TY - JOUR
T1 - Predicting asthma control deterioration in children
AU - Luo, Gang
AU - Stone, Bryan L.
AU - Fassl, Bernhard
AU - Maloney, Christopher G.
AU - Gesteland, Per H.
AU - Yerram, Sashidhar R.
AU - Nkoy, Flory L.
N1 - Funding Information:
We thank Intermountain Allergy & Asthma for sharing their pollen count and mold level data, and Tom H. Greene and Xiaoming Sheng for helpful discussions. Drs. Nkoy, Stone, Fassl, and Maloney are supported by grants 1R18HS018166-01A1 and 1R18HS018678-01A1 from the Agency for Healthcare Research and Quality. Dr. Stone is also supported by award KM1CA156723 from the National Cancer Institute.
Publisher Copyright:
© 2015 Luo et al.
PY - 2015/10/14
Y1 - 2015/10/14
N2 - Background: Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence. Methods: We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead. Results: Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic. Conclusions: Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.
AB - Background: Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence. Methods: We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead. Results: Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic. Conclusions: Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.
KW - Asthma control
KW - Child
KW - Predict
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U2 - 10.1186/s12911-015-0208-9
DO - 10.1186/s12911-015-0208-9
M3 - Article
C2 - 26467091
AN - SCOPUS:84944245904
SN - 1472-6947
VL - 15
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 84
ER -