Lung clearance index predicts persistence of preschool wheeze.

BACKGROUND: The lung clearance index (LCI) is a measure of pulmonary function. Variable feasibility (50->80%) in preschool children has been reported. There are limited studies exploring its relationship to respiratory symptoms and how it predicts persistent wheeze. We aimed to assess the association with respiratory symptoms in preschool-aged children with LCI and determine its utility in predicting persistent wheeze. METHODS: LCI was measured in a subcohort of the CHILD Cohort Study at age 3 years using SF6  multiple breath washout test mass spectrometry. Respiratory symptom phenotypes at age 3 were derived from children's respiratory symptoms reported by their parents. Responses were used to categorize children into 4 symptom groups: recurrent wheeze (3RW), recurrent cough (3RC), infrequent symptoms (IS), and no current symptoms (NCS). At age 5 years, these children were seen by a specialist clinician and assessed for persistent wheeze (PW). RESULTS: At age 3 years, 69% (234/340) had feasible LCI. Excluding two children with missing data, 232 participants were categorized as follows: 33 (14%) 3RW; 28 (12%) 3RC; 17 (7%) IS; and 154 (66%) NCS. LCI z-score at age 3 years was highest in children with 3RW compared to 3RC (mean (SD): 1.14 (1.56) vs. 0.09 (0.95), p < .01), IS (mean (SD): -0.14 (0.59), p < .01), and NCS (mean (SD): -0.08 (1.06), p < .01). LCI z-score at age 3 was predictive of persistent wheeze at age 5 (PW) (AUROC: 0.87). CONCLUSIONS: LCI at age 3 was strongly associated with recurrent wheeze at age 3, and predictive of its persistence to age 5.

Development of a Symptom-Based Tool for Screening of Children at High Risk of Preschool Asthma.

Importance: Despite advances in asthma therapeutics, the burden remains highest in preschool children; therefore, it is critical to identify primary care tools that distinguish preschool children at high risk for burdensome disease for further evaluation. Current asthma prediction tools, such as the modified Asthma Predictive Index (mAPI), require invasive tests, limiting their applicability in primary care and low-resource settings. Objective: To develop and evaluate the use of a symptom-based screening tool to detect children at high risk of asthma, persistent wheeze symptoms, and health care burden. Design, Setting, and Participants: The cohort for this diagnostic study included participants from the CHILD Study (n = 2511) from January 1, 2008, to December 31, 2012, the Raine Study from January 1, 1989, to December 31, 2012 (n = 2185), and the Canadian Asthma Primary Prevention Study (CAPPS) from January 1, 1989, to December 31, 1995 (n = 349), with active follow-up to date. Data analysis was performed from November 1, 2019, to May 31, 2022. Exposures: The CHILDhood Asthma Risk Tool (CHART) identified factors associated with asthma in patients at 3 years of age (timing and number of wheeze or cough episodes, use of asthma medications, and emergency department visits or hospitalizations for asthma or wheeze) to identify children with asthma or persistent symptoms at 5 years of age. Main Outcomes and Measures: Within the CHILD Study cohort, CHART was evaluated against specialist clinician diagnosis and the mAPI. External validation was performed in both a general population cohort (Raine Study [Australia]) and a high-risk cohort (CAPPS [Canada]). Predictive accuracy was measured by sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and positive and negative predicted values. Results: Among 2511 children (mean [SD] age at 3-year clinic visit, 3.08 [0.17] years; 1324 [52.7%] male; 1608 of 2476 [64.9%] White) with sufficient questionnaire data to apply CHART at 3 years of age, 2354 (93.7%) had available outcome data at 5 years of age. CHART applied in the CHILD Study at 3 years of age outperformed physician assessments and the mAPI in predicting persistent wheeze (AUROC, 0.94; 95% CI, 0.90-0.97), asthma diagnosis (AUROC, 0.73; 95% CI, 0.69-0.77), and health care use (emergency department visits or hospitalization for wheeze or asthma) (AUROC, 0.70; 95% CI, 0.61-0.78). CHART had a similar predictive performance for persistent wheeze in the Raine Study (N = 2185) in children at 5 years of age (AUROC, 0.82; 95% CI, 0.79-0.86) and CAPPS (N = 349) at 7 years of age (AUROC, 0.87; 95% CI, 0.80-0.94). Conclusions and Relevance: In this diagnostic study, CHART was able to identify children at high risk of asthma at as early as 3 years of age. CHART could be easily incorporated as a routine screening tool in primary care to identify children who need monitoring, timely symptom control, and introduction of preventive therapies.

The Development of an Infrastructure to Facilitate the Use of Whole Genome Sequencing for Population Health.

The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems.