Allergic Reactions in Two Academic Medical Centers
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J Gen Intern Med DOI: 10.1007/s11606-020-06190-6 © Society of General Internal Medicine 2020
methods proved too time- and labor-intensive for large-scale allergy safety monitoring, we used a machine learning model to describe the epidemiology of allergic reactions at two academic medical centers (AMCs).
INTRODUCTION
Although healthcare-related allergic reactions to foods, drugs, and other culprits are increasing, at least a fifth of allergies are inaccurately documented or interpreted.1 Clinicians do not routinely recognize or appropriately treat allergic reactions; even life-threatening anaphylaxis is often treated with antihistamines or corticosteroids rather than recommended intramuscular epinephrine.2 Improving allergic reaction recognition, management, and documentation requires improved detection and system-level targeting. We previously identified healthcare system allergic reactions using specialist-derived keyword search on safety reporting data3 and natural language processing on clinical notes4, 5, each followed by manual review. However, as such
METHODS
Using a machine learning model trained on the free-text of 9107 manually labeled safety reports (rL, Toronto, Canada) (average area under the receiver operating characteristic curve 0.979, 95% confidence interval 0.973–0.985),6 we sorted voluntarily filed reports from July 1, 2008, to June 30, 2018, at two United States AMCs by their model-predicted probability of describing an allergic reaction in descending order. Reports were manually reviewed until the last 200 reports contained just one allergic event (i.e., false negative rate of 0.5%). Data were obtained from rL databases; culprit allergens were manually reviewed and grouped. Descriptive statistics were reported.
Figure 1 A decade of allergic reactions in two United States (US) academic medical centers. There were 290 (SD 75, range 141–385) mean allergy events identified per year across both hospitals. Allergic reactions increased over a decade of measurement at a rate of 11.8 reactions per year (p < 0.05).
Received March 19, 2020 Revised June 12, 2020 Accepted August 27, 2020
Phadke et al.: A Machine Learning Model to Describe Allergic Reactions
JGIM
Figure 2 Allergic reaction culprits. This Pareto chart demonstrates the most common allergen culprits: diagnostic contrast agents (n = 1694), chemotherapeutics (n = 385), and monoclonal antibodies (n = 224). Some culprit agents (n = 164) were unknown. “Diagnostic contrast agent” (superscript “a”) includes computed tomography contrast (n = 1081), magnetic resonance imaging contrast (n = 367), unknown contrast (n = 241), ultrasound contrast (n = 2), fluorescein (n = 1), I-123 NaI capsule (n = 1), and nuclear medicine contrast (n = 1). “Chemotherapeutics” (superscript “b”) includes paclitaxel (n = 117), docetaxel (n = 81), oxaliplatin (n = 64), carboplatin (n = 58), unknown chemotherapy (n = 30), cisplatin (n = 9), doxorubicin (n = 7), etoposide (n = 5), gemcitabine (n = 3), STA-9090 (n = 2), irinotecan (n = 2), cytosine arabinoside (n = 1),
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