Risk for Shoulder Conditions After Vaccination: A Population-Based Study Using Real-World Data

Aiming too high: Shoulder injury related to vaccine administration (SIRVA): A case series

PURPOSE

Shoulder injury directly related to vaccination (SIRVA) occurs when a vaccine is administered too high in the shoulder. The primary aim of this study was to accurately detail the occurrence, symptoms, diagnosis, management and long-term outcomes of SIRVA cases in Victoria, Australia.

PRINCIPAL RESULTS

The study identified 102 SIRVA cases from 2007 to 2020 from the Surveillance of Adverse Events Following Vaccination in the Community (SAEFVIC) database. The majority [73/85; (86 %)] of cases resolved completely with a median time to resolution of 8 weeks and no statistically significant difference in recovery by immunisation provider type or baseline imaging.

MAJOR CONCLUSIONS

This large case series includes long-term clinical progress in SIRVA, allowing accurate evaluation and analysis. Further evaluation is required to establish if other risk factors contribute to SIRVA, which may help with targeted, tailored education for providers on correct vaccine administration technique, including in large and rapid vaccine rollouts.

Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method (Preprint)

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, due to the difficulty of finding SIRVA cases in large health care databases, population-based studies are scarce.

Objective

The goal of the research was 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 April 1, 2016, and December 31, 2017, and had subsequent diagnosis codes indicative of shoulder injury. Based on a training data set with a chart review reference standard of 164 cases, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified 3 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 cases. We then applied the final automated NLP algorithm to a broader cohort of vaccinated persons 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 cases without SIRVA. In the broader cohort of 53,585 vaccinations, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.5% (278/291), 67.7% (84/124), and 17.3% (9/52), 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.