BackgroundContemporary critical care research necessitates involvement of multiple centers, preferably from many countries. Adult and pediatric research networks have produced outstanding data; however, their involvement is restricted to a small percentage of the industrialized nations. Implementation of their findings in low- and middle-income countries (LMICs) is fraught with challenges.MethodsWe conducted an online international survey to assess and compare disease burden and resources to participate in multicenter research studies through a listserv of the World Federation of Pediatric Intensive and Critical Care Societies. Respondents were grouped into high-income countries and LMICs on the basis of World Bank classification.ResultsSurvey was completed by 73 centers in 34 countries (34 from high-income countries and 39 from LMICs). Compared with high-income countries, the pediatric intensive care units in LMICs were characterized by a lower number of critical care specialists, more difficult access to hemodialysis, and a lower number of elective postoperative patients, but a similar overall disease burden. Training and resources for research were comparable in the two cohorts.ConclusionsAlthough differences exist in access to both trained providers and equipment, the survey results were more striking in their similarity. It is essential that centers from LMICs be included in multinational studies, to generate results applicable to all children worldwide.
BackgroundThus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.MethodsThis is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma.ResultsAmong the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6–6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8–10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively.ConclusionNLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.
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