A computational phenotype for pediatric asthma exacerbations requiring hospitalization using electronic health record...

Computational phenotypes can be used to improve patient selection for observational studies. We sought to derive and validate a computational phenotype for pediatric asthma exacerbation requiring hospitalization.

Retrospective cohort study using electronic health record (EHR) data from a single quaternary children's hospital. We used ICD 9 and 10 diagnostic codes and medication data to develop multiple iterations of a computational phenotype. Encounters identified by each phenotype were manually reviewed by expert chart reviewers, and positive predictive value (PPV) was calculated. Sensitivity was obtained by comparison with an established cohort in the Virtual Pediatric Systems (VPS) database.

Our cohort included 19 015 pediatric encounters from 2014 to 2022 admitted with an indication of asthma. Starting with 3 broad criteria (a diagnostic code for asthma, receipt of albuterol, and receipt of systemic steroids), we iteratively added phenotype criteria to improve the PPV. The initial phenotype using albuterol and systemic steroids had 65% PPV. Restricting the timeframe for receipt to the first 24 h of the encounter and increasing the amount of albuterol used improved the PPV to 85%. Encounters using only dexamethasone or not having a diagnostic code for asthma were tested and found to have low PPV. The final phenotype included the use of >1 nebulized albuterol treatment or continuous albuterol and the use of systemic steroids in the first 24 h of hospitalization, plus a diagnostic code for asthma, and excluded encounters that only used dexamethasone. This phenotype achieved a PPV of 93%. Using this as a reference metric, defining the cohort based only on diagnostic codes had a PPV of 34%. Applying these criteria to the VPS cohort admitted to the intensive care unit yielded a sensitivity of 97%.

We created a computational phenotype for pediatric asthma exacerbation requiring hospitalization using structured EHR data elements, which achieved a high positive predictive value and sensitivity. This phenotype can be leveraged for future observational studies in this population, but may require institution-specific adjustments.