Natural Language Processing Identification of Symptoms in Emergency Department Visits in Patients Receiving Radiation

Abstract

Patients undergoing radiotherapy (RT) for cancer often require emergency department (ED) attention with possible hospitalization. Designing strategies to mitigate hospital admissions requires understanding the causal symptoms to tailor interventional strategies. Natural language processing (NLP) has previously been shown to accurately identify documented symptoms and may help characterize factors contributing to admission. The objective of this study was to use NLP to identify documented symptoms during ED visits and their association with subsequent hospital admission of patients undergoing RT. A de-identified, single tertiary-care institution cohort of patients who received radiation between 2013 and 2022 was identified from the electronic health record using International Classification of Disease (ICD) and Current Procedural Terminology (CPT) codes. We applied a previously validated clinical Text Analysis and Knowledge Extraction System (cTAKES)-based NLP pipeline to extract Common Terminology Criteria for Adverse Events (CTCAE) encoded symptoms from ED encounter clinical notes. Chi-squared testing was used to compare demographics, and logistic regression was used to identify symptoms associated with subsequent admission from ED visits. We identified 14,007 patients who received radiation, of whom 270 (1.9%) experienced 302 ED visits during their radiation course. 141 (46.7%) of ED visits resulted in an admission. Among patients with an ED encounter, there were no differences in admission rates based on primary language (p = 0.771), sex (p = 0.824), marital status (p = 0.753), race (p = 0.222), or age (p = 0.123). In admitted patients, the top 5 symptoms were pain (94.3%), nausea (92.1%), vomiting (73.7%), constipation (70.9%), and weakness (63.8%). In patients who did not require admission, the most common symptoms were pain (84.5%), nausea (67.1%), vomiting (47.2%), headache (36.6%), and weakness (35.4%). The 10 symptoms most associated with admission from the ED based on logistic regression were malaise (OR 21.7, [95% CI 10.1 - 51.0]), lethargy (19.1, [8.5 - 51.3]), flushing (15.7, [8.6 - 30.4]), agitation (12.4, [3.5 - 78.7]), somnolence (10.3, [4.7 - 25.9]), fall (8.5, [3.7 - 23.2]), fatigue (7.8, [4.6 - 13.4]), constipation (6.9, [4.2 - 11.6]), nausea (5.8, [3.0 - 12.2]), and swelling (5.4, [3.3 - 9.1]). Admitted and non-admitted ED patients with cancer seen in the ED during radiotherapy are documented to experience similar symptoms, but certain symptoms are associated with a higher risk of hospital admission. NLP may offer a mechanism for early, automated identification to facilitate supportive interventions for patients at high risk for admission during radiotherapy.