scholarly journals Shoulder injury related to SARS-CoV-2 vaccine administration

2022 ◽  
Vol 194 (2) ◽  
pp. E46-E49
Author(s):  
Jacky C.K. Chow ◽  
Sarah L. Koles ◽  
Aaron J. Bois
Keyword(s):  
Shoulder Injury ◽  
Vaccine Administration

scholarly journals Shoulder Injury Related to Vaccine Administration: The DoD Experience

2020 ◽  
Vol 145 (2) ◽  
pp. AB70
Author(s):  
Laurie Housel ◽  
Bruce McClenathan ◽  
Jeannie Huh ◽  
John Klaric
Keyword(s):  
Shoulder Injury ◽  
Vaccine Administration

scholarly journals Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) Using Natural Language Processing

2021 ◽  
Author(s):  
Chengyi Zheng ◽  
Jonathan Duffy ◽  
In-Lu Amy Liu ◽  
Lina S. Sy ◽  
Ronald A. Navarro ◽  
...  
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chart Review ◽  
Population Based ◽  
Care Organization ◽  
Reference Standard ◽  
Shoulder Injury ◽  
Vaccine Administration

Background: Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, there is a lack of population-based studies due to the challenge of identifying SIRVA cases in large health care databases. Objective: To develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes. Methods: We conducted the study among members of a large integrated health care organization who were vaccinated between 04/1/2016 and 12/31/2017 and had subsequent diagnosis codes indicative of shoulder injury. Based on a training dataset with a chart review reference standard of 164 individuals, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified three groups of positive SIRVA cases (definite, probable and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated individuals. We then applied the final automated NLP algorithm to a broader cohort of vaccinated individuals with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases. Results: In the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 individuals without SIRVA. In the broader cohort of 53,585 individuals, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.3%, 67.7% and 18.9%, respectively. Conclusions: The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation.

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