Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis. (1st November 2020)

• Record Type: Journal Article
• Authors: Fishe, Jennifer N.; Bian, Jiang; Chen, Zhaoyi; Hu, Hui; Min, Jae; Modave, Francois; Prosperi, Mattia
• Is Part Of: Journal of asthma. Volume 57:Number 11(2020)
• Page Start: 1155
• Page End: 1167
• Publication Date: 2020-11-01
• Subjects: Asthma -- COPD -- prediction -- multi-domain -- machine learning; Asthma -- Periodicals; 616.238005
• DOI: 10.1080/02770903.2019.1642352
• Languages: English
• ISSNs: 0277-0903

Abstract

Abstract: Objectives: To identify prodromal correlates of asthma as compared to chronic obstructive pulmonary disease and allied-conditions (COPDAC) using a multi domain analysis of socio-ecological, clinical, and demographic domains. Methods: This is a retrospective case-risk-control study using data from Florida's statewide Healthcare Cost and Utilization Project (HCUP). Patients were grouped into three groups: asthma, COPDAC (without asthma), and neither asthma nor COPDAC. To identify socio-ecological, clinical, demographic, and clinical predictors of asthma and COPDAC, we used univariate analysis, feature ranking by bootstrapped information gain ratio, multivariable logistic regression with LogitBoost selection, decision trees, and random forests. Results: A total of 141, 729 patients met inclusion criteria, of whom 56, 052 were diagnosed with asthma, 85, 677 with COPDAC, and 84, 737 with neither asthma nor COPDAC. The multi-domain approach proved superior in distinguishing asthma versus COPDAC and non-asthma/non-COPDAC controls (area under the curve (AUROC) 84%). The best domain to distinguish asthma from COPDAC without controls was prior clinical diagnoses (AUROC 82%). Ranking variables from all the domains found the most important predictors for the asthma versus COPDAC and controls were primarily socio-ecological variables, while for asthma versus COPDAC without controls, demographic and clinical variables such as age, CCI, and prior clinical diagnoses, scored better. Conclusions: In this large statewide study using a machine learning approach, we found that a multi-domain approach with demographics, clinical, and socio-ecological variables best predicted an asthma diagnosis. Future work should focus on integrating machine learning-generated predictive models into clinical practice to improve early detection of those common respiratory diseases.