PEDIATRICS Vol. 117 No. 3 March 2006, pp. 741-753 (doi:10.1542/peds.2005-1067)
Polysomnographic Characteristics in Normal Preschool and Early School-Aged Children
a Department of Psychology, West Virginia University, Morgantown, West Virginia
b Kosair Children’s Hospital Research Institute and Division of Pediatric Sleep Medicine, Department of Pediatrics, University of Louisville, Louisville, Kentucky
c Department of Paediatric Respiratory and Sleep, John Hunter Children’s Hospital, Newcastle, New South Wales, Australia
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONREFERENCES |
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OBJECTIVE. The objective of this study was to describe overnight polysomnographic measures in normal children aged 3 to 7 years. We conducted a retrospective analysis of normal polysomnographic evaluations from participants in 2 large community-based studies of sleep-disordered breathing among preschoolers and early school-aged children at Kosair Children’s Hospital Sleep Medicine Research Center at the University of Louisville. Participants included 542 healthy children with ages ranging from 3.2 to 8.6 years.
RESULTS. Subjects were excluded from analysis if they had documented snoring during polysomnography, an obstructive apnea-hypopnea index of 
1.0, or a periodic leg-movement index of 5.0. Because the greatest differences in polysomnography occurred between ages 5 and 6 years, analyses were performed for children 3 to 5 years and for ages 6. Sleep cyclicity was distinct between age groups, with both showing an initial brief rapid-eye-movement period, which lengthened across the night, but only the older group showing a decrease in cycle length across the night. Average obstructive apnea indices were 0.03 per hour of total sleep time (TST) for 3- to 5-year-old children and 0.05 per hour of TST for 6-year-old children, whereas central apnea indices were 0.82 and 0.45 per hour of TST, respectively. Older children spent a greater percentage of sleep time supine, and the apnea-hypopnea index differed according to body position. Twenty percent of all subjects had end tidal carbon dioxide values of >45 mm Hg, and 2.2% had recorded values >50 mm Hg during 50% TST. High variance was present on all measures.
CONCLUSIONS. Developmental changes occur in several polysomnographic measures among normal children from 3 to 7 years of age, particularly during transition from preschool to early school age. Our findings in a large number of healthy community children comprise the most extensive compilation of normative reference values for laboratory-based pediatric polysomnography to date.
Key Words: sleep architecture • gas exchange • reference values
Abbreviations: SDB—sleep-disordered breathing • AHI—apnea-hypopnea index • PLM—periodic leg movement • ETCO2—end-tidal carbon dioxide • PETCO2—peak end-tidal carbon dioxide • SpO2—arterial oxygen saturation • TST—total sleep time • TIB—time in bed • REM—rapid eye movement • NREM—nonrapid eye movement sleep • AI—apnea index • SWS—slow-wave sleep • SPS—sleep pressure score • TAI—total arousal index • RAI—respiratory arousal index • SAI—spontaneous arousal index • PR-RSDB—parent report for risk for sleep-disordered breathing
Pediatric sleep continues to gain significant recognition as a result of both increasing evidence for a high prevalence of sleep disorders among children and by virtue of the potential somatic and biobehavioral effects of altered sleep during development. Sleep-disordered breathing (SDB) is by far the most frequently diagnosed pediatric sleep disorder, affects at least 1% to 3% of all children, and has been found to impose substantial adverse effects on cognition and school performance.1–9 Indeed, symptoms consistent with risk for SDB have been reported in 6% to 27% of children.2,10,11 Children with SDB use greater health care resources12 and experience more frequent cardiovascular morbidity13,14 and comorbid chronic illnesses.10,15 Children with SDB also display greater psychiatric and behavioral comorbidities.5,6,16–18 The cumulative evidence pointing to potential partial irreversibility of the cardiovascular, economic, and cognitive effects in both humans19–22 and animal models23,24 (see review in ref 25) highlights the importance of early detection of pediatric sleep disorders. However, interpretation of abnormal polysomnography and accurate diagnosis of disorders remains one of the challenges to pediatric sleep medicine and research, largely as a result of a historic lack of normative reference values. Because polysomnography is expensive and labor-intensive, and demands on resources are generally taken up by evaluation of disordered subjects or patients, few studies report normative values. These studies have universally reported either a small number of subjects or a wide age range, and most have focused on polysomnography in the laboratory,26–30 but several have reported results from polysomnography in the home31–34 or arterial oxygen saturation (SpO2)35 recorded in the home. In accordance with the 1996 mandate by the American Thoracic Society,36 the purpose of the present study was to develop normative polysomnographic reference values from a large group of healthy children spanning over a concise age range from a diverse, nonclinical community population.
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METHODS |
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The study was approved by the institutional review boards at University of Louisville and Kosair Children’s Hospital; informed consent was obtained before the overnight polysomnography. For subjects
7 years, subject assent was in the presence of a parent.
Participants
Data were obtained retrospectively from participants in 2 ongoing large-scale studies of community children attending either preschool or first-grade classes in the Jefferson County Public School System (Louisville, KY). Screening questionnaires were sent home with all children enrolled in Jump Start preschool classes to be completed by the parent and returned to the school where they then were collected by a researcher. Screening questionnaires were also sent to the homes of all children who were enrolled in first-grade classes in the Louisville metropolitan public school system to be returned by postage-paid mail to the research office.
The purpose of these large-scale studies was to evaluate children for evidence of SDB. Thus, qualifying at-risk subjects were recruited for overnight polysomnographic assessment using a parentally reported questionnaire that had been previously validated with the same populations.8 Control children who did not have a history of risk for SDB were also recruited into both studies and were included in the present analyses.
Screening Questionnaire
The questionnaire and extensive validation on the same groups whose data are reported here have been described previously.8 Briefly, information collected included demographics for parents and child and child health, sleep, and daytime behaviors. Participants in the larger studies included those within the entire range of parental report for risk for SDB who were contacted by phone from November 2001 through September 2002 for preschoolers and January 2000 to October 2004 for first-graders and invited to participate in the second phase of the study, which included overnight polysomnographic and cognitive and behavioral assessments. Results of the SDB evaluations and relationships with cognitive competence have been reported elsewhere.7,9,22,37,38
Exclusionary Criteria
Subjects were excluded from polysomnography if they had incomplete questionnaires and/or contact information, chronic medical conditions, genetic or overt craniofacial abnormalities, and/or they had previously sought medical evaluation for snoring. No polysomnography was performed on a night when a child had a fever, signs of respiratory or other infection or was reported to be feeling ill.
For the present study, participants in the original studies were excluded if they had an obstructive apnea-hypopnea index (AHI) of 1 or a periodic leg-movement (PLM) index of 5. Analyses for cyclicity, body position, and state dependency were available only for subjects recorded after February 2002, and end-tidal carbon dioxide (ETCO2) values from 43 subjects were unavailable as a result of equipment malfunction, persistent oral breathing, or cannula occlusion.
Overnight Polysomnography
Standard overnight multichannel polysomnographic evaluation was performed in the Sleep Medicine Center at Kosair Children’s Hospital. Children were studied for up to 12 hours in a quiet, darkened room with an ambient temperature of 
24°C with a parent or guardian present. Average time for lights out was 21:31 (SD: ±23 minutes) and average time for lights on was 06:20 (SD: ±26 minutes). No medications were used to induce sleep.
All measures were digitized by using commercially available polysomnographic systems; the first 11 preschool and 187 first-grade subjects were recorded by using Stellate Systems (Mission Viejo, CA). Subsequent recordings were performed by using MedCare Systems (MedCare Diagnostics, Buffalo, NY). The same parameters were used with both systems to record physiological measures: chest and abdominal wall movement by respiratory impedance or inductance plethysmography; heart rate by electrocardiogram; air flow with a sidestream end-tidal capnograph (PETCO2; BCI SC-300, Redding Medical, Inc, Finksburg, MD), which also provided breath-by-breath assessment of ETCO2 levels, and an oral-nasal airflow thermistor. SpO2 was assessed by pulse oximetry (Nellcor N 100; Nellcor, Inc, Hayward, CA) with simultaneously recorded pulse-wave form. Bilateral electrooculograms, 8 channels of electroencephalograms, chin and anterior tibial electromyograms, and analog output from a body-position sensor (Braebon Medical Corp, Ogdensburg, NY) were monitored also. Tracheal sound was monitored with a microphone sensor (Sleepmate; Rochester Electro-Medical, Inc, Tampa, FL), and digital time-synchronized video images were collected.
Sleep and Cyclicity
Sleep architecture was assessed by standard techniques.39 Polysomnographic data were scored by 2 analysts, and interscorer reliability assessments were performed.
Sleep-state percentages were calculated on the basis of both the total sleep time (TST) and time in bed (TIB) (from lights out to lights on); analyses for age, gender, and ethnicity differences were performed by using measures based on TST. Sleep efficiency was calculated as the percent of time sleeping as a function of the TIB. Sleep latency was the time from lights out to the first 3 consecutive epochs of stage 1 sleep. Rapid-eye-movement (REM) sleep latency was the time from lights out to the first epoch of REM sleep.
Sleep cyclicity was calculated; the first cycle was defined as the period from sleep onset through the ending of the first REM cycle, and subsequent cycles were based on the period from the ending of one REM bout through the ending of the subsequent REM bout. A REM bout was defined as a period of 1 REM epochs separated by <40 epochs of stage 1, 2, 3, or 4 or wake.
Respiratory Events, Oxygen Saturation, and Peak ETCO2
Central, obstructive, and mixed apneic events were scored by changes occurring in oral-nasal thermistor and/or ETCO2 cannula. Obstructive apnea was defined as the absence of airflow with continued chest-wall and abdominal movement for at least 2 breaths.28,36 Hypopneas were defined as a decrease in nasal flow of 50% with a corresponding decrease in SpO2 4% and/or with associated arousal.36 The AHI was defined as the number of apneas and hypopneas per hour of TST or by sleep-state and/or body-position subanalyses as appropriate. Apnea index (AI) was defined as the number of apneas per hour of TST.*
Central apnea was defined as the absence of both airflow and respiratory effort for at least 2 breaths.
Oxygen saturation values were computed to determine the following measures: mean SpO2 during sleep time; SpO2 nadir; and SpO2 desaturation indices during TST, REM, and non-REM (NREM). The percent of desaturations (
4% below baseline) to <95%, 90%, and 85% were calculated, as was the percent of sleep time spent in the ranges from 96% to 100%, 91% to 95%, 86% to 90%, and 81% to 85%.
Mean TST peak ETCO2 (PETCO2) was determined, along with the percent of TST spent 45 and 50 mm Hg.
Scoring of respiratory events and desaturations was initially automated; all events and signals were then individually scorer-validated.
Arousals
Because criteria for arousal have not yet been developed for children, arousals were defined as recommended by the American Sleep Disorders Association Task Force report40 and included spontaneous, respiratory-related (occurring immediately subsequent to an apnea, hypopnea, or snore), and technician-induced arousals. All arousals were scored manually. Total and subtype arousal indices were expressed as the number of arousals per hour of TST and REM, stages 1/2, and slow-wave sleep (SWS).
PLMs
PLMs were scored consistent with American Sleep Disorders Association Task Force guidelines41: a leg movement was scored if the electromyographic burst was between 0.5 and 5 seconds in duration and at least 25% of the burst elicited during prestudy calibration. A PLM sequence was assigned if there were 4 leg movements separated by at least 5 but no more than 90 seconds. Scoring was automated and then visually validated. PLM indices were calculated on the basis of TST, REM, stages 1/2, and SWS.
Body Position
Body position was sensor-measured and user-validated during scoring by using video recordings. The percentage of time spent supine, side, or prone was calculated. AHI, AI, and AI subtypes were calculated during supine, side, and prone sleeping and during REM, stages 1/2, and SWS within each body position.
Statistical Analyses
Cronbach’s 
and intraclass correlation coefficients were calculated for interscorer reliability. One-way analysis of variance was used to test for significant differences in main measures among the individual ages and between successive ages. Appropriate polynomial significance using linear or quadratic trend analyses with unweighted means were calculated. SPSS 11.5 (SPSS Inc, Chicago, IL) was used, and P .05 was considered statistically significant. Cohen’s d was calculated to determine gender and ethnicity effect sizes. Sleep pressure scores (SPSs) were calculated as described previously38; briefly, the SPS uses the reciprocal relationships between the total arousal index (TAI), respiratory arousal index (RAI), and spontaneous arousal index (SAI) in the following equation: SPS = RAI/TAI x [1 – (SAI/TAI)].
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RESULTS |
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Of a total of 542 children, 23 were aged 3 years, 78 aged 4 years, 72 aged 5 years, 238 aged 6 years, 124 aged 7 years, and 7 aged 8 years. Children 3 to 5 years were grouped into the "younger" age group (173 children), and children
6 years were classified as the "older" age group (369 children). For cyclicity and state and body-position subanalyses, only 107 younger and 162 older subjects were available. Ninety-five younger and 131 older subjects were available for PETCO2 analyses.
Cronbach’s and significance for intraclass correlations for interscorer reliability were = .975 (P < .001) for sleep-stage percents, = .821 (P < .05) for arousal subtypes, = .957 (P < .01) for PLM subtypes, and = .975 (P < .001) for apnea subtypes and hypopneas. For overall combined polysomnographic measures, = .996 (P < .001).
Original validation of the questionnaire instrument revealed that the parentally reported risk-factor profiles differed between preschoolers and first-graders, with different questionnaire items contributing to a parentally reported risk for SDB (PR-RSDB) score between the 2 groups.8 In addition, likelihood ratios indicated that a PR-RSDB score of 17 for children 4 years was consistent with a diagnosis of SDB after polysomnography, whereas a PR-RSDB of 24 was required for children >4 years. Accordingly, for the present study, comparisons between high and low PR-RSDB scores were made by using the appropriate profile and cutoff for the 2 age groups. No significant differences were found at either age for TST, sleep efficiency, sleep latency, REM latency, number of awakenings, amount of wake after sleep onset, percent awake, percent stage 1, percent stage 2, percent stage 3, percent stage 4, percent REM, TAI, PLM index, or AHI. Thus, although 20% of the younger and 16% of the older children had a moderate parental-reported history of risk for SDB, they are included in the normative analyses because they had clinically normal polysomnography and their polysomnographic measures did not differ from those of children with a low history of risk for SDB.
The duration of recording time (minutes) (x = 528.9; SD: 28.5; range: 297.5–624.5), number of awakenings (x = 9.37; SD: 6.6; range: 0–33), percent of wake time after sleep onset (x = 35.1; SD: 38.0; range: 0–295), REM percent, desaturation (4% baseline) index, percent time in SpO2 (ranges: 96–100, 91–95, 86–90, and 81–85), and spontaneous and total arousal indices did not differ between 3- to 5-year-old and 6- to 7-year-old children. RAIs differed (F = 2.6; P < .05), but there was a nonsignificant geometric trend among the ages, and there were no sequential age differences. Data are subsequently presented for combined ages for these measures; all other measures differed among ages and are presented separately in subsequent tables (Table 1).
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There were significant gender-by-age-group differences on several measures. Younger females had higher sleep efficiency (F = 5.1; P = .03; d = .29), less awake percent (F = 5.9; P = .02; d = .38), and higher stage 3 percent (F = 5.8; P = .02; d = .31) than males in the same age group. Among older children, females had higher stage 1 percent than males (F = 7.3; P = .007; d = .28). Ethnicity differences were tested among white and black children; white children had a higher stage 1 percent (F = 8.5; P = .004; d = .22) and lower stage 2 percent (F = 4.9; P = .027; d = .22). Because the younger children were predominantly from a low-income group and the older children represented the general community, maternal education was used as a proxy for socioeconomic status to test potential differences between these groups. Latency to REM onset increased as maternal education increased (F = 2.7; P = .04; d = .50), whereas the number of awakenings (F = 7.7; P < .001; d = .86) and stage 4 percent (F = 3.9; P = .009; d = .90) both decreased with advanced maternal education. Subject demographics, maternal education, and anthropometry data are presented in Table 2.
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Sleep time, latencies, and percent of time awake and in each sleep stage based on TIB and TST are shown in Table 3. TST and sleep efficiency initially increased until age 5 and then decreased in significant quadratic trends with significant changes from ages 3 to 4 on TST and from 3 to 4 and 6 to 7 for sleep efficiency. Sleep latency decreased linearly and REM latency increased linearly across ages, with significant changes from 3 to 4 for sleep latency and from 4 to 5 and 5 to 6 for REM latency (Table 1).
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Wake percent showed a significant linear decrease across all ages, with significant differences from 3 to 4 and 6 to 7 years. There were significant linear increases in stages 1 and 2 percent, whereas stages 3 and 4 percent decreased. For stages 1, 2, and 3 percent, there were significant changes between ages 5 and 6 (Table 3).
Subjects varied in the number of REM/NREM sleep cycles that they had during polysomnography: from 4 to 8 cycles for younger children (13% had 4 cycles, 31% had 5 cycles, 43% had 6 cycles, 8% had 7 cycles, and 5% had 8 cycles) and from 2 to 9 cycles for older children (3% had 2 cycles, 7% had 3 cycles, 28% had 4 cycles, 36% had 5 cycles, 18% had 6 cycles, 6% had 7 cycles, 1% had 8 cycles, and 1% had 9 cycles). The mode for number of REM/NREM cycles was used at each age (6 for younger and 5 for older children) to calculate the display values in Table 4 and the idealized hypnograms in Fig 1.
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NREM period; 
REM period.For both age groups, there was a significant linear trend for increased minutes of REM within cycles across the night (Flin = 27.45 and P < .001 for younger children; Flin = 17.9 and P < .001 for older children). There was no change in the duration of NREM/REM cycle lengths for younger children, but there was a linear decrease in the NREM/REM cycle length across the night for older children (Flin = 63.7; P < .001).
There was a significant linear trend for decreased AHI across age but no significant change between progressive ages (Table 1). AHI and AI could be 1 as a result of the presence of central apneas. There was wide variance among subjects, but higher AHI in the younger group was accounted for by central apneas, which were prevalent at almost twice the rate in younger children (Table 5).
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Although AHI showed developmental changes, the average SpO2 (97.7 [SD: ±0.72]), nadir (93.6–99.9), and desaturation indices did not differ among ages. At all ages, desaturation indices in these healthy children were several-fold higher in REM than NREM sleep (Table 6), and the frequency of desaturations to <95%, <90%, and <85% SpO2 was higher during REM than NREM (Table 7). Overall, the average percent of time subjects spent at <95% SpO2 was <1% (Table 8). There were no age differences for PETCO2 values, with the average TST value being 40.7. Twenty percent of the subjects spent
50% of TST at 45 mm Hg; 2.2% spent 50% of TST at 50 mm Hg (Table 9).
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A subgroup of children had screening questionnaires (N = 239) asking if they had asthma using a yes/no response option; 14% of this group were reported to have asthma. There were no significant differences between yes and no responders in either age group on any arousal measure or sleep architecture. Fifty-one younger children were available for analysis of SpO2 and PETCO2 measures. There were no significant differences between those children whose parents reported that they did or did not have asthma with regard to desaturation indices, average PETCO2, or percent TST when PETCO2 was >45 or >50 mm Hg. Those children reported to have asthma spent a lower percent of TST in SpO2 values of 96% to 100% and a higher percent of TST in SpO2 values of 91% to 95% (F = 15.2; P < .001, each); children reported as having asthma spent 98.9% TST in SpO2 values of 96% to 100%, and those without asthma spent 99.9% TST in the same range.
Arousals did not differ among the various ages. Spontaneous and respiratory-related arousals were highest in stages 1 and 2 and higher in REM than SWS. Overall, arousals occurred every 6 minutes throughout the night (Table 10). A linear increase in SPS was found across ages, but sequential age differences were not found. Five percent of the subjects had SPS 0.25, with all but 3 of these children in the older group.
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The PLM index was higher in younger children and decreased linearly across ages, with significant changes from 5 to 6 years. These differences seemed to be accounted for by differences in REM, which were higher in younger children (Table 11).
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From 5 to 6 years of age, there was a change toward greater time spent supine sleeping with reciprocal changes in side sleeping, both of which changed linearly across ages (Table 12). The percentage of time sleeping in the prone position did not change across age (Table 1).
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AHI during side sleeping in REM was higher in younger than in older children (Table 13).
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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These data comprise the first large-scale study providing reference values for normal sleep in children ages 3 to 7 years and the first to analyze developmental changes across this relatively narrow age range. Developmental analysis was particularly important because significant individual differences were found throughout the analyses; SDs were higher than means on most measures. Tables 14–16 summarize the present findings and those previously reported for comparable age ranges.
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TST in the present cohort was higher than that reported for several other sleep laboratory–based studies26,28,30,34,42 and less than the sleep duration recorded when the studies were conducted in the home.31,33 Sleep efficiency was comparable to that reported by other studies in a laboratory setting30,34,42,50 but lower than the sleep efficiency during home recordings.31,33 Sleep latency was twice as long among our subjects compared with that found in the Tucson Children’s Assessment of Sleep Apnea (TuCASA) study,33 but only marginally longer when compared with the other home-based study by Stores et al31 and the laboratory-based study by Coble et al.26 We were unable to find previous reference values for REM latency. There is variance among previous reports for sleep stage percents26,30,31,34,42,50; those in the present study were particularly distinct from those found in the TuCASA study,33 which found higher stage 2 and lower SWS percentages, and the SWS values reported by Traeger et al.50
Consistent with Coble et al,26 we found a lengthening of REM periods across the first 4 cycles of the night, and compared with that group’s values for 6- to 7-year-old children, the present study reports the exact same number of REM/NREM sleep cycles, and REM period and REM/NREM cycle lengths are similar to within a few minutes.
Although some children with parentally reported risk for SDB were included in the present data, their average obstructive AI and variance is 3 times lower than that reported by Marcus et al,28,50 whereas the central AI reported here is several times higher.28 We suspect that the more narrowly focused age range and inclusion of a larger group of younger children may underlie the difference in central apneas, especially because there was a significantly higher central AI among the younger compared with older children in our sample. Only 1 published study was available for comparison of SpO2 baseline with a similar age group30 and was nearly identical to that reported here for older children. In studies encompassing slightly older children,35 the oxyhemoglobin saturation values were also similar to those reported here.
Decreased AHIs were observed across age and paralleled the changes in sleep position throughout the night. We postulate that the decrease in central AIs with advancing age reflects a subtle process of maturation of the central nervous system. It is interesting to note that the decrease in central apnea with age accounted for the age-related decrease in AHI.
The lowest median TAI reported in similar-aged children was 4 per hour of TST in children 6 to 7 years of age,33 and the highest was 9 per hour of TST in children 5 to 7 years of age.31 Both of these values were derived from home-based studies. Laboratory-based studies have found median arousal indices ranging from 5 to 8 per hour of TST,30,34,42,50 which was consistent with the present report in which the higher respiratory-related arousal indices were recorded in NREM sleep stages 1 and 2 as opposed to REM sleep. However, it was during REM sleep that most of the respiratory events occurred. These findings therefore may reflect differential state-dependent arousal thresholds in these ages. SPS is a new numeric algorithm38 for intrapolysomnographic assessment of sleep disruption and resultant sleep pressure by respiratory disturbances. This study not only expands the normative reference values for SPS, but also emphasizes the use of this measure as a practical and meaningful assessment of sleepiness in young children, considering the markedly small fraction of subjects in this healthy group with SPS .25.
Several limitations inherent to this study deserve mention. First, the younger age group in the study was recruited predominantly from a low-income population. Although minority children who disproportionately make up this population are known to suffer more frequently from SDB, we are not aware of any studies showing either polysomnographic differences among normative children in high- versus low-income groups or of any showing differential response to polysomnography (first-night effects) between these groups. Although the younger group contained a higher proportion of black children, the effect sizes of the stage 1 and state 2 percent differences between these groups were trivial. We did find maternal education differences, which may be considered a proxy for socioeconomic status, on 3 measures. Effect size for latency to REM differences was small, but those for number of awakenings after sleep onset and stage 4 percent were large. Additional research into these effects may be warranted, but because only REM latency differed between our younger low-socioeconomic-status and older community children, these differences cannot be considered to alter the generalizability of the current report.
Second, the accuracy of using a nasal cannula to measure ETCO2 has been questioned by a number of studies.43–45 Biologic factors such as tidal volume and respiratory rates as well as mechanical factors (diameter and length of the cannula and diameter of prongs) may influence the reliability of ETCO2 measurements. In addition, when compared with partial pressure of carbon dioxide values, studies in children have found that mouth breathing, airway obstruction, oxygen delivery through the ipsilateral nasal cannula, and cyanotic heart disease adversely affect the accuracy of ETCO2 measurements. In pediatric subjects without these factors, however, ETCO2 correlated well with partial pressure of carbon dioxide measurements. We now provide more extensive characterization of the anticipated normative distribution of ETCO2 in sleeping children, which are remarkably higher than previously reported.28,30
Aside from a 1% difference in the percent of time spent in SpO2 range from 96% to 100%, we were unable to find differences on any cardiopulmonary measure between children whose parents report that they had asthma. However, parent report of asthma did not specify that the child had been clinically diagnosed with asthma or whether they were currently undergoing treatment for this condition.
Finally, although statistically significant, the effect sizes for gender and ethnicity differences were quite small, and therefore gender-based correction seems unnecessary at this time. Of note, the lack of gender and ethnicity differences is consistent with work by Quan et al.33
With the relative growth of research activity focusing on pediatric sleep, multiple methods for recording sleep measures have been developed, including actigraphy and motility monitoring as well as polysomnography.46 The "first-night" effects of polysomnographic monitoring on sleep characteristics are well-documented in adults, and although SDB indices do not seem vulnerable to first-night effects,47 the influence of a single night of disrupted sleep in children remains unknown. We restricted the analysis of the current data set to polysomnographic studies in which at least 6 hours of sleep were documented. Nevertheless, the percentage of overnight assessment excluded based on this criterion was <1%, suggesting that the normative parameters delineated in this study are commensurate with and applicable to the clinical setting. Although it is possible that transition to home-based polysomnographic testing may occur in the near future such as to facilitate access to more timely diagnosis and care in children, it is reassuring that many of the polysomnographic measures from our laboratory-based study are similar to those recorded in the home, suggesting that the reference values delineated here should continue to serve as discriminatory guidelines for the presence of sleep disorders in children.
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ACKNOWLEDGMENTS |
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This work was supported in part by US Department of Education grant H324E011001, Centers for Disease Control and Prevention grant E11/CCE 422081–01, and National Institutes of Health grants HL-62570 and F32 HL-074591, the Children’s Foundation Endowment for Sleep Research, and the Commonwealth of Kentucky Challenge for Excellence Trust Fund.
We thank the parents and children who participated in the study. Jennifer Bruner, RPSGT, Carrie Klaus, RPSGT, and Nigel Smith, PSGT, performed polysomnographic recordings; Cheryl Holbrook, MAT, RPSGT, provided logistic support; and Karen Hawkins, RPSGT, R EEG/EP T, and Mike Longman, RPSGT, RRT, of Medcare Systems created the customized software-derived algorithms used to generate the polysomnographic reports.
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FOOTNOTES |
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Accepted Nov 17, 2005.
Address correspondence to David Gozal, MD, Kosair Children’s Hospital Research Institute, University of Louisville School of Medicine, 570 S Preston St, Suite 204, Louisville, KY 40202; E-mail: david.gozal{at}louisville.edu
The authors have indicated they have no relationships relevant to this article to disclose.
* The respiratory disturbance index (RDI) has been operationally defined in several different ways. For example, 1 study defined RDI as an index different than the AHI (which was referred to as the "AHT"),48 whereas another study defined RDI as being synonymous with the AHI.49 To avoid confusion, the term RDI was not used here, and AHI refers to the number of apneas and hypopneas per hour of sleep. 
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