Vaccine · 2021
ICD-10 anaphylaxis algorithm and the estimate of vaccine-attributable anaphylaxis incidence in Medicare.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Goud Ravi, Thompson Deborah, Welsh Kerry, Lu Michael, Loc Julie, Lindaas Arnstein, Arya Deepa, Chillarige Yoganand, Wernecke Michael, MaCurdy Thomas E, Kelman Jeffrey A
Аннотация
BACKGROUND: Anaphylaxis is a rare, serious allergic reaction. Its identification in large healthcare databases can help better characterize this risk.
OBJECTIVE: To create an ICD-10 anaphylaxis algorithm, estimate its positive predictive values (PPVs) in a post-vaccination risk window, and estimate vaccination-attributable anaphylaxis rates in the Medicare Fee For Service (FFS) population.
METHODS: An anaphylaxis algorithm with core and extended portions was constructed analyzing ICD-10 anaphylaxis claims data in Medicare FFS from 2015 to 2017. Cases of post-vaccination anaphylaxis among Medicare FFS beneficiaries were then identified from October 1, 2015 to February 28, 2019 utilizing vaccine relevant anaphylaxis ICD-10 codes. Information from medical records was used to determine true anaphylaxis cases based on the Brighton Collaboration's anaphylaxis case definition. PPVs were estimated for incident anaphylaxis and the subset of vaccine-attributable anaphylaxis within a 2-day post-vaccination risk window. Vaccine-attributable anaphylaxis rates in Medicare FFS were also estimated.
RESULTS: The study recorded 66,572,128 vaccinations among 21,685,119 unique Medicare FFS beneficiaries. The algorithm identified a total of 190 suspected anaphylaxis cases within the 2-day post-vaccination window; of these 117 (62%) satisfied the core algorithm, and 73 (38%) additional cases satisfied the extended algorithm. The core algorithm's PPV was 66% (95% CI [56%, 76%]) for identifying incident anaphylaxis and 44% (95% CI [34%, 56%]) for vaccine-attributable anaphylaxis. The vaccine-attributable anaphylaxis incidence rate after any vaccination was 0.88 per million doses (95% CI [0.67, 1.16]).
CONCLUSION: The ICD-10 claims algorithm for anaphylaxis allows the assessment of anaphylaxis risk in real-world data. The algorithm revealed vaccine-attributable anaphylaxis is rare among vaccinated Medicare FFS beneficiaries.
OBJECTIVE: To create an ICD-10 anaphylaxis algorithm, estimate its positive predictive values (PPVs) in a post-vaccination risk window, and estimate vaccination-attributable anaphylaxis rates in the Medicare Fee For Service (FFS) population.
METHODS: An anaphylaxis algorithm with core and extended portions was constructed analyzing ICD-10 anaphylaxis claims data in Medicare FFS from 2015 to 2017. Cases of post-vaccination anaphylaxis among Medicare FFS beneficiaries were then identified from October 1, 2015 to February 28, 2019 utilizing vaccine relevant anaphylaxis ICD-10 codes. Information from medical records was used to determine true anaphylaxis cases based on the Brighton Collaboration's anaphylaxis case definition. PPVs were estimated for incident anaphylaxis and the subset of vaccine-attributable anaphylaxis within a 2-day post-vaccination risk window. Vaccine-attributable anaphylaxis rates in Medicare FFS were also estimated.
RESULTS: The study recorded 66,572,128 vaccinations among 21,685,119 unique Medicare FFS beneficiaries. The algorithm identified a total of 190 suspected anaphylaxis cases within the 2-day post-vaccination window; of these 117 (62%) satisfied the core algorithm, and 73 (38%) additional cases satisfied the extended algorithm. The core algorithm's PPV was 66% (95% CI [56%, 76%]) for identifying incident anaphylaxis and 44% (95% CI [34%, 56%]) for vaccine-attributable anaphylaxis. The vaccine-attributable anaphylaxis incidence rate after any vaccination was 0.88 per million doses (95% CI [0.67, 1.16]).
CONCLUSION: The ICD-10 claims algorithm for anaphylaxis allows the assessment of anaphylaxis risk in real-world data. The algorithm revealed vaccine-attributable anaphylaxis is rare among vaccinated Medicare FFS beneficiaries.