Assessment of a new algorithm in the management of acute respiratory tract infections in children

Assistant Professor, Department of Pediatrics, Mofid Children Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Journal of research in medical sciences (Impact Factor: 0.65). 02/2012; 17(2):182-5.
Source: PubMed


To assess the practicability of a new algorithm in decreasing the rate of incorrect diagnoses and inappropriate antibiotic usage in pediatric Acute Respiratory Tract Infection (ARTI).
Children between 1 month to15 years brought to outpatient clinics of a children's hospital with acute respiratory symptoms were managed according to the steps recommended in the algorithm.
Upper Respiratory Tract Infection, Lower Respiratory Tract Infection, and undifferentiated ARTI accounted for 82%, 14.5%, and 3.5% of 1 209 cases, respectively. Antibiotics were prescribed in 33%; for: Common cold, 4.1%; Sinusitis, 85.7%; Otitis media, 96.9%; Pharyngotonsillitis, 63.3%; Croup, 6.5%; Bronchitis, 15.6%; Pertussis-like syndrome, 82.1%; Bronchiolitis, 4.1%; and Pneumonia, 50%.
Implementation of the ARTIs algorithm is practicable and can help to reduce diagnostic errors and rate of antibiotic prescription in children with ARTIs.

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Available from: Alireza Khatami, Dec 27, 2013
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    ABSTRACT: Background: Clinical symptoms in children with pulmonary diseases are frequently non-specific. Rare diseases such as primary ciliary dyskinesia (PCD), cystic fibrosis (CF) or protracted bacterial bronchitis (PBB) can be easily missed at the general practitioner (GP). Objective: To develop and test a questionnaire-based and data mining-supported tool providing diagnostic support for selected pulmonary diseases. Methods: First, interviews with parents of affected children were conducted and analysed. These parental observations during the pre-diagnostic time formed the basis for a new questionnaire addressing the parents’ view on the disease. Secondly, parents with a sick child (e.g. PCD, PBB) answered the questionnaire and a data base was set up. Finally, a computer program consisting of eight different classifiers (support vector machine (SVM), artificial neural network (ANN), fuzzy rule-based, random forest, logistic regression, linear discriminant analysis, naive Bayes and nearest neighbour) and an ensemble classifier was developed and trained to categorise any given new questionnaire and suggest a diagnosis. For estimating the diagnostic accuracy, we applied ten-fold stratified cross validation. Results: All questionnaires of patients suffering from CF, asthma (AS), PCD, acute bronchitis (AB) and the healthy control group were correctly diagnosed by the fusion algorithm. For the pneumonia (PM) group 19/21 (90.5%) and for the PBB group 17/18 (94.4%) correct diagnoses could be reached. The program detected the correct diagnoses with an overall sensitivity of 98.8%. Receiver operating characteristics (ROC) analyses confirmed the accuracy of this diagnostic tool. Case studies highlighted the applicability of the tool in the daily work of a GP. Conclusion: For children with symptoms of pulmonary diseases a questionnaire-based diagnostic support tool using data mining techniques exhibited good results in arriving at diagnostic suggestions. In the hands of a doctor, this tool could be of value in arousing awareness for rare pulmonary diseases such as PCD or CF.
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