Microbiology and management of respiratory infections in children with tracheostomy.

Tracheostomy-related respiratory infections are common, though the diagnosis and management can be challenging in children. The goal of this review article was to provide an overview of the current knowledge known about recognizing and treating respiratory infections in this population and to emphasize future areas for further research. While several small and retrospective papers attempt to provide information, there remain more questions than answers. We have reviewed ten published articles to understand this topic, bringing to light significant variation in clinical practices across institutions. While identifying the microbiology is important, it is also crucial to recognize when to treat. Differentiating acute infection, chronic infection, and colonization are important features that influence the treatment of lower respiratory tract infection in children with a tracheostomy.

Development of a pediatric obstructive sleep apnea triage algorithm.

INTRODUCTION: Diagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists. METHOD: We used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT. RESULTS: From the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm. CONCLUSION: Tonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist.

Concordance between the Piko - 1(R) portable device and pneumotachography in measuring PEF and FEV1 in asthmatic children

Objective: To assess concordance in the measurement of peak expiratory flow (PEF) and forced expiratory volume in one second (FEV1) between the portable device Piko-1 (Ferraris) and a pneumotachograph. Patients and methods: Forced spirometry (Master Screen Jaeger) was performed according to ATS/ERS norms, selecting the best value of three curves, and three measurements with the Piko-1 were recorded, following the recommendations of the manufacturer. Results. Eighty patients between 5–18 years of age were studied. Based on the Bland-Altman method, the mean differences obtained were 9.82 (95%CI: 2.43–17.21) for PEF and 0.17 (95%CI: 0.12–0.21) for FEV1. The intraclass correlation coefficient was 0.96 (p<0,001; 95%CI: 0.93–0.97) for FEV1 and 0.93 (p<0,001; 95%CI: 0.89–0.95) for PEF. Conclusions: Piko-1 offers FEV1 measurements close to those obtained with forced spirometry, thus allowing more exact patient assessment in home-based follow-up, emergency services, or hospital wards (AU)

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Concordance between the Piko - 1 portable device and pneumotachography in measuring PEF and FEV(1) in asthmatic children.

OBJECTIVE: To assess concordance in the measurement of peak expiratory flow (PEF) and forced expiratory volume ino ne second (FEV(1)) between the portable device Piko-1 (Ferraris) and a pneumotachograph. PATIENTS AND METHODS: Forced spirometry (Master Screen Jaeger) was performed according to ATS/ERS norms, selecting the best value of three curves, and three measurements with the Piko-1 were recorded the recommendations of the manufacturer. RESULTS: Eighty patients between 5-18 years of age were studied. Based on the Bland-Altman method, the mean differences obtained were 9.82 (95%Cl: 2.43-17.21) for PEF and 0.17 (95%CL: 0.12-0.21 for FEV(1). The intraclass correlation coefficient was 0.96 (p <0,001; 95%Cl: 0.93-0.97) for PEV(1) and 0.93 (p<0,0001; 95%Cl: 0.89-0.95) for PEF. CONCLUSIONS: Piko-1 offers FEV(1) measurements close to those obtained with forced spirometry, thus allowing more exact patient assessment in home-based follow-up emergency services, or hospital wards.