Modeling Efficacy and Safety of Engineered Biologics

Chabot, J; Gomes, Bruce

doi:10.1002/9780470431818.ch12

Cited by 2 publications

References 72 publications

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Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study

Holdefer,

Pizarro,

Saunders-Hastings

et al. 2024

JMIR Public Health Surveill

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Background: Adverse events (AEs) associated with vaccination have been evaluated by epidemiological studies, and more recently gained additional attention with the Emergency Use Authorization (EUA) of several COVID-19 vaccines. As part of its responsibility to conduct post-market surveillance, the U.S. Food and Drug Administration (FDA) continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19.Objective: This study is part of the Biologics Effectiveness and Safety (BEST) Initiative, which aims to improve FDA's postmarket surveillance capabilities while minimizing public burden. This study looks to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify five AESIs: anaphylaxis, Guillain-Barré syndrome (GBS), myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome (TTS), and febrile seizure. AESI phenotype algorithms can be developed to apply to electronic health record (EHR) data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. Methods:To validate the performance of the algorithms, we applied them to EHR data from a United States academic health system and clinicians evaluated a sample of cases. Performance was assessed using positive predictive value (PPV).Results: Our anaphylaxis algorithm was the best performing, having a PPV of 93.3%. The PPVs for our febrile seizure, myocarditis/pericarditis, TTS, and GBS algorithms were 89.0%, 83.5%, 70.2%, and 47.2%, respectively.Conclusions: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI post-market detection.

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Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study

Holdefer,

Pizarro,

Saunders-Hastings

et al. 2024

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study (Preprint)

Holdefer,

Pizarro,

Saunders-Hastings

et al. 2023

Preprint

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BACKGROUND Adverse events (AEs) associated with vaccination have been evaluated by epidemiological studies, and more recently gained additional attention with the Emergency Use Authorization (EUA) of several COVID-19 vaccines. As part of its responsibility to conduct post-market surveillance, the U.S. Food and Drug Administration (FDA) continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. OBJECTIVE This study is part of the Biologics Effectiveness and Safety (BEST) Initiative, which aims to improve FDA’s post-market surveillance capabilities while minimizing public burden. This study looks to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify five AESIs: anaphylaxis, Guillain-Barré syndrome (GBS), myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome (TTS), and febrile seizure. AESI phenotype algorithms can be developed to apply to electronic health record (EHR) data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. METHODS To validate the performance of the algorithms, we applied them to EHR data from a United States academic health system and clinicians evaluated a sample of cases. Performance was assessed using positive predictive value (PPV). RESULTS Our anaphylaxis algorithm was the best performing, having a PPV of 93.3%. The PPVs for our febrile seizure, myocarditis/pericarditis, TTS, and GBS algorithms were 89.0%, 83.5%, 70.2%, and 47.2%, respectively. CONCLUSIONS Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI post-market detection.

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Modeling Efficacy and Safety of Engineered Biologics

Cited by 2 publications

References 72 publications

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study (Preprint)

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