Patient Trajectories from Electronic Health Records Suggest Specialty Specific Warning Signs for Common Variable Immunodeficiency
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Background
Common variable immunodeficiency is frequently underrecognized due to its heterogeneous clinical presentation, resulting in diagnostic delays that increase disease-related complications, with each year of delay increasing mortality risk by 4% for the most common inborn error of immunity.
Objective
In individuals with established common variable immunodeficiency, we sought to recognize patterns in patient diagnostic trajectories to identify opportunities for interventions to reduce time to diagnosis.
Methods
We analyzed retrospective electronic health records data from 139 individuals with physician-confirmed common variable immunodeficiency across five academic health systems representing >10million patients. We evaluated diagnosing specialties and international classification of disease code trajectories in the two years prior to and at common variable immunodeficiency confirmation. Additionally, we construct specialty-specific prediction on a 1:50 case: control cohort using a gradient boosting algorithm.
Results
The leading diagnoses at common variable immunodeficiency determination were respiratory (35% of individuals), and individuals with respiratory symptoms were more likely to be diagnosed by Allergy / Immunology than by other specialties (odds ratio 2.9, standard error 0.35, p-value 1.9x10 - 3 ) and had a higher frequency of visits to pulmonary disease in the two years prior to diagnosis. Individuals with neoplasms were 50% more likely than other patients (OR 2.0, SE 0.29, p-value 0.02) to receive a diagnosis at each visit, reducing their diagnostic delay by up to a year. We find that a larger ratio of number of specialties visited to the total number of visits is significantly correlated with an increased time to diagnosis. A trajectory-informed specialty-specific prediction improved detection with an area under the precision-recall curve of fifty times the value expected under the null.
Conclusions
Individuals referred to Allergy / Immunology have shorter time to diagnosis, and a specialty-specific prediction could improve detection of common variable immunodeficiency.
Clinical Implications
Overall, we find that phenotype-specialty interactions may inform targeted interventions to improve common variable immunodeficiency recognition across specialties, reducing diagnostic odyssey and disease-related complications.
Capsule Summary
Specialty-specific symptom patterns and patient trajectories preceding common variable immunodeficiency diagnosis reveal opportunities for earlier recognition through context-aware electronic health record–based prediction.
