Allergic Reactions Captured by Voluntary Reporting

A Novel Informatics Tool to Detect Periprocedural Antibiotic Allergy Adverse Events for Near Real-time Surveillance to Support Audit and Feedback

Importance

Standardized processes for identifying when allergic-type reactions occur and linking reactions to drug exposures are limited.

Objective

To develop an informatics tool to improve detection of antibiotic allergic-type events.

Design, Setting, and Participants

This retrospective cohort study was conducted from October 1, 2015, to September 30, 2019, with data analyzed between July 1, 2021, and January 31, 2022. The study was conducted across Veteran Affairs hospitals among patients who underwent cardiovascular implantable electronic device (CIED) procedures and received periprocedural antibiotic prophylaxis. The cohort was split into training and test cohorts, and cases were manually reviewed to determine presence of allergic-type reaction and its severity. Variables potentially indicative of allergic-type reactions were selected a priori and included allergies entered in the Veteran Affair's Allergy Reaction Tracking (ART) system (either historical [reported] or observed), allergy diagnosis codes, medications administered to treat allergic reactions, and text searches of clinical notes for keywords and phrases indicative of a potential allergic-type reaction. A model to detect allergic-type reaction events was iteratively developed on the training cohort and then applied to the test cohort. Algorithm test characteristics were assessed.

Exposure

Preprocedural and postprocedural prophylactic antibiotic administration.

Main Outcomes and Measures

Antibiotic allergic-type reactions.

Results

The cohort of 36 344 patients included 34 703 CIED procedures with antibiotic exposures (mean [SD] age, 72 [10] years; 34 008 [98%] male patients); median duration of postprocedural prophylaxis was 4 days (IQR, 2-7 days; maximum, 45 days). The final algorithm included 7 variables: entries in the Veteran Affair's hospitals ART, either historic (odds ratio [OR], 42.37; 95% CI, 11.33-158.43) or observed (OR, 175.10; 95% CI, 44.84-683.76); PheCodes for "symptoms affecting skin" (OR, 8.49; 95% CI, 1.90-37.82), "urticaria" (OR, 7.01; 95% CI, 1.76-27.89), and "allergy or adverse event to an antibiotic" (OR, 11.84, 95% CI, 2.88-48.69); keyword detection in clinical notes (OR, 3.21; 95% CI, 1.27-8.08); and antihistamine administration alone or in combination (OR, 6.51; 95% CI, 1.90-22.30). In the final model, antibiotic allergic-type reactions were identified with an estimated probability of 30% or more; positive predictive value was 61% (95% CI, 45%-76%); and sensitivity was 87% (95% CI, 70%-96%).

Conclusions and Relevance

In this retrospective cohort study of patients receiving periprocedural antibiotic prophylaxis, an algorithm with a high sensitivity to detect incident antibiotic allergic-type reactions that can be used to provide clinician feedback about antibiotic harms from unnecessarily prolonged antibiotic exposures was developed.

Allergy Safety Events in Health Care: Development and Application of a Classification Schema Based on Retrospective Review

BACKGROUND

Allergy safety requires understanding the operational processes that expose patients to their known allergens, including how and when such processes fail.

OBJECTIVE

To improve health care safety for patients with allergies, we developed and assessed an allergy safety event classification schema to describe failures resulting in allergy-related safety events.

METHODS

Using keyword searches followed by expert manual review of 299,031 voluntarily-filed safety event reports at 2 large academic medical centers, we identified and classified allergy-related safety events from 5 years of safety reports. We used driver diagrams to elucidate root causes for commonly observed allergy safety events in health care settings.

RESULTS

From 299,031 safety reports, 1922 (0.6%) were extracted with keywords and 744 (0.2%) were manually confirmed as allergy-related safety events. Safety failures were due to incomplete/inaccurate electronic health record documentation (n = 375, 50.4%), human factors (n = 175, 23.5%), allergy alert limitation and/or malfunction (n = 127, 17.1%), data exchange and interoperability failures (n = 92, 12.4%), and electronic health record system default options (n = 30, 4.0%). Safety failures resulted in known allergen exposures to drugs (n = 537), including heparin (n = 27) and topical anesthetics such as lidocaine (n = 8); latex (n = 114); food allergens (n = 73); and adhesive (n = 23).

CONCLUSIONS

We identified 744 allergy-related safety events to inform a novel safety failure classification schema as an important step toward a safer health care environment for patients with allergies. Improved systems are required to address safety issues with certain food and drug allergens.

Improving Allergy Documentation: A Retrospective Electronic Health Record System-Wide Patient Safety Initiative

OBJECTIVES

Documentation of allergies in a coded, non-free-text format in the electronic health record (EHR) triggers clinical decision support to prevent adverse events. Health system-wide patient safety initiatives to improve EHR allergy documentation by specifically decreasing free-text allergy entries have not been reported. The goal of this initiative was to systematically reduce free-text allergen entries in the EHR allergy module.

METHODS

We assessed free-text allergy entries in a commercial EHR used at a multihospital integrated health care system in the greater Boston area. Using both manual and automated methods, a multidisciplinary consensus group prioritized high-risk and frequently used free-text allergens for conversion to coded entries, added new allergen entries, and deleted duplicate allergen entries. Environmental allergies were moved to the patient problem list.

RESULTS

We identified 242,330 free-text entries, which included a variety of environmental allergies (42%), food allergies (18%), contrast media allergies (13%), "no known allergy" (12%), drug allergies (2%), and "no contrast allergy" (2%). Most free-text entries were entered by medical assistants in ambulatory settings (34%) and registered nurses in perioperative settings (20%). We remediated a total of 52,206 free-text entries with automated methods and 79,578 free-text entries with manual methods.

CONCLUSIONS

Through this multidisciplinary intervention, we identified and remediated 131,784 free-text entries in our EHR to improve clinical decision support and patient safety. Additional strategies are required to completely eliminate free-text allergy entry, and establish systematic, consistent, and safe guidelines for documenting allergies.

Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals

IMPORTANCE

Although critical to patient safety, health care-related allergic reactions are challenging to identify and monitor.

OBJECTIVE

To develop a deep learning model to identify allergic reactions in the free-text narrative of hospital safety reports and evaluate its generalizability, efficiency, productivity, and interpretability.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed hospital safety reports filed between May 2004 and January 2019 at Brigham and Women's Hospital and between April 2006 and June 2018 at Massachusetts General Hospital in Boston. Training and validating a deep learning model involved extracting safety reports using 101 expert-curated keywords from Massachusetts General Hospital (data set I). The model was then evaluated on 3 data sets: reports without keywords (data set II), reports from a different time frame (data set III), and reports from a different hospital (Brigham and Women's Hospital; data set IV). Statistical analyses were performed between March 1, 2019, and July 18, 2020.

MAIN OUTCOMES AND MEASURES

The area under the receiver operating characteristic curve and area under the precision-recall curve were used on data set I. The precision at top-k was used on data sets II to IV.

RESULTS

A total of 299 028 safety reports with 172 854 patients were included. Of these patients, 86 544 were women (50.1%) and the median (interquartile range [IQR]) age was 59.7 (43.8-71.6) years. The deep learning model achieved an area under the receiver operating characteristic curve of 0.979 (95% CI, 0.973-0.985) and an area under the precision-recall curve of 0.809 (95% CI, 0.773-0.845). The model achieved precisions at the top 100 model-identified cases of 0.930 in data set II, 0.960 in data set III, and 0.990 in data set IV. Compared with the keyword-search approach, the deep learning model reduced the number of cases for manual review by 63.8% and identified 24.2% more cases of confirmed allergic reactions. The model highlighted important words (eg, rash, hives, and Benadryl) in prediction and extended the list of expert-curated keywords through an attention layer.

CONCLUSIONS AND RELEVANCE

This study showed that a deep learning model can accurately and efficiently identify allergic reactions using free-text narratives written by a variety of health care professionals. This model could be used to improve allergy care, potentially enabling real-time event surveillance and guidance for medical errors and system improvement.

Conducting Safety Research Safely: A Policy-Based Approach for Conducting Research with Peer Review Protected Material

A multidisciplinary team developed a policy-based approach that provides guidance for using peer review protected information for safety research while maintaining peer review privilege. The approach includes project approval by an ad hoc review committee, signed confidentiality agreements by investigators and study staff, early removal of case identification numbers, standards for maintaining data security, and publication of aggregate data without data set sharing. By describing this procedure and embedding into an institutional policy on Data for Performance Improvement, the team encourages other institutions to develop similar policies consistent with their state regulations.

Addressing the epidemic of antibiotic "allergy" over-diagnosis

OBJECTIVE

An epidemic of antibiotic allergy is occurring.

DATA SOURCES

Articles published since 2008.

STUDY SELECTIONS

Articles on antibiotic allergy and stewardship.

RESULTS

A number of overlapping factors contribute. The most important factor is antibiotic overuse. Antibiotics are commonly used in situations in which no antibiotics are indicated. Thirty percent to 50% of ambulatory antibiotic use may be inappropriate. The duration of indicated antibiotic use is often excessive, which leads to more side effects. All antibiotic use can result in adverse reactions, and a fraction of these will be dutifully recorded as an allergy in the electronic health record (EHR). Most EHRs are not well structured to accurately convey information on expected side effects that have occurred, metabolic or other contraindications, dose-related or situational toxicities, personal preferences, clinically significant immunologically mediated hypersensitivity, and other reasons a particular patient may not want or should not be given a specific drug or type of drug in the future. As populations age, their accumulated baggage of reported antibiotic allergies increase. Suspected antibiotic allergy is rarely confirmed with appropriate testing or rechallenge. Patients then receive suboptimal antibiotic therapy and experience more side effects, treatment failures, and serious antibiotic-resistant infections. Reporting an antibiotic allergy in the EHR is nominally done to improve patient safety, but unfortunately, this is often not the actual result.

CONCLUSION

Audit and feedback, to help ensure adherence to Choosing Wisely recommendations and good antibiotic stewardship practices, can help reduce inappropriate antibiotic use. Restructuring EHRs to facilitate correct drug intolerance reporting, along with active antibiotic allergy delabeling programs, can help stem this epidemic.

Cephalosporin Allergy: Current Understanding and Future Challenges

Cephalosporins are commonly used antibiotics both in hospitalized patients and in outpatients. Hypersensitivity reactions to cephalosporins are becoming increasingly common with a wide range of immunopathologic mechanisms. Cephalosporins are one of the leading causes for perioperative anaphylaxis and severe cutaneous adverse reactions. Patients allergic to cephalosporins tend to tolerate cephalosporins with disparate R1 side chains but may react to other beta-lactams with common R1 side chains. Skin testing for cephalosporins has not been well validated but appears to have a good negative predictive value for cephalosporins with disparate R1 side chains. In vitro tests including basophil activation tests have lower sensitivity when compared with skin testing. Rapid drug desensitization procedures are safe and effective and have been used successfully for immediate and some nonimmediate cephalosporin reactions. Many gaps in knowledge still exist regarding cephalosporin hypersensitivity.