Browsing by Author "Castro Rodríguez, José Antonio"

Now showing 1 - 14 of 14
  • Thumbnail Image
    Item
    Automated chart review utilizing natural language processing algorithm for asthma predictive index
    (2018) Kaur, Harsheen; Sohn, Sunghwan; Wi, Chung-Il; Ryu, Euijung; Park, Miguel A.; Bachman, Kay; Kita, Hirohito; Croghan, Ivana; Castro Rodríguez, José Antonio; Voge, Gretchen A.
    Abstract Background Thus 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. Methods This 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. Results Among 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. Conclusion NLP-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.Abstract Background Thus 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. Methods This 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. Results Among 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. Conclusion NLP-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.Abstract Background Thus 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. Methods This 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. Results Among 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. Conclusion NLP-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.
  • No Thumbnail Available
    Item
    Cost-effectiveness analysis of phenotypic-guided versus guidelines-guided bronchodilator therapy in viral bronchiolitis
    (2021) Rodriguez Martinez, Carlos E.; Nino, Gustavo; Castro Rodríguez, José Antonio; Pérez, Geovanny F.; Sossa Briceño, Mónica P.; Buendia, Jefferson A.
  • Thumbnail Image
    Item
    Epigenética en enfermedades alérgicas y asma
    (2016) Castro Rodríguez, José Antonio; Krause Leyton, Bernardo; Uauy, Ricardo; Casanello Toledo, Paola Cecilia
    Las enfermedades alérgicas y el asma son el resultado de complejas interacciones entre la predisposición genética y factores ambientales. El asma es una de las enfermedades crónicas más prevalentes en niños. En este artículo se revisan algunos factores ambientales como la exposición a alérgenos, tabaco, bacterias, componentes microbianos, dieta, obesidad y estrés, que intervienen durante la vida intrauterina y la infancia en la regulación epigenética de las enfermedades alérgicas y el asma. La revisión se realiza en tres tipos de modelos: in-vitro, animales y humanos.
  • Thumbnail Image
    Item
  • No Thumbnail Available
    Item
    Guía ALERTA 2. América Latina y España: Recomendaciones para la prevención y el tratamiento de la exacerbación asmática
    (2010) Rodrigo, Gustavo Javier; Plaza Moral, V.; Forns, Santiago Bardagí; Castro Rodríguez, José Antonio; De Diego Damiá, Alfredo; Cortés, Santos Liñán; Melero-Moreno, Carlos; Nannini, Luís Javier; Neffen, Hugo E.; Salas, Jorge Del Diego
  • Thumbnail Image
    Item
    IL-10 expression in macrophages from neonates born from obese mothers is suppressed by IL-4 and LPS/INFγ
    (2017) Cifuentes Zuniga, Francisca; Arroyo Jousse, Viviana; Soto Carrasco, Gustavo; Casanello Toledo, Paola Cecilia; Uauy, Ricardo; Krause Leyton, Bernardo; Castro Rodríguez, José Antonio
  • Thumbnail Image
    Item
    Oxygen Saturation in Childhood at High Altitude: A Systematic Review
    (2020) Ucrós, Santiago; Granados Claudia M.; Castro Rodríguez, José Antonio; Hill, Catherine M.
    Background: It is well known that oxygen saturation as measured by pulse oximetry (SpO2) decreases as altitude increases. However, how SpO2 changes across childhood, and more specifically during sleep/wake states, at different high altitudes are less well understood. We aimed to perform a systematic review of all studies with direct SpO2 measurement in healthy children living at high altitude (>2500 meters above sea level) to address these questions. Methods: MEDLINE, EMBASE, and SciELO databases were searched up to December 2018. Two independent reviewers screened the literature and extracted relevant data. Results: Of 194 references, 20 studies met the eligibility criteria. Meta-analysis was not possible due to the use of different oximeters and/or protocols for data acquisition and reporting of different SpO2 central tendency and dispersion measures. The most relevant findings from the data were: (1) SpO2 is lower as altitude increases; (2) at high altitude, SpO2 improves with age through childhood; (3) SpO2 is lower during sleep and feeding in comparison to when awake, this SpO2 gap between wake and sleep states is more evident in the first months of life and narrows later in life; (4) SpO2 dispersion (interindividual variation) is higher at younger ages, and more so during sleep; (5) In 6/20 studies, the SpO2 values were nonnormally distributed with a consistent left skew. Conclusions: At high altitude, the mean/median SpO2 increases in children with aging; a significant gap between wake and sleep states is seen in the first months of life, which narrows as the infant gets older; SpO2 dispersion at high altitude is wider at younger ages; at high altitude, SpO2 shows a nonnormal distribution skewed to the left; this bias becomes more evident as altitude increases, at younger ages and during sleep.
  • Thumbnail Image
    Item
    Predicting the outcome of respiratory disease in wheezing infants using tidal flow-volume loop shape
    (2020) Keklikian, E.; Cornes, P.; Cela, C. J.; Sanchez Solis, M.; García Marcos, L.; Castro Rodríguez, José Antonio
  • Thumbnail Image
    Item
    Relación entre asma e infecciones virales
    (2007) Castro Rodríguez, José Antonio
  • No Thumbnail Available
    Item
  • Thumbnail Image
    Item
    Solar radiation, air pollution, and bronchiolitis hospitalizations in Chile : an ecological study
    (2019) Terrazas, C.; Castro Rodríguez, José Antonio; Camargo, C. A.; Borzutzky Schachter, Arturo
  • No Thumbnail Available
    Item
    The asthma predictive index remains a useful tool to predict asthma in young children with recurrent wheeze in clinical practice
    (2011) Castro Rodríguez, José Antonio; Cifuentes, Lorena; Rodríguez-Martínez, Carlos E.
  • Thumbnail Image
    Item
    Urinary leukotriene excretion profile in children with exercise-induced asthma compared with controls: A preliminary study
    (2012) Brockmann Veloso, Pablo Edmundo; Castro Rodríguez, José Antonio; Holmgren Palmen, Nils Linus Anders; Cerda, Jaime; Contreras Sepúlveda, Ana María; Moya I., Ana; Bertrand N., Pablo
  • No Thumbnail Available
    Item