Development of a machine learning algorithm based on administrative claims data for identification of ED anaphylaxis patient visits.

BACKGROUND

Epidemiologic studies of anaphylaxis commonly rely on () codes to identify anaphylaxis cases, which may lead to suboptimal epidemiologic classification.

OBJECTIVE

We sought to develop and assess the accuracy of a machine learning algorithm using codes and other administrative data compared with code-only algorithms to identify emergency department (ED) anaphylaxis visits.

METHODS

We conducted a retrospective review of ED visits from January 2013 to September 2017. Potential ED anaphylaxis visits were identified using 3 methods: anaphylaxis diagnostic codes (method 1), symptom-based codes with or without a code indicating an allergic trigger (method 2), and codes indicating a potential allergic reaction only (method 3). A machine learning algorithm was developed from administrative data, and test characteristics were compared with code-only algorithms.

RESULTS

A total of 699 of 2191 (31.9%) potential ED anaphylaxis visits were classified as anaphylaxis. The sensitivity and specificity of method 1 were 49.1% and 87.5%, respectively. Method 1 used in combination with method 2 resulted in a sensitivity of 53.9% and a specificity of 68.7%. Method 1 used in combination with method 3 resulted in a sensitivity of 98.4% and a specificity of 15.1%. The sensitivity and specificity of the machine learning algorithm were 87.3% and 79.1%, respectively.

CONCLUSIONS

coding alone demonstrated poor sensitivity in identifying cases of anaphylaxis, with venom-related anaphylaxis missing 96% of cases. The machine learning algorithm resulted in a better balance of sensitivity and specificity and improves upon previous strategies to identify ED anaphylaxis visits.