Voge, Gretchen A.

AbstractBackgroundFrequent wheezing in original asthma predictive index (API) was defined by parental report of recurrent wheezing within 1 year during the first 3 years of life. The nature of frequent wheezing in children, particularly aged over 3 years, has not been studied. We aimed to assess the frequency and interval of wheezing to define frequent wheezing in ascertaining asthma for children using medical records.MethodsAmong children who participated in a previous study (n = 427), all wheezing episodes documented in medical records were collected for children who had ≥2 wheezing episodes PLUS met one major criterion or two minor criteria of API. We compared the distribution of known risk factors for asthma between subjects having two consecutive wheezing episodes with shorter interval (≤1 year) compared to those with longer interval (1 to 3 years).ResultsA total of 62 children met API at median age of 2.3 years. During follow‐up period (median age: 11.3 years), a total of 198 wheezing episodes were observed. 81% of wheezing intervals were within 3 years from the earlier wheezing episode, including 60% within 1 year. Children who met API based on 1‐year interval (n = 40) vs 1‐ to 3‐year interval (n = 13) appeared to be similar in regard to the known risk factors for asthma.ConclusionsOur exploratory study finding suggests that children who had frequent wheezing episodes with longer interval (<3 years) need to be considered to be determined as asthma cases when API is applied to retrospective medical records. Prospective studies with a larger sample size need to replicate this finding.

Abstract Rationale Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. Objectives We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs). Methods The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997–2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis). Measurements and Main Results After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same. Conclusions Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.

BACKGROUND AND OBJECTIVE: Previous studies suggest that former late preterm infants are at increased risk for learning and behavioral problems compared with term infants. These studies have primarily used referred clinical samples of children followed only until early school age. Our objective was to determine the cumulative incidence of attention deficit/hyperactivity disorder (ADHD) and learning disabilities (LD) in former late preterm versus term infants in a population-based birth cohort. METHODS: Subjects included all children born 1976 to 1982 in Rochester, MN who remained in the community after 5 years. This study focused on the comparison of subjects in 2 subgroups, late preterm (34 to &lt;37 weeks) and term (37 to &lt;42 weeks). School and medical records were available to identify individuals who met research criteria for ADHD and LD in reading, written language, and math. The Kaplan-Meier method was used to estimate the cumulative incidence of each condition by 19 years of age. Cox models were fit to evaluate the association between gestational age group and condition, after adjusting for maternal education and perinatal complications. RESULTS: We found no statistically significant differences in the cumulative incidence of ADHD or LD between the late preterm (N = 256) versus term (N = 4419) groups: ADHD (cumulative incidence by age 19 years, 7.7% vs 7.2%; P = .84); reading LD (14.2% vs 13.1%; P = .57); written language LD (13.5% vs 15.7%; P = .36), and math LD (16.1% vs 15.5%; P = .89). CONCLUSIONS: These data from a population-based birth cohort indicate that former late preterm infants have similar rates of LD and ADHD as term infants.