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Evaluating the impact of alternative phenotype definitions on incidence rates across a global data network. | LitMetric

AI Article Synopsis

  • The study focuses on the importance of creating accurate phenotype definitions for reliable safety research, comparing different definitions to see how they affect background incidence rates of adverse events.
  • Using data from 16 sources, the researchers analyzed 13 adverse events and discovered significant variations in incidence rates based on how phenotypes were defined, particularly with different modifications like inpatient settings.
  • The results indicated that requiring an inpatient setting significantly increased the incidence rates, showing the need to carefully evaluate definitions before using them for background rate assessments in a global context.

Article Abstract

Objective: Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome.

Materials And Methods: We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates.

Results: Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52.

Discussion: The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition.

Conclusion: Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662662PMC
http://dx.doi.org/10.1093/jamiaopen/ooad096DOI Listing

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