Vaccine · 2026
Accuracy of ICD and SNOMED search strategies for adverse events following COVID-19 vaccination: Analysis of hospital administrative data.
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Kenealy Timothy, Aguirre-Duarte Nelson, Roxburgh Richard H, Royle Gordon, Mitchelson Bryan, Ingram Joan, Williams Nicky, Petousis-Harris Helen
Аннотация
BACKGROUND: Hospital discharge codes can be used to identify possible Adverse Events of Special Interest (AESI) following COVID-19 vaccinations.
AIM: We sought to estimate the positive predictive value (PPV) and level of certainty of ICD-10-AM and SNOMED coding for meeting Brighton Collaboration case definitions of AESIs in Aotearoa, New Zealand.
METHODS: Our expert panel identified 24 ICD-10-AM codes and 2 SNOMED codes expected to identify 9 AESIs. We sought codes likely to be specific rather than sensitive. Medical record reviews were conducted by an experienced coder, adjudicated by medical specialists, for the level of certainty that each case met the target case definition. Admissions coded to an explicit alternative diagnosis were classified as Not a Case.
RESULTS: Our data covered over 3 million people. Reviews were conducted on 761 medical records, randomly selected from admissions principally in calendar years 2016 to 2019. We report the PPV of each code with respect to its level of certainty of meeting the case definition. Only Guillain-Barré had a single code specific to a single AESI. Several neurological conditions had overlapping codes, conditions and case definitions. PPVs for individual codes to meet case definitions ranged from 11% to 100%. Level of certainty of each code and PPV are specified.
CONCLUSION: PPVs of codes for each AESI need to be assessed on a case-by-case basis. Many codes are arguably insufficiently 'accurate' to identify a target AESI. We did not assess PPVs for combinations of codes. Our method did not allow us to estimate the number of cases missed by the coding.
AIM: We sought to estimate the positive predictive value (PPV) and level of certainty of ICD-10-AM and SNOMED coding for meeting Brighton Collaboration case definitions of AESIs in Aotearoa, New Zealand.
METHODS: Our expert panel identified 24 ICD-10-AM codes and 2 SNOMED codes expected to identify 9 AESIs. We sought codes likely to be specific rather than sensitive. Medical record reviews were conducted by an experienced coder, adjudicated by medical specialists, for the level of certainty that each case met the target case definition. Admissions coded to an explicit alternative diagnosis were classified as Not a Case.
RESULTS: Our data covered over 3 million people. Reviews were conducted on 761 medical records, randomly selected from admissions principally in calendar years 2016 to 2019. We report the PPV of each code with respect to its level of certainty of meeting the case definition. Only Guillain-Barré had a single code specific to a single AESI. Several neurological conditions had overlapping codes, conditions and case definitions. PPVs for individual codes to meet case definitions ranged from 11% to 100%. Level of certainty of each code and PPV are specified.
CONCLUSION: PPVs of codes for each AESI need to be assessed on a case-by-case basis. Many codes are arguably insufficiently 'accurate' to identify a target AESI. We did not assess PPVs for combinations of codes. Our method did not allow us to estimate the number of cases missed by the coding.