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Are All Vaccines Created Equal? Using Electronic Health Records to Discover Vaccines Associated With Clinician-Coded Adverse Events.

Boland MR, Tatonetti NP - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: To adjust for healthcare-process effects, phase two compared cases against those who returned to CUMC within 3 months without an ADE.We report 7 results passing multiplicity correction after demographic confounder adjustment.Our algorithm could inform clinicians of the risks/benefits of vaccinations towards improving clinical care.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University ; Observational Health Data Sciences and Informatics, Columbia University.

ABSTRACT
Adverse drug events (ADEs) are responsible for unnecessary patient deaths making them a major public health issue. Literature estimates 1% of ADEs recorded in Electronic Health Records (EHRs) are reported to federal databases making EHRs a vital source of ADE-related information. Using Columbia University Medical Center (CUMC)'s EHRs, we developed an algorithm to mine for vaccine-related ADEs occurring within 3 months of vaccination. In phase one, we measured the association between vaccinated patients with an ADE (cases) against those vaccinated without an ADE. To adjust for healthcare-process effects, phase two compared cases against those who returned to CUMC within 3 months without an ADE. We report 7 results passing multiplicity correction after demographic confounder adjustment. We observed an association, having some literature support, between swine flu vaccination and ADEs (H1N1v-like, OR=9.469, p<0.001; H1N1/H3N2, OR=3.207, p<0.001). Our algorithm could inform clinicians of the risks/benefits of vaccinations towards improving clinical care.

No MeSH data available.


Related in: MedlinePlus

Algorithm Schema to Detect Vaccines Associated with Clinician-Coded Adverse Events
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Related In: Results  -  Collection


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f1-2091521: Algorithm Schema to Detect Vaccines Associated with Clinician-Coded Adverse Events

Mentions: The first part of our algorithm (Figure 1) calculates the association between each vaccine and an ADE within 3 months by comparing each individual vaccine (case) to all other vaccines in our dataset (as controls). Controls include all patients who were vaccinated regardless of whether they returned to the hospital for a follow-up visit. Associations are measured using the fisher-exact test with multiplicity correction using Bonferroni’s method (R v.3.1.0).


Are All Vaccines Created Equal? Using Electronic Health Records to Discover Vaccines Associated With Clinician-Coded Adverse Events.

Boland MR, Tatonetti NP - AMIA Jt Summits Transl Sci Proc (2015)

Algorithm Schema to Detect Vaccines Associated with Clinician-Coded Adverse Events
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525221&req=5

f1-2091521: Algorithm Schema to Detect Vaccines Associated with Clinician-Coded Adverse Events
Mentions: The first part of our algorithm (Figure 1) calculates the association between each vaccine and an ADE within 3 months by comparing each individual vaccine (case) to all other vaccines in our dataset (as controls). Controls include all patients who were vaccinated regardless of whether they returned to the hospital for a follow-up visit. Associations are measured using the fisher-exact test with multiplicity correction using Bonferroni’s method (R v.3.1.0).

Bottom Line: To adjust for healthcare-process effects, phase two compared cases against those who returned to CUMC within 3 months without an ADE.We report 7 results passing multiplicity correction after demographic confounder adjustment.Our algorithm could inform clinicians of the risks/benefits of vaccinations towards improving clinical care.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University ; Observational Health Data Sciences and Informatics, Columbia University.

ABSTRACT
Adverse drug events (ADEs) are responsible for unnecessary patient deaths making them a major public health issue. Literature estimates 1% of ADEs recorded in Electronic Health Records (EHRs) are reported to federal databases making EHRs a vital source of ADE-related information. Using Columbia University Medical Center (CUMC)'s EHRs, we developed an algorithm to mine for vaccine-related ADEs occurring within 3 months of vaccination. In phase one, we measured the association between vaccinated patients with an ADE (cases) against those vaccinated without an ADE. To adjust for healthcare-process effects, phase two compared cases against those who returned to CUMC within 3 months without an ADE. We report 7 results passing multiplicity correction after demographic confounder adjustment. We observed an association, having some literature support, between swine flu vaccination and ADEs (H1N1v-like, OR=9.469, p<0.001; H1N1/H3N2, OR=3.207, p<0.001). Our algorithm could inform clinicians of the risks/benefits of vaccinations towards improving clinical care.

No MeSH data available.


Related in: MedlinePlus