Expert artificial intelligence-based natural language processing characterises childhood asthma.

INTRODUCTION: The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics. METHODS: Using the 1997-2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC-/NLP-API-)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs). RESULTS: Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC-/NLP-API-. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects. CONCLUSION: Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

Fracture incidence in Ehlers-Danlos syndrome - A population-based case-control study.

BACKGROUND: The differential diagnosis of non-accidental injury during childhood includes medical conditions that predispose to skeletal fragility. Ehlers-Danlos syndrome (EDS) has been proposed as one such condition despite little objective evidence in the medical literature. OBJECTIVE: To investigate if EDS causes increased bone fragility during infancy and childhood. PARTICIPANTS AND SETTING: Residents of an 8-county region in southern Minnesota using the Rochester Epidemiology Project (REP) medical records-linkage system. METHODS: This retrospective, population-based, case-control study identified subjects with EDS from 1976 to 2015 who had complete records for at least their first year of life. Validity of diagnosis was ascertained using the 2017 International Classification of the Ehlers-Danlos Syndromes. Records were reviewed for fracture diagnoses that were characterized by age, location, type and mechanism. RESULTS: Of 219 potential cases, 21 had complete records for the first year of life and sufficient evidence in the medical record to support an EDS diagnosis. Of these 21, there were 14 hypermobile, 2 classical, 4 vascular, and 1 arthrochalasia EDS subtypes. 11 of 21 EDS cases (52.4%) and 15 of 63 controls (23.8%) had one or more fractures during childhood. No fractures were identified in the first year of life. Comparing cases to controls, EDS was associated with having any fractures during childhood with an odds ratio of 3.4 (95% CI: 1.20-9.66). CONCLUSIONS: We found no evidence that infants with common forms of EDS are predisposed to more frequent fractures. Ambulatory subjects with these EDS subtypes may have a higher incidence of fractures during childhood.

Association of Asthma with Rheumatoid Arthritis: A Population-Based Case-Control Study.

BACKGROUND: TH1 and TH2 cells have counterregulatory relationships. However, the relationship between asthma, a TH2-predominant condition, and risk of systemic inflammatory diseases such as rheumatoid arthritis (RA), a TH1 condition, is poorly understood. OBJECTIVE: We aimed to determine whether asthma was associated with increased risks of incident RA among adults. METHODS: We conducted a retrospective population-based case-control study that examined existing incident RA cases and controls matched by age, sex, and registration year from the general population in Olmsted County, Minnesota, between January 2002 and December 2007. We performed comprehensive medical record reviews to ascertain asthma status using predetermined asthma criteria. The frequency of a history of asthma before the index date was compared between cases and controls. Logistic regression models were used to adjust for confounding factors. RESULTS: We enrolled 221 RA cases and 218 controls. Of the 221 RA cases, 156 (70.6%) were females, 207 (93.7%) were white, the median age at the index date was 52.5 years, and 53 (24.0%) had a history of asthma. Controls had similar characteristics except that 35 of 218 controls (16.1%) had a history of asthma. After adjustment for sex, age, smoking, body mass index, socioeconomic status, and comorbidity, asthma was significantly associated with increased risks of RA (adjusted odds ratio, 1.74; 95% CI, 1.05-2.90; P = .03). CONCLUSIONS: Despite the counterregulatory relationship between TH1 and TH2 cells, patients with asthma had a significantly higher risk of developing RA than healthy individuals.

Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.

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.