke diagnosis (ICD-10 codes I60, I61, I63, and I64) between January 2011 and December 2020. Logistic regression analysis was performed to examine the effect of clinical variables on post-stroke infection. Multivariable analysis revealed that post-stroke infection was associated with the male sex (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.49–2.15), brain surgery (OR: 7.89; 95% CI: 6.27–9.92), mechanical ventilation (OR: 18.26; 95% CI: 8.49–44.32), enteral tube feeding (OR: 3.65; 95% CI: 2.98–4.47), and functional activity level (modified Barthel index: OR: 0.98; 95% CI: 0.98–0.98). In addition, exposure to steroids (OR: 2.22; 95% CI: 1.60–3.06) and acid-suppressant drugs (OR: 1.44; 95% CI: 1.15–1.81) increased the risk of infection. On the basis of the findings from this multicenter study, it is crucial to carefully evaluate the balance between the potential benefits of acid-suppressant drugs or corticosteroids and the increased risk of infection in patients at high risk for post-stroke infection.

KW - big data

KW - electronic health record

KW - functional level (modified Barthel)

KW - infection

KW - pneumonia

KW - risk factors

KW - steroids

KW - stroke

KW - urinary tract infection

UR - http://www.scopus.com/inward/record.url?scp=85153716737&partnerID=8YFLogxK

U2 - 10.3390/antibiotics12040740

DO - 10.3390/antibiotics12040740

M3 - Article

AN - SCOPUS:85153716737

SN - 2079-6382

VL - 12

JO - Antibiotics

JF - Antibiotics

IS - 4

M1 - 740

ER -

Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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Catholic University of Korea Researcher Publishes New Studies and Findings in the Area of Stroke [Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)]

Cite this

Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Press/Media

Catholic University of Korea Researcher Publishes New Studies and Findings in the Area of Stroke [Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)]

Cite this