ABSTRACT

BACKGROUND: Venous thromboembolism (VTE) is a preventable, common vascular disease that has been estimated to affect up to 900,000 people per year. It has been associated with risk factors such as recent surgery, cancer, and hospitalization. VTE surveillance for patient management and safety can be improved via natural language processing (NLP). NLP tools have the ability to access electronic medical records, identify patients that meet the VTE case definition, and subsequently enter the relevant information into a database for hospital review.

OBJECTIVE: We aimed to evaluate the performance of a VTE identification model of IDEAL-X (Information and Data Extraction Using Adaptive Learning; Emory University)-an NLP tool-in automatically classifying cases of VTE by "reading" unstructured text from diagnostic imaging records collected from 2012 to 2014.

METHODS: After accessing imaging records from pilot surveillance systems for VTE from Duke University and the University of Oklahoma Health Sciences Center (OUHSC), we used a VTE identification model of IDEAL-X to classify cases of VTE that had previously been manually classified. Experts reviewed the technicians' comments in each record to determine if a VTE event occurred. The performance measures calculated (with 95% CIs) were accuracy, sensitivity, specificity, and positive and negative predictive values. Chi-square tests of homogeneity were conducted to evaluate differences in performance measures by site, using a significance level of .05.

RESULTS: The VTE model of IDEAL-X "read" 1591 records from Duke University and 1487 records from the OUHSC, for a total of 3078 records. The combined performance measures were 93.7% accuracy (95% CI 93.7%-93.8%), 96.3% sensitivity (95% CI 96.2%-96.4%), 92% specificity (95% CI 91.9%-92%), an 89.1% positive predictive value (95% CI 89%-89.2%), and a 97.3% negative predictive value (95% CI 97.3%-97.4%). The sensitivity was higher at Duke University (97.9%, 95% CI 97.8%-98%) than at the OUHSC (93.3%, 95% CI 93.1%-93.4%; P<.001),P

CONCLUSIONS: The VTE model of IDEAL-X accurately classified cases of VTE from the pilot surveillance systems of two separate health systems in Durham, North Carolina, and Oklahoma City, Oklahoma. NLP is a promising tool for the design and implementation of an automated, cost-effective national surveillance system for VTE. Conducting public health surveillance at a national scale is important for measuring disease burden and the impact of prevention measures. We recommend additional studies to identify how integrating IDEAL-X in a medical record system could further automate the surveillance process.

PMID:37206160 | PMC:PMC10193259 | DOI:10.2196/36877

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J Vasc Interv Radiol. 2023 May 17:S1051-0443(23)00355-X. doi: 10.1016/j.jvir.2023.05.023. Online ahead of print.

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PMID:37207811 | DOI:10.1016/j.jvir.2023.05.023

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PubMed articles on: Cardio-Oncology

The Incident Ocular Diseases Related to Chemotherapy in Cancer Patients are Associated with Increasing Risk of Incident Stroke

Acta Cardiol Sin. 2023 May;39(3):435-448. doi: 10.6515/ACS.202305_39(3).20221005A.