Early-stage COVID-19 pandemic observations on pulmonary embolism using nationwide multi-institutional data harvesting

We introduce a multi-institutional data harvesting (MIDH) method for longitudinal observation of medical imaging utilization and reporting. By tracking both large-scale utilization and clinical imaging results data, the MIDH approach is targeted at measuring surrogates for important disease-related...

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Published in:NPJ digital medicine 2022-08, Vol.5 (1), p.120-120, Article 120
Main Authors: Wismüller, Axel, DSouza, Adora M., Abidin, Anas Z., Ali Vosoughi, M., Gange, Christopher, Cortopassi, Isabel O., Bozovic, Gracijela, Bankier, Alexander A., Batra, Kiran, Chodakiewitz, Yosef, Xi, Yin, Whitlow, Christopher T., Ponnatapura, Janardhana, Wendt, Gary J., Weinberg, Eric P., Stockmaster, Larry, Shrier, David A., Shin, Min Chul, Modi, Roshan, Lo, Hao Steven, Kligerman, Seth, Hamid, Aws, Hahn, Lewis D., Garcia, Glenn M., Chung, Jonathan H., Altes, Talissa, Abbara, Suhny, Bader, Anna S.
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Language:English
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692/699/255/2514
692/700/139
Biomedicine
Biotechnology
Coronaviruses
COVID-19
Digital technology
Medical imaging
Medicine
Medicine & Public Health
Pandemics
Pulmonary embolisms
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cited_by cdi_FETCH-LOGICAL-c540t-203064ce11e43811e9c17641733948e04ec01053aa72e2ee90896a166f74b26b3
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container_end_page 120
container_issue 1
container_start_page 120
container_title NPJ digital medicine
container_volume 5
creator Wismüller, Axel
DSouza, Adora M.
Abidin, Anas Z.
Ali Vosoughi, M.
Gange, Christopher
Cortopassi, Isabel O.
Bozovic, Gracijela
Bankier, Alexander A.
Batra, Kiran
Chodakiewitz, Yosef
Xi, Yin
Whitlow, Christopher T.
Ponnatapura, Janardhana
Wendt, Gary J.
Weinberg, Eric P.
Stockmaster, Larry
Shrier, David A.
Shin, Min Chul
Modi, Roshan
Lo, Hao Steven
Kligerman, Seth
Hamid, Aws
Hahn, Lewis D.
Garcia, Glenn M.
Chung, Jonathan H.
Altes, Talissa
Abbara, Suhny
Bader, Anna S.
description We introduce a multi-institutional data harvesting (MIDH) method for longitudinal observation of medical imaging utilization and reporting. By tracking both large-scale utilization and clinical imaging results data, the MIDH approach is targeted at measuring surrogates for important disease-related observational quantities over time. To quantitatively investigate its clinical applicability, we performed a retrospective multi-institutional study encompassing 13 healthcare systems throughout the United States before and after the 2020 COVID-19 pandemic. Using repurposed software infrastructure of a commercial AI-based image analysis service, we harvested data on medical imaging service requests and radiology reports for 40,037 computed tomography pulmonary angiograms (CTPA) to evaluate for pulmonary embolism (PE). Specifically, we compared two 70-day observational periods, namely (i) a pre-pandemic control period from 11/25/2019 through 2/2/2020, and (ii) a period during the early COVID-19 pandemic from 3/8/2020 through 5/16/2020. Natural language processing (NLP) on final radiology reports served as the ground truth for identifying positive PE cases, where we found an NLP accuracy of 98% for classifying radiology reports as positive or negative for PE based on a manual review of 2,400 radiology reports. Fewer CTPA exams were performed during the early COVID-19 pandemic than during the pre-pandemic period (9806 vs. 12,106). However, the PE positivity rate was significantly higher (11.6 vs. 9.9%, p  
doi_str_mv 10.1038/s41746-022-00653-2
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Natural language processing (NLP) on final radiology reports served as the ground truth for identifying positive PE cases, where we found an NLP accuracy of 98% for classifying radiology reports as positive or negative for PE based on a manual review of 2,400 radiology reports. Fewer CTPA exams were performed during the early COVID-19 pandemic than during the pre-pandemic period (9806 vs. 12,106). However, the PE positivity rate was significantly higher (11.6 vs. 9.9%, p  &lt; 10 −4 ) with an excess of 92 PE cases during the early COVID-19 outbreak, i.e., ~1.3 daily PE cases more than statistically expected. 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Med</stitle><addtitle>NPJ Digit Med</addtitle><date>2022-08-19</date><risdate>2022</risdate><volume>5</volume><issue>1</issue><spage>120</spage><epage>120</epage><pages>120-120</pages><artnum>120</artnum><issn>2398-6352</issn><eissn>2398-6352</eissn><abstract>We introduce a multi-institutional data harvesting (MIDH) method for longitudinal observation of medical imaging utilization and reporting. By tracking both large-scale utilization and clinical imaging results data, the MIDH approach is targeted at measuring surrogates for important disease-related observational quantities over time. To quantitatively investigate its clinical applicability, we performed a retrospective multi-institutional study encompassing 13 healthcare systems throughout the United States before and after the 2020 COVID-19 pandemic. Using repurposed software infrastructure of a commercial AI-based image analysis service, we harvested data on medical imaging service requests and radiology reports for 40,037 computed tomography pulmonary angiograms (CTPA) to evaluate for pulmonary embolism (PE). Specifically, we compared two 70-day observational periods, namely (i) a pre-pandemic control period from 11/25/2019 through 2/2/2020, and (ii) a period during the early COVID-19 pandemic from 3/8/2020 through 5/16/2020. Natural language processing (NLP) on final radiology reports served as the ground truth for identifying positive PE cases, where we found an NLP accuracy of 98% for classifying radiology reports as positive or negative for PE based on a manual review of 2,400 radiology reports. Fewer CTPA exams were performed during the early COVID-19 pandemic than during the pre-pandemic period (9806 vs. 12,106). However, the PE positivity rate was significantly higher (11.6 vs. 9.9%, p  &lt; 10 −4 ) with an excess of 92 PE cases during the early COVID-19 outbreak, i.e., ~1.3 daily PE cases more than statistically expected. Our results suggest that MIDH can contribute value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>35986059</pmid><doi>10.1038/s41746-022-00653-2</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-3596-0787</orcidid><orcidid>https://orcid.org/0000-0003-2552-7362</orcidid><orcidid>https://orcid.org/0000-0002-3807-0261</orcidid><orcidid>https://orcid.org/0000-0002-7459-0854</orcidid><orcidid>https://orcid.org/0000-0002-1224-4665</orcidid><orcidid>https://orcid.org/0000-0002-0029-8906</orcidid><orcidid>https://orcid.org/0000-0002-2790-8946</orcidid><oa>free_for_read</oa></addata></record>
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identifier ISSN: 2398-6352
ispartof NPJ digital medicine, 2022-08, Vol.5 (1), p.120-120, Article 120
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2398-6352
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_d7f681c2f5e449f48e1a91279ad68a22
source Publicly Available Content Database; PubMed Central; Alma/SFX Local Collection; Coronavirus Research Database; Springer Nature - nature.com Journals - Fully Open Access
subjects 692/699/255/2514
692/700/139
Biomedicine
Biotechnology
Coronaviruses
COVID-19
Digital technology
Medical imaging
Medicine
Medicine & Public Health
Pandemics
Pulmonary embolisms
title Early-stage COVID-19 pandemic observations on pulmonary embolism using nationwide multi-institutional data harvesting
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