Elsevier

Sleep Medicine

Volume 13, Issue 3, March 2012, Pages 217-227
Sleep Medicine

Review Article
Algorithm for the diagnosis and treatment of pediatric OSA: A proposal of two pediatric sleep centers

https://doi.org/10.1016/j.sleep.2011.09.009Get rights and content

Abstract

There is currently no consensus on the best method of managing of obstructive sleep apnea (OSA) in childhood. In the present paper, an algorithm for the diagnosis and treatment of the disorder is proposed. Sleep apnea is suspected when parents report relevant symptoms or when there are abnormalities that predispose to OSA such as adenotonsillar hypertrophy, obesity, craniofacial anomalies, or neuromuscular disorders. OSA-associated morbidity including elevated blood pressure, daytime sleepiness or learning problems, growth failure, and enuresis should be recognized. Severity of intermittent upper airway obstruction during sleep can be determined objectively by polysomnography or, if polysomnography is not available, by nocturnal pulse oximetry. Risk factors predicting persistence of OSA in adolescence (male gender, development of obesity) need to be identified. Children with moderate-to-severe OSA, or with mild OSA, but accompanied by morbidity, or by risk factors predicting persistence of the disorder should have priority for treatment. An individualized and multifaceted therapeutic approach which addresses in a step-by-step fashion all abnormalities that contribute to upper airway obstruction during sleep is necessary.

Introduction

Obstructive sleep-disordered breathing describes a spectrum of abnormal breathing patterns during sleep characterized by snoring and increased respiratory effort [1]. Depending on the severity of upper airway obstruction, these breathing patterns may range from primary snoring to upper airway resistance syndrome, obstructive hypoventilation, and obstructive sleep apnea (OSA) [1]. The American Thoracic Society has defined OSA as a disorder of breathing during sleep characterized by prolonged partial or intermittent complete upper airway obstruction (hypopnea or obstructive apnea) which impairs normal ventilation and sleep pattern [2]. Polysomnography (nocturnal sleep recordings) is the main tool for the diagnosis of sleep-disordered breathing and the apnea–hypopnea index (mean number of central + mixed + obstructive apneas and hypopneas per hour of total sleep) (AHI) is the most frequently used polysomnography index for characterizing the severity of upper airway obstruction [3].

OSA is not a distinct disease, but rather a syndrome of functional impairment of the upper airway in a sleeping individual resulting from multiple disorders. Each of these multiple OSA etiologies is a disease entity by itself with its own genetic background and influences from the environment (Table 1). Dysfunction of the upper airway activates a number of pathogenetic mechanisms that may lead to overt morbidity in the long term, depending on the degree of functional impairment of the airway, the individual’s genetic background, and, possibly, environmental and lifestyle factors.

More specifically, patency of the upper airway during sleep is controlled by complex interactions between upper airway resistance, pharyngeal collapsibility, tone of pharyngeal dilator muscles, and negative intralumenal pressure generated by the muscles of inspiration [4]. In some children this fine balance of mechanical forces is disrupted. For example, enlarged adenotonsillar tissue and obesity may increase resistance to airflow and pharyngeal collapsibility [5]. The tendency of the airway walls to collapse under the influence of negative intralumenal pressure is counterbalanced by increased neuromuscular activation of the pharyngeal dilator muscles. Nevertheless, abrupt, intermittent reductions in activation of the pharyngeal dilator muscles during sleep in susceptible individuals lead to episodic airway collapse and hypopneic or apneic events [6].

Response to adenotonsillectomy, the standard treatment for pediatric OSA, is relatively unpredictable in regards to normalization of the breathing patterns during sleep [7], [8], [9]. In a recent multicenter center study, 21.6% of children with OSA had an AHI >5 episodes/h, postoperatively [9]. This finding is consistent with the concept that OSA is not a distinct disease, but rather the consequence of one or more disorders. Nasal continuous positive airway pressure (nCPAP), the main therapy for OSA in obese children and adolescents not cured by adenotonsillectomy, simply compensates for the upper airway dysfunction by “stenting” the pharyngeal lumen. The “disease” re-emerges immediately after the “treatment” is withdrawn.

Due to the complexity of the problem (multiple conditions predisposing to OSA which may or may not be accompanied by morbidity), and the fact that at least mild OSA resolves spontaneously or does not deteriorate in an appreciable proportion of children [10], [11], there is no consensus in the literature regarding its diagnosis and treatment. The American Academy of Pediatrics recommends overnight polysomnography for the definition of the severity of upper airway obstruction during sleep [12]. However, fewer than 10% of children referred for adenotonsillectomy due to habitual snoring in the US undergo polysomnography [13]. Moreover, there is no consensus for cut-off values of polysomnography parameters that will separate children requiring treatment from those in whom treatment is not necessary. In addition, few sleep laboratories, especially outside the US, have expertise in the evaluation of children with sleep-disordered breathing.

In the present paper, an algorithm for the diagnosis and treatment of OSA is presented by two pediatric sleep centers with the hope that it will stimulate fruitful discussion. The algorithm has been adjusted for application to pediatric populations around the world, and even in settings in which access to a sleep laboratory may not be available. It should be emphasized that this paper is partly an opinion piece and not a consensus guideline or a practice parameter prepared by a scientific society. We recognize that more research is required to validate the proposed diagnostic and therapeutic approach, but see this paper as an initial step in this discussion. Obstructive sleep-disordered breathing patterns without apneas and hypopneas (i.e., primary snoring, upper airway resistance syndrome, and obstructive hypoventilation) are not addressed in the current review since published evidence on their management is extremely limited [1], [14].

Section snippets

Methodology of literature review

For the construction of the proposed algorithm, evidence has been collected by literature search of the PubMed database for the period between January 1970 and June 2011. The search strategy was: “sleep apnea” or “sleep-disordered breathing” or “snoring.” The search was limited to articles in the English language and referred to humans with an age of 0–18 years. This literature search produced 4087 articles. Initially all titles were screened relevant abstracts were selected and read in detail.

Proposed algorithm for the diagnosis and management of OSA

The proposed algorithm has been summarized in Fig. 1. Information collected in Steps 1–4 is used subsequently to identify children who require treatment for OSA (Step 5) and to determine the type(s) of appropriate therapeutic measure(s).

Step 1: Recognize the child who is at increased risk for having OSA. One or more of the following will be present:

  • A.

    Parents report symptoms that are indicative of OSA (snoring, witnessed apneas, laboured breathing, and restless sleep) (Evidence A).

  • B.

    During visit to

Conclusion

An integrated, stepwise (hierarchical) management of the child with suspected diagnosis of OSA should take under consideration severity of intermittent upper airway obstruction during sleep and presence of morbidity or other coexisting conditions that share common pathogenetic mechanisms with sleep apnea. Successful treatment of OSA is multifaceted, addressing all abnormalities which contribute to upper airway dysfunction in the individual patient.

Funding sources

LKG is supported by NIH Grant K12-HL-090003; DG is supported by NIH grants HL-065270 and HL-086662 and by P50 HL-107160.

Conflicts of interest

The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: doi:10.1016/j.sleep.2011.09.009.

. ICMJE Form for Disclosure of Potential Conflicts of Interest form.

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