Early-stage COVID-19 pandemic observations on pulmonary embolism using nationwide multi-institutional data harvesting
Year: 2022
Type: article
Study design: Observational Study
Abstract: We introduce a multi-institutional data harvesting (MIDH) method for longitudinal observation of medical imaging utilization and reporting. By trackingbothlarge-scale utilizationandclinical 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 < 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.
Source: npj Digital Medicine
Authors: Axel Wismüller, Adora M. DSouza, Anas Z. Abidin, M. Ali Vosoughi, Christopher Gange, Isabel O. Cortopassi, Gracijela Bozovic, Alexander A. Bankier, Kiran Batra, Yosef Chodakiewitz, Yin Xi, Christopher T. Whitlow, Janardhana Ponnatapura, Gary J. Wendt, Eric P. Weinberg, Larry Stockmaster, David A. Shrier, Min Chul Shin, Roshan Modi, Hao Steven Lo, Seth Kligerman, Aws Hamid, Lewis D. Hahn, Glenn M. Garcia, Jonathan H. Chung, Talissa Altes, Suhny Abbara, Anna S. Bader
Institutions: University of Rochester Medicine, University of Rochester, Ludwig-Maximilians-Universität München, Yale University, Mayo Clinic, Mayo Clinic in Florida, University of Massachusetts Chan Medical School, The University of Texas Southwestern Medical Center, Cedars-Sinai Medical Center, S. Mark Taper Foundation, Wake Forest University Health Sciences, Wake Forest University, University of Wisconsin–Madison, Christiana Care Health System, Christiana Hospital, University of California San Diego, Emory University, The University of Texas Medical Branch at Galveston, University of Chicago, University of Missouri
Language: English
Subfield: Internal Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,290
OpenAlex ID: W4292332655