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 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).
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.
Of 251,476 safety reports, 3189 (1.3%) were confirmed allergic reactions in 2923 patients (mean age 52 years, standard deviation [SD] 17 years; 62% female). Allergic events increased over time (Fig. 1). Allergic reactions were most common in imaging (n = 1674, 53%), infusion (n = 838, 26%), and inpatient (n = 321, 10%) settings, but also occurred in procedural (n = 121, 4%) and ambulatory (n = 122, 4%) settings. Most reactions resulted in temporary or minor harm (n = 2639, 83%); fifteen (< 1%) reactions resulted in major harm or death.
Culprit allergens included diagnostic contrast agents (n = 1694, 53%), medications (n = 1154, 36%), blood products (n = 118, 4%), other healthcare products (n = 63, 2%) including latex (n = 21, < 1%), and foods (n = 18, < 1%) (Fig. 2). Chemotherapeutics (n = 385, 12%), monoclonal antibodies (n = 224, 7%), and antibiotics (n = 178, 5%) such as beta-lactams (n = 66, 2%) and vancomycin (n = 52, 1%) were the most common medication culprits. Culprit agents could not be identified for 164 (5%) cases.
We identified 3189 allergic events from over 250,000 voluntarily reported safety events across two large AMCs. Most events occurred in imaging; over a quarter occurred in infusion settings. Over half were due to contrast media, and over one-third were due to medications, commonly chemotherapeutics, monoclonal antibodies, and antimicrobial agents; blood products, foods, and other healthcare products were also identified as culprits. Although over 80% of allergic reactions caused only temporary or minor harm, fifteen events resulted in major harm or death.
Allergic reaction treatment requires prompt identification; future allergic reaction prevention requires appropriate documentation. Unfortunately, these critical safety steps happen infrequently.2, 7 Our study suggests the strongest potential for allergy safety initiatives that target radiology and infusion settings where contrast media, chemotherapeutics, and monoclonal antibodies are administered.
Because allergic events are rare and caused by a variety of culprits, healthcare systems do not explicitly track them. This machine learning model enabled allergic reaction detection from large amounts of voluntarily reported data; however, to facilitate automated downstream quality and safety improvement initiatives, the model would need to identify the culprit and reaction and demonstrate a similarly strong performance with other types of free-text clinical data (e.g., notes).
Study limitations include the voluntary nature of safety reports, including reporting culture variations between hospitals and over time. However, we reduced the impact of reporting culture by studying two large hospitals with different reporting practices. Minor reactions not warranting a safety report may have been missed. Finally, results from two AMCs in one US city may not be generalizable.
We identified and characterized allergic reactions in academic healthcare settings from a large volume of voluntarily reported safety data; we elucidate the variety of potential healthcare allergens and show that contrast media, chemotherapeutics, and biologics are the highest-risk culprits. These epidemiologic data can guide hospitals in concentrating allergy safety efforts.
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The authors thank Indira S. Padubidri, MBA, Andrea Shellman, the Edward P. Lawrence Center for Quality and Safety at Massachusetts General Hospital, and the Department of Quality and Safety at Brigham and Women’s Hospital for assistance in data acquisition. We thank Elizabeth Mort, MD, MPH, for review of the final manuscript and Liqing Wang, PhD, for assistance with development of the machine learning model.
This work was supported by CRICO, the risk management foundation.Availability of Data
The datasets generated during and/or analyzed during the current study are not publicly available as they contain peer-protected data and cannot be shared per institutional policy.
Conflict of Interest
The authors declare that they do not have a conflict of interest.
This study was reviewed and approved by the Partners HealthCare System Institutional Review Board.
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The content is solely the responsibility of the authors and does not necessarily represent the official views of CRICO.
The source code can be accessed at https://github.com/jiesutd/AllergicEvent. Analysis code can be provided upon request.
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Phadke, N.A., Zhou, L., Mancini, C.M. et al. Allergic Reactions in Two Academic Medical Centers. J GEN INTERN MED 36, 1814–1817 (2021). https://doi.org/10.1007/s11606-020-06190-6