Only then will telemonitoring achieve the aim of preventing hospital admissions , contributing to the reduction of health re source utilization and improving the quality of life of patients
Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors , poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations .
This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease ( COPD ) or asthma .
Prediction of Asthma Exacerbations in Children by Innovative Exhaled Inflammatory Markers: Results of a Longitudinal Study
Background In asthma management guidelines the primary goal of treatment is asthma control . To date , asthma control , guided by symptoms and lung function , is not optimal in many children and adults . Direct monitoring of airway inflammation in exhaled breath may improve asthma control and reduce the number of exacerbations . Aim 1 ) To study the use of fractional exhaled nitric oxide ( FeNO ) and inflammatory markers in exhaled breath condensate ( EBC )  , in the prediction of asthma exacerbations in a pediatric population . 2 ) To study the predictive power of these exhaled inflammatory markers combined with clinical parameters . Methods 96 asthmatic children were included in this one-year prospect i ve observational study , with clinical visits every 2 months . Between visits , daily symptom scores and lung function were recorded using a home monitor . During clinical visits , asthma control and FeNO were assessed . Furthermore , lung function measurements were performed and EBC was collected . Statistical analysis was performed using a test data set and validation data set for 1 ) conditionally specified models , receiver operating characteristic-curves ( ROC-curves ) ; 2 ) k-nearest neighbors algorithm . Results Three conditionally specified predictive models were constructed . Model 1 included inflammatory markers in EBC alone , model 2 included FeNO plus clinical characteristics and the ACQ score , and model 3 included all the predictors used in model 1 and 2 . The area under the ROC-curves was estimated as 47 % , 54 % and 59 % for models 1 , 2 and 3 respectively . The k-nearest neighbors predictive algorithm , using the information of all the variables in model 3 , produced correct predictions for 52 % of the exacerbations in the validation data set . Conclusion The predictive power of FeNO and inflammatory markers in EBC for prediction of an asthma exacerbation was low , even when combined with clinical characteristics and symptoms . Qualitative improvement of the chemical analysis of EBC may lead to a better non-invasive prediction of asthma exacerbations
Only then will telemonitoring achieve the aim of preventing hospital admissions , contributing to the reduction of health re source utilization and improving the quality of life of patients
Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors , poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations .
This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease ( COPD ) or asthma .