Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias

Diseases Common in Persons With Cystic Fibrosis Among CFTR Heterozygotes

Importance

Cystic fibrosis is one of the most commonly diagnosed autosomal recessive disorders in the US. It is estimated that more than 10 million individuals are heterozygous for a pathogenic CFTR gene variant in the US (heterozygotes). The phenotypic risk of these heterozygotes is not well defined, particularly among populations of predominantly non-European genetic ancestry. Understanding disease risk across each population can improve management strategies for all.

Objective

To examine associations of diseases across the phenome with CFTR heterozygotes.

Design, Setting, and Participants

The All of Us Research Program is a US-based ongoing longitudinal cohort study whose enrollment started nationally in 2018. In this genetic association study, whole-genome sequencing data were linked to electronic health records (EHRs) and surveys. Participants were 18 years and older. Similarity to genetic ancestral groups was genetically inferred using All of Us data and 2 large reference datasets, the 1000 Genomes Project and Human Genome Diversity Project. This analysis was conducted between February and April 2025.

Exposures

A single pathogenic CFTR variant.

Main Outcomes and Measures

The main variables included clinical diagnoses documented in EHRs. Multivariable-adjusted phenome-wide association studies were performed. The main measures were odds ratios (ORs), indicating risk for a particular disease or condition.

Results

Overall, 363 pathogenic variants were identified in the cohort. Among 317 964 adult participants (55.7% female; mean [SD] last age in EHR, 56.1 [16.9] years), 7957 heterozygotes and 280 995 noncarriers were identified. Participants were followed up through EHRs with a mean (SD) follow-up of 12.4 (9.0) years. The genetically inferred ancestral distribution of the cohort was 18.0% African, 16.2% American or Admixed American, 2.1% East Asian, 53.4% European, 0.3% South Asian, and 0.4% West Asian. Frequencies of heterozygotes varied by groups of genetic similarity to reference populations: 3.62% in participants most genetically similar to a European reference population (n = 169 812), 1.35% in participants most genetically similar to an African reference population (n = 57 297), and 1.86% in participants most genetically similar to an Admixed American reference population (n = 51 483). A total of 2909 phenotypes were analyzed. No statistically significant associations were identified in heterozygotes of all populations combined or within each genetic ancestral group. Among 52 cystic fibrosis-associated diseases, although an elevated risk of respiratory diseases and infections was observed in some heterozygotes (allergic bronchopulmonary aspergillosis [OR, 2.50; 95% CI, 1.27-4.95]; bronchiectasis [OR, 1.21; 95% CI, 1.00-1.47]; pneumonia due to Streptococcus pneumoniae [OR, 1.54; 95% CI, 1.05-2.26]; chronic obstructive pulmonary disease [OR, 1.14; 95% CI, 1.05-1.24]; asthma [OR, 1.08; 95% CI, 1.01-1.15]; and Pseudomonas infection [OR, 1.34; 95% CI, 1.03-1.74]), effect sizes of these associations were several orders of magnitude lower than those found in homozygotes or predicted compound heterozygotes.

Conclusions and Relevance

In this genetic association study, most heterozygotes did not appear to have a substantially higher risk of cystic fibrosis-associated diseases during their adulthood compared to noncarriers. Additional studies are needed to investigate the underlying factors for the elevated risk of respiratory and infectious diseases in some heterozygotes.

Computable phenotypes to identify respiratory viral infections in the All of Us research program

Electronic health records (EHRs) contain rich temporal data about respiratory viral infections, but methods to identify these infections from EHR data vary widely and lack robust validation. We developed computable phenotypes by integrating virus-specific International Classification of Diseases (ICD) billing codes, prescriptions, and laboratory results within 90-day episodes. Analysis of 265,222 participants with EHR data from the All of Us Research Program yielded national cohorts of varied size: large cohorts for SARS-CoV-2 (n = 28,729) and influenza (n = 19,784); medium cohorts for rhinovirus, human coronavirus, and respiratory syncytial virus (n = 1,161-1,620); and smaller cohorts for the other viruses (n = 238-486). Using laboratory results as a reference standard, phenotypes using virus-specific ICD codes and medications had variable sensitivity (8-67%) but high positive predictive value (PPV, 90-97%) for most viruses, while influenza virus and SARS-CoV-2 phenotypes had lower PPV (69-70%) that improved with the inclusion of additional ICD codes. Identified infections exhibited expected seasonal patterns matching CDC data. This integrated approach identified infections more effectively than individual components alone and demonstrated utility for severe infections in hospital settings. This method enables large-scale studies of host genetics, health disparities, and clinical outcomes across episodic diseases, with flexibility to optimize sensitivity or PPV depending on the specific research question.

Computable Phenotypes for Respiratory Viral Infections in the All of Us Research Program

Electronic health records (EHRs) contain rich temporal data about infectious diseases, but an optimal approach to identify infections remains undefined. Using the All of Us Research Program, we developed computable phenotypes for respiratory viruses by integrating billing codes, prescriptions, and laboratory results within 90-day episodes. Phenotypes computed from 265,222 participants yielded cohorts ranging from 238 (adenovirus) to 28,729 (SARS-CoV-2) cases. Virus-specific billing codes showed varied sensitivity (8-67%) and high positive predictive value (90-97%), except for influenza virus and SARS-CoV-2 where lower PPV (69-70%) improved with increasing billing codes. Identified infections exhibited expected seasonal patterns and virus proportions when compared with CDC data. This integrated approach identified episodic disease more effectively than individual components alone and demonstrated utility in identifying severe infections. The method enables large-scale studies of host genetics, health disparities, and clinical outcomes across episodic diseases.