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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

In Phase Two, Controls were Selected that Returned to CUMC within 3 Months Minimizing Healthcare Process Biases that Affect Patients’ Ability to Return for Treatment.
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f2-2091521: In Phase Two, Controls were Selected that Returned to CUMC within 3 Months Minimizing Healthcare Process Biases that Affect Patients’ Ability to Return for Treatment.

Mentions: To adjust for various health-care process effects [8, 9] that may affect whether or not a patient returns to CUMC within 3 months, we decided to use as controls all patients who were vaccinated and were subsequently diagnosed with some other medical condition (not an ADE) within 3 months of the vaccination date. Our cases remained unchanged and consisted of all vaccinated patients with an ADE diagnosis within 3 months. Therefore, in this second phase of the algorithm both cases and controls returned to CUMC within 3 months. For this analysis, we had 65,708 controls and the same 1,231 cases (Figure 1). We measured the association between each vaccination and an ADE diagnosis using logistic regression. Specifically, each potential confounder (i.e., ethnicity, race, sex, age (at time of vaccination)) was modeled as a covariate in the logistic regression equation with the binary response (outcome) variable indicating the presence or absence of an ADE within 3 months of vaccination and the predictor variable denoting presence or absence of the vaccine of interest (R v.3.1.0). An association is reported as significant if the Bonferroni adjusted p-value is <=0.05. We further illustrate phase two’s control selection method in Figure 2.


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)

In Phase Two, Controls were Selected that Returned to CUMC within 3 Months Minimizing Healthcare Process Biases that Affect Patients’ Ability to Return for Treatment.
© Copyright Policy
Related In: Results  -  Collection

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

f2-2091521: In Phase Two, Controls were Selected that Returned to CUMC within 3 Months Minimizing Healthcare Process Biases that Affect Patients’ Ability to Return for Treatment.
Mentions: To adjust for various health-care process effects [8, 9] that may affect whether or not a patient returns to CUMC within 3 months, we decided to use as controls all patients who were vaccinated and were subsequently diagnosed with some other medical condition (not an ADE) within 3 months of the vaccination date. Our cases remained unchanged and consisted of all vaccinated patients with an ADE diagnosis within 3 months. Therefore, in this second phase of the algorithm both cases and controls returned to CUMC within 3 months. For this analysis, we had 65,708 controls and the same 1,231 cases (Figure 1). We measured the association between each vaccination and an ADE diagnosis using logistic regression. Specifically, each potential confounder (i.e., ethnicity, race, sex, age (at time of vaccination)) was modeled as a covariate in the logistic regression equation with the binary response (outcome) variable indicating the presence or absence of an ADE within 3 months of vaccination and the predictor variable denoting presence or absence of the vaccine of interest (R v.3.1.0). An association is reported as significant if the Bonferroni adjusted p-value is <=0.05. We further illustrate phase two’s control selection method in Figure 2.

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