ArticleEClinicalMedicine2025
External validation and update of the pediatric asthma risk score as a passive digital marker for childhood asthma using integrated electronic health records.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Effect of an electronic health record-integrated machine learning asthma risk marker on pediatrician prognostic accuracy during preschool age: a pilot randomized clinical trial.Scientific reports · 2026Trial
- Precision medicine to diagnose asthma in preschool children: comparison of clinical scores, lung function, biomarkers, and genetic tests.Paediatric respiratory reviews · 2026Review
- Timing of High-Risk Asthma Specialist Enrollment and Severe Asthma Exacerbations in Children.The journal of allergy and clinical immunology. In practice · 2026Article
- Early initiation of inhaled corticosteroid-long-acting βPediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026Article
- Feasibility and acceptability of a passive digital marker for childhood asthma risk prognosis: a pilot cross-sectional study.Pilot and feasibility studies · 2026Article
- Real-world effectiveness of asthma biologics by age of initiation and early-childhood risk factors.Annals of the American Thoracic Society · 2026Article
- Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.Current opinion in allergy and clinical immunology · 2026Review
- Prognostic utility of pre-biologic treatment correlates of childhood severe asthma exacerbation risk: Real world evidence.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Article
- Predictive correlates of poor sleep associated with increased risk of severe asthma exacerbations among children with moderate-to-severe asthma.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Article
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Authors and funding
5 authors.
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Abstract
Background: In the search for practical prognostic decision support, numerous childhood asthma prediction tools (including a recent Pediatric Asthma Risk Score [PARS]) with modest prognostic accuracy have been developed, however, the prognostic utility of these tools using existing electronic health records (EHR) in clinical settings is unknown. To test the hypothesis that childhood asthma can be predicted using EHR, we sought to externally validate and update the PARS as a passive digital marker (PDM) for asthma risk. Methods: Using a retrospective, population-based observational study design, children born between 2010 and 2017 who were consecutively enrolled at any of the pediatric healthcare institutions that contribute EHR data to the Indiana Network of Patient Care (INPC) databases were included in our analyses. Logistic and Cox proportional hazards models were used to validate and update the EHR-based PARS as a PDM for the prediction of physician documented diagnosis of asthma between ages 4-11 years. Findings: Among 69,109 eligible children, of whom 5290 (7.65%) had a confirmed asthma diagnosis after age 4-years, our PDM had a higher prognostic accuracy (Area Under the Curve (AUC): 0.79; 95% CI: 0.78, 0.80; sensitivity-0.71 and specificity-0.74) than the EHR-based PARS (AUC: 0.76; 95% CI: 0.75, 0.76; sensitivity-0.74 and specificity-0.68) for early case detection. Both the PDM and EHR-based PARS had satisfactory calibration. For children classified as high-risk at age 3-years, the incidence of asthma was higher using the PDM than the EHR-based PARS (37% vs. 26%, p < 0.0001). Interpretation: It is feasible to use EHR data for childhood asthma risk prediction by updating existing tools (e.g., PARS) with relevant clinical context to assure high prognostic accuracy and clinical utility during early childhood, a period of diagnostic uncertainty. Funding: This study was supported by National Institutes of Health grants, K01HL166436 (AHO), R03HS029088 (AHO) and P01HL158507 (BG).
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