Variation in Phenotype Definitions in Observational Clinical Research: A Review of Three Conditions

Area of Science:

• Health research methodology
• Observational database studies
• Biostatistics

Background:

• Phenotype algorithms (PA) are crucial for identifying patient cohorts in observational studies.
• Heterogeneity in PA definitions can lead to variability in research findings.
• Alzheimer's disease (AD), major depressive disorder (MDD), and pulmonary arterial hypertension (PAI) are complex conditions with diverse patient populations.

Purpose of the Study:

• To assess the heterogeneity of phenotype algorithms (PA) used in Alzheimer's disease (AD), major depressive disorder (MDD), and pulmonary arterial hypertension (PAI) research.
• To evaluate the impact of PA variations on patient cohort overlap and incidence rate estimation.
• To identify inconsistencies in reporting and documentation across studies.

Main Methods:

• Systematic review of 49 replicated phenotype algorithms (PAs) across AD, MDD, and PAI.
• Analysis of PA variations and their effect on patient identification in six observational databases.
• Assessment of reporting completeness for primary condition codes and inclusion criteria.

Main Results:

• Significant heterogeneity was observed across the 49 reviewed PAs (13 for AD, 23 for MDD, 13 for PAI).
• Varied PAs identified distinct patient cohorts, leading to substantial heterogeneity in incidence rates.
• Comprehensive documentation for reproducibility was frequently lacking.

Conclusions:

• Heterogeneity in phenotype algorithms significantly impacts patient cohort identification and incidence rate estimation in AD, MDD, and PAI research.
• The lack of standardized and transparent PA reporting hinders research reproducibility.
• Promoting robust and transparent research practices is essential for reliable observational database studies.