Abstract
Introduction
Obstructive sleep apnea (OSA) is a public health problem that affects children. Although continuous positive airway pressure (CPAP) therapy is effective, the CPAP adherence rate in children is varied. This study aimed to evaluate the CPAP adherence rate and factors associated with CPAP adherence in children with OSA using a systematic review.
Methods
The inclusion criteria were observational studies conducted in children with OSA and assessed adherence of CPAP using objective evaluation. The literature search was performed in four databases. Meta-analysis using fixed-effect model was conducted to combine results among included studies.
Results
In all, 34 studies that evaluated adherence rate and predictors of CPAP adherence in children with OSA were included, representing 21,737 patients with an average adherence rate of 46.56%. There were 11 calculations of factors predictive of CPAP adherence: age, sex, ethnicity, body mass index, obesity, income, sleep efficiency, the apnea-hypopnea index (AHI), severity of OSA, residual AHI, and lowest oxygen saturation level. Three different factors were linked to children with adherence and non-adherence to CPAP: age, body mass index, and AHI.
Conclusion
The CPAP adherence rate in children with OSA was 46.56%. Young age, low body mass index, and high AHI were associated with acceptable CPAP adherence in children with OSA.
Keywords: age, body mass index, apnea-hypopnea index
Introduction
Obstructive sleep apnea (OSA) is a public health problem in children. A review reported a prevalence rate of pediatric OSA that varied among countries with a range of 0.1% (Singapore) to 13.0% (Italy).1 A report from China found that the prevalence of OSA may be as high as 63.9% of 1578 children between 2 and 15 years of age with snoring.2 OSA is reported to be more common in children with certain conditions such as obesity, allergic rhinitis, or Down syndrome.3–6 The prevalence of OSA among children with obesity was 4.9 times higher than among non-obese children (44.6% vs 9.1%; p = 0.0002).6 If left untreated, OSA in children increases their risks for neurocognitive dysfunction, abnormal behavior, and cardiovascular diseases such as hypertension or left ventricular hypertrophy.7,8
Continuous positive airway pressure (CPAP) is recommended for children with OSA if adenotonsillectomy is not performed or OSA is unresolved after this surgery.9 A prospective study among children with OSA found that CPAP provided immediate and sustained improvement of OSA over a one-year period.10 Scores on the Epworth Sleepiness Scale improved significantly at one year from baseline (14.44 to 7.86; p = 0.001).10 However, information regarding the cardiovascular impact of OSA in children is lacking and evidence of CPAP benefits is quite limited.
A known problem with CPAP therapy is adherence. A previous systematic review of 46 included studies found that the average adherence rate from 20 studies (1079 participants) was 56.9% (range of 24–87%). Note that, for this systematic review, the average age of participants was 9.4 years with a range of 0.5–16 years; 34 studies had participants with developmental delays. Several factors were found to be associated with poorer CPAP adherence, including age (five studies), sex (two studies), race (one study), body mass index (BMI) (one study), and developmental delay (two studies).11 However, this systematic review was reported as a scoping review without summarized calculation according to meta-analysis style.
Another meta-analysis on this topic published in 2020 with the final search in 2019 identified five factors calculated as predictors of CPAP adherence in children with OSA.12 Studies conducted on non-invasive ventilator or a randomized controlled trial were included, resulting in an ability to report the CPAP adherence rate for children with OSA.13,14 Additionally, the adherence rate was not reported. As the previous meta-analysis did not report the CPAP adherence rate with a gap of three years and limited predictors, this study aimed to evaluate the adherence rate and add more calculations regarding predictors of CPAP adherence in children with OSA by updating the search.
Methods
This was an update to a meta-analysis study to evaluate factors predictive of adequate CPAP adherence in children with OSA. The study was registered at PROSPERO (ID: CRD42022346132).
Eligibility Criteria
Population
This study included studies conducted among children with OSA. It evaluated CPAP adherence using objective evaluation regardless of types of CPAP device, CPAP company, adherence criteria, inclusion criteria, or other factors such as age, sex, or co-morbid diseases. The CPAP devices used to treat OSA in children were standard CPAP, automatic CPAP (APAP), or bilevel positive airway pressure (BPAP). The details regarding each device have been reported elsewhere.15
Intervention and Control Group
This study did not evaluate any type of intervention to assist CPAP use in children with OSA. We mainly evaluated personal and laboratory factors associated with CPAP adherence in children with OSA who experienced CPAP therapy. Those who adhered to CPAP therapy (studied group) were compared with those who did not (control group). The diagnosis of OSA is based on polysomnography with an apnea-hypopnea index (AHI) of 1/hour or higher.2 During polysomnography, the lowest oxygen saturation level while asleep was reported as a percentage, and sleep efficiency percentage was reported defined as the total sleep time divided by the total time in bed during polysomnography. After treatment with CPAP, adherence data were downloaded either from the device or cloud technology.
Outcomes
The outcomes of the study were overall CPAP adherence rate and factors predictive of adequate CPAP adherence in children with OSA.
Study Types
The study types included in this study were any observational studies. Those studies with intervention, stimulation, no adherence data, non-CPAP or non-BPAP, non-objective data on adherence were excluded as were case reports/case series, commentaries, books, abstracts, and reviews.
Search Strategy
Four databases were used for systematic searching including PubMed, Central database, Scopus, and CINAHL Plus. Hand-searching for related studies was also conducted. Search terms included OSA, adherence, CPAP, and bilevel positive airway pressure (BPAP/BiPAP). The full list of search terms is shown in the Appendix. The final search was conducted on August 31, 2022.
Selection Process
After the removal of duplicates, initial screening was conducted for non-relevant articles. Only pediatric studies were eligible and were included if factors predictive of adequate CPAP adherence were evaluated. The initial screening process was performed independently by two authors (BS, KS). Studies selected by each reviewer were compared and entered in the full-text review process. Data extraction and full-text reviews were performed independently by two authors (BS, KS) using a priori form. Any disagreements between the authors were reviewed and decided by a third reviewer (CN). A Prima flowchart of article searching and included studies is illustrated in Figure 1.
Figure 1.
A Prima flowchart for adherence rate and predictors adherence of a positive airway pressure machine (CPAP) in pediatric patients with obstructive sleep apnea.
Data Collection
Data collection for each included study was retrieved for the following: publication characteristics, study characteristics, outcome characteristics, and predictor characteristics. The publication characteristics comprised the first author, year of publication, and country of study origin, while study characteristics comprised the study aim and design; age, sex, comorbidities (if any), and AHI; and duration of CPAP use. The outcome characteristics included method of assessment of CPAP adherence, definition of adherence, numbers of total cases, numbers of cases in adherence group, and numbers of cases in non-adherence group. For predictor characteristics, numbers and percentages of each potential predictor in the adherence and non-adherence groups were tabulated.
Data Analysis
There were two groups in this study: an adherence and a non-adherence group. Differences between both groups were computed for CPAP adherence. Mean differences with 95% confidence interval (CI) of numerical factors between both groups were calculated, while odds ratio with 95% CI of categorical factors between both groups were executed. I2 statistic was used to measure heterogeneity among the results of included studies. Substantial heterogeneity was identified when I2 was greater than 75%. Fixed-effect model was used to perform meta-analysis when I2 was less than 75%. Random-effects model was used to combine the results of included studies when substantial heterogeneity was found. A forest plot of each factor was created to show differences between both groups. All analyses were performed by Review Manager 5.4 (RevMan).
Risk of Bias
Study quality of observational studies including retrospective, prospective, and cross-sectional studies was evaluated using the Newcastle-Ottawa Scale (NOS), which was adapted for cross-sectional studies and applied to evaluate study quality.16 Details of the scale have been described elsewhere.16,17 Study quality is categorized as very good, good, satisfactory, and unsatisfactory. Risk of bias was performed by two authors independently (BS, CN). A disagreement between authors was reviewed and decided by a third reviewer (KS).
Results
There were 232 studies related to adherence to CPAP in patients with OSA after duplicate studies were removed (Figure 1). Of these, 50 were eligible for a full-text review and 16 of them were excluded because they were intervention studies (nine), had no objective adherence assessment (two), were based on non-invasive ventilation (two), were non-OSA (one), had no objective adherence data (one), or were conducted with adults (one). Thus, 34 studies remained that evaluated adherence in children with OSA; these were published from 2011 to 2022 (Tables 1–4).18–51 Of these 34, 12 studies had compared those who were adherent with those who were not and had applied a meta-analysis calculation (Tables 3 and 4).40–51 Most of the 34 studies were conducted in North America (23 of 34 studies) and had a retrospective study design (20 of 34 studies). The longest duration for CPAP adherence evaluation was four years22 with an average duration of 10.04 (SD 9.82) months.
Table 1.
Characteristics of Pediatric Studies with Obstructive Sleep Apnea (OSA) on Compliance of a Continuous Positive Airway Pressure Machine (CPAP): Included Studies but Not in a Meta-Analysis
| Study | Year | Country | Study Aim | Study Design | Age | Male Sex/Total | Comorbidities | AHI (events/hour)a | Factors of Interest |
|---|---|---|---|---|---|---|---|---|---|
| Bhattacharjee18 | 2020 | USA | Find adherence patterns of CPAP therapy | Cross-sectional | 4–18 years | NA | NA | NA | Age group, residual apnoea–hypopnoea index, use and onset of patient engagement programmes, PAP pressure, and nightly median PAP mask leak |
| Blinder19 | 2021 | Canada | Factors associated with CPAP adherence | Retrospective cohort | 8–17 years | 80/104 (77%) | Developmental delay, obesity, asthma, mental health disorder, behavioral disorder | 11 (5–23) | age, sex, SDB diagnosis, CPAP mode, comorbidities, PSG indices, sleep symptoms |
| Cielo20 | 2021 | USA | Compare CPAP in infant vs school-age children | Retrospective | 6 months-10 years | 95/150 (63.3%) | Obesity, craniofacial abnormality, neurologic abnormality, Down syndrome | 25.7 (17.8–35.9) infant vs 12.1 (7.6–21.5) school-aged | Infant vs school-aged |
| Castorena-Maldonado21 | 2008 | Mexico | Preoperative compliance | Prospective | 4–7 years | 37/48 (77%) | Obesity | RDI 63 (50–80) | Body weight, type of CPAP |
| Com22 | 2015 | USA | Characters and surgical outcomes | Retrospective cohort | NA | 87/143 (61%) | Obesity | 17.7 (8.3–37.2) | AHI, PSG parameters |
| DiFeo23 | 2012 | USA | Factors associated with CPAP adherence | Prospective | 2–16 years | 38/56 (68%) | Genetic syndrome, central nervous system abnormality, craniofacial syndrome, pulmonary disease, growth hormone deficiency, neurodevelopmental disability | 19 (16) | Age, sex, obesity, AHI, CPAP mode, maternal education, race, developmental delay, social support, child behavior, parental stress, nasal symptoms |
| Elder24 | 2022 | New Zealand | Long term CPAP outcome | Retrospective | NA | 52/70 (70%) | OSA (82%), obesity, Down syndrome, craniofacial abnormality, cerebral palsy, chromosomal abnormality, nasal obstruction, muscular dystrophy, Prader-Willi syndrome, CNS lesion, asthma, chronic lung disease, subglottic stenosis | NA | Periods of offered CPAP |
| Kang25 | 2019 | USA | Factors associated with CPAP adherence in children with developmental disabilities | Retrospective | 0–18 years | 141/240 (58.8%) | Developmental disabilities | 18 (10.9–51.8)*, 12.9 (9.4–18.5)** | Developmental disabilities |
| Katz26 | 2017 | Canada | Prevalence of cardiometabolic disease, CPAP and cardiometabolic markers | Prospective | 8–16 years | 22/27 (81.5%) | Obesity | 15.5 (7.4–25.2) | BMI, CPAP mode |
| Machaalani27 | 2016 | Australia | Factors associated with CPAP/BPAP adherence | Retrospective | 0–18 years | 63/99 (63.6%) | Chromosomal syndrome, neuromuscular, lung disorder, central nervous system | 21.2 (21.5) BPAP; 24.9 (22.8) CPAP | Age, gender, BMI, AHI, duration of CPAP, CPAP pressure |
| Marcus28 | 1995 | USA | Safety and efficacy of CPAP | Retrospective | NA | 64% | Obesity, craniofacial anomalies, Down syndrome, neuromuscular disease, mental retardation/cerebral palsy, pharyngeal flap surgery, Arnold-Chiari malformation | NA | Age, CPAP device, frequent clinic visit, professional counseling, parental neglect |
| Mulholland29 | 2021 | Australia | CPAP adherence in children between remote APAP vs fixed CPAP | Retrospective | > 3 years | 67/87 (77.0%) | Down syndrome, cerebral palsy, craniofacial syndromes | 14.3 (9.5–34.9) CPAP; 23.3 (13.3–52.0) APAP | APAP vs CPAP |
| O’Donnell30 | 2006 | Canada | Compliance rate | Retrospective cohort | 6 months-18 years | 67/79 (84.8%) | Down syndrome, developmental delay, obesity, cerebral palsy, Pierre-Robin syndrome, tumor, seizure, neuromuscular disease, Tetralogy of Fallot, Klippel-Feil syndrome, Marfan syndrome, bipolar disorder, severe asthma | 11.2 | Behavioral modification and parental commitment |
| Perriol31 | 2019 | France | Ambulatory CPAP and its adherence | Prospective | NA | 59/78 (75.6%) | Neurocognitive dysfunction | 12.2 (10.6) | Age, AHI, education level, neurocognitive disorders |
| Puri32 | 2016 | USA | Factors associated with CPAP adherence | Retrospective | < 18 years | 34/56 (60%) | Obesity, genetic syndrome, central nervous system abnormality, craniofacial syndrome, neurodevelopmental disability, attention deficit hyperactivity disorder, asthma, allergic rhinitis, post-adenotonsillectomy | 25.2 (28.7) | Family member on CPAP therapy |
| Ramirez33 | 2013 | France | Predictors of CPAP or NIV adherence | NA | 2–18 years | NA | OSA (82%), lung disease, neuromuscular disease | NA | Type of interface |
| Trucco34 | 2018 | UK | Types of sleep disordered breathing and adherence to respiratory support | Retrospective | < 18 years | 34/60 (56.7%) | Down syndrome | 11.1 (4.4–24.9) n = 42 | Oxygen therapy, CPAP, NIV |
| Uong35 | 2007 | USA | CPAP effectiveness and adherence in students | Retrospective | > 7 years | 26/46 (56.5%) | Obesity, Down syndrome, myelomeningocele, Asperger syndrome, autism, Chiari malformation, Prader-Willi syndrome, allergic rhinitis, cerebral palsy, developmental delay, fragile X, muscular dystrophy, otopalatodigital syndrome, Toureete’s syndrome | 28.4 (31.8) | AHI, CPAP mode, PSG parameters, OSA symptoms |
| van den Broek36 | 2021 | Netherlands | Adherence to CPAP in children with intellectual disabilities | Prospective cohort, subgroup | 1–18 years | 4/9 (44.4%) | Intellectual disabilities, autism spectrum disorder, attention deficit hyperactivity disorder, challenging behavior, epilepsy | 21.9 (18.6) | Age group |
| Weiss37 | 2021 | USA | Factors associated with CPAP adherence | Retrospective | 1 month-18 years | 152/250 (61%) | Down syndrome, developmental delay, obesity | 22.8 (28) IQR | BMI, AHI, CPAP setting, adenotonsillectomy |
| Willis38 | 2022 | USA | PSG titration and CPAP prescription; adherence of CPAP before and after PSG titration | Retrospective | NA | 77/121 (64%) | Obesity, allergic rhinitis, asthma, Down syndrome, attention deficit disorder, depression, gastroesophageal reflux, seizure, hypertension, developmental delay, Asperger syndrome, cardiac diagnoses, neuromuscular disease, cerebral palsy, Prader-Willi, diabetes, hypothyroidism, unspecified behavioral issue, anxiety, upper-airway anomalies, obsessive-compulsive disorder, spina bifida, adjustment disorder, eating disorder | 20.1 (11–34) | PSG titration |
| Xanthopoulos39 | 2017 | USA | Perceptions of OSA, self-efficacy on CPAP use | Retrospective | NA | 95/141 (67.4%) | Obesity, genetic disorder, craniofacial anomaly, CNS abnormality | 13.8 (7.1, 29.7) | Caregiver-reported and patient-reported perception, self-efficacy on CPAP use |
Notes: aMean (SD) or median (1st-3rd interquartile range: IQR); *Indicating children with developmental disabilities; **Indicating typically developing children.
Abbreviations: NA, not available; NAFLD, nonalcoholic fatty liver disease; MPS, mucopolysaccharidosis; NIV, non-invasive ventilator; BPAP, bilevel positive airway pressure; APAP, automatic CPAP; AHI, mean or median (IQR); SDB, sleep-disordered breathing; BMI, body mass index; RDI, respiratory disturbance index; PSG, polysomnography; CNS, central nervous system.
Table 2.
Characteristics and Outcomes of Pediatric Studies with Obstructive Sleep Apnea (OSA) on Compliance of a Continuous Positive Airway Pressure Machine (CPAP): Included Studies but Not in a Meta-Analysis
| Study | Devices | Data | Criteria for Adherence | Duration of CPAP Use | Total Cases, n | Adherence, n (%) |
|---|---|---|---|---|---|---|
| Bhattacharjee18 | AirSense 10 or AirCurve 10; ResMed | Airview: cloud technology | Usage >4 h/night for >70% of nights | 90 days | 20,553* | 9504 (46.3%) |
| Blinder19 | NA | Device download and clinic note | Use of >4 h/night for >70% of the nights | 6 months | 104* | 46 (51%) |
| Castorena-Maldonado20 | Resmed S7 Spirit or Americas | Device download | NA | 8–71 days | 48* | 26/48 (54%) 4 h usage |
| Cielo21 | Philips Respironics | Device download | Percentage of night usage >4 h | 63–110 days | 150 | 76.5% of nights (infants), 61.2% of nights (school-aged children) |
| Com22 | NA | Device download | Usage >4 h/night for >70% of nights | 4 years | 36* | 4 (11%) |
| DiFeo23 | Philips Respironics | Device download | Night used, and duration of used | 3 months | 56 | Night used 19 (9) night/month; duration 2.8 (2.7) h/night |
| Elder24 | NA | Clinical data | Usage >4 h/night for >70% of nights | NA | 74* | 25 (34%) |
| Kang25 | APAP, CPAP, BPAP of Philips Respironics | EncoreAnywhere web-based | Percentage of nights used, h of usage on nights used | 6 months | 177 | 73/81 (90%) for DD, 68/96 (70.7%) for TD |
| Katz26 | NA | Device download | Use of >4 h/night for >50% of the nights | 12 months | 16* | 11 (68.8%) |
| Machaalani27 | Philips Respironics; ResMed | Device download | Usage >4 h/night, 70% of nights | 12 months | 99; 55 CPAP, 44 BPAP* | 41/55 (75%) CPAP; 40/44 (91%) BPAP |
| Marcus28 | Varied | Device download | 50% of the hour prescribed | 1 year | 94 | 82 (87%) |
| Mulholland29 | RemStar Auto, Respironics; Fisher & Pakel or ResMed for fixed CPAP | Device download | Usage >4 h/night for >70% of nights | 90 days | 87* | 21/61 (34%) APAP; 7/26 (27%) fixed CPAP |
| O’Donnell30 | Aria LX, REMstar Pro | Device download | Day used and hour used | 46 months | 65 | 50 available: 76% used half night and 52% used 75% (26/50) |
| Perriol31 | NA | CPAP machine | Mean CPAP use, h | 24 month | 34 | 7.0 (2.7) h/night** |
| Puri32 | Philips Respironics | EncoreAnywhere | Hours of night used | 3 months | 56 | 4.8 (0.6) h: family; 3.8 (0.4) no family |
| Ramirez33 | VPAP IV & S9, ResMed; Trilogy 100, Respironics; ICON, Fisher & Paykel | Device download | Usage >8 h/night | NA | 62* | 45 (72%) |
| Trucco34 | NA | Device download | Usage >4 h/night for >50% of nights | 1.9 years | 18* | 7 (39%) |
| Uong35 | NA | Device download and parent report | Use of >4 hours per night of use and >5 nights per week | 18.1 months (average) | 27* | 19 (70.4) |
| van den Broek36 | NA | Device download | Use of >4 h/night for >70% of the nights | 8 months | 9* | 6 (66.7%) |
| Weiss37 | NA | Device download | Average hours and percentage of CPAP use | NA | 250 | 4.4 (5.8) h/night; 65.8% day used |
| Willis38 | NA | Device download | Usage >4 h/night | NA | 107* | 81 (76%) |
| Xanthopoulos39 | Philips Respironics | Device download | Average use >20 minutes/night | 1 month | 70 | 50 (79%), total used on days used (269.7 minutes) |
Notes: *For adherence rate; **At 24 months.
Abbreviations: NA, not available; APAP, automatic CPAP; BPAP, bi-level positive airway pressure machine; h, hours; DD, children with developmental disabilities; TD, typically developing children.
Table 3.
Characteristics of Pediatric Studies with Obstructive Sleep Apnea (OSA) on Compliance of a Continuous Positive Airway Pressure Machine (CPAP): Included Studies for a Meta-Analysis
| Study | Year | Country | Study Aim | Study Design | Age | Male Sex/Total | Comorbidities | AHI (Events/Hour)a | Factors of Interest |
|---|---|---|---|---|---|---|---|---|---|
| Alebraheem40 | 2018 | Canada | Find predictors and barriers for CPAP | Qualitative | 11–17 years | 13/21 (61.9%) | Obesity, Chiari malformation, renal transplant, sickle cell diseases, narcolepsy, NAFLD, MPS, mitochondrial disease | 14.1 (13.9) | Restriction of the tubing, the discomfort of the mask, and concerns with its size and weight |
| Avis41 | 2019 | USA | CPAP therapy and pedestrian injury | Prospective | 8–16 years | 17/29 (58.6%) | Obesity | 10.7 (15.0) | CPAP improved daytime functioning |
| Beebe42 | 2011 | USA | CPAP and academic function | Prospective | 10–16 years | 9/13 (69.2%) | Obesity | 10.0 (6.8)* 9.3 (5.7)** |
Academic grade, vigilance, academic quality of life |
| Hawkins43 | 2016 | USA | Factors associated with CPAP adherence | Retrospective | NA | 76/140 (52.3%) | NA | 20.3 (26.8)*, 16.5 (21.2)** |
Female sex and developmental delay |
| Katz44 | 2020 | Canada | Factors associated with CPAP adherence in children with obesity | Prospective, substudy | 8–16 years | 13/14 (92.9%) | Obese, moderate to severe OSA | 12.2 (7.1–36.4) | Age, AHI, lowest oxygen saturation, highest carbon dioxide |
| Lynch45 | 2019 | USA | Evaluated CPAP adherence on quality of life | Prospective, substudy | 8–16 years with caregiver | 16/25 (64.0%) | NA | 13.15 (19.58)*, 10.45 (7.62)** | CPAP adherence improved quality of life |
| Nathan46 | 2013 | Singapore | Factors associated with CPAP adherence | Retrospective | 8–13 years | 36/51 (70.6%) | Obesity, asthma, allergic rhinitis, large tonsils/adenoids, neurodevelopmental delay, chronic lung disease, cardiovascular disease, neuromuscular disease | 24.75 (6.7–53.8)*, 12.5 (5.7, 29.2)** |
Female gender and asthma |
| Nixon47 | 2011 | Australia | Factors associated with CPAP adherence | Retrospective | NA | 21/30 (70.0%) | Down syndrome, craniofacial syndrome, intellectual disability | NA | Usage in the first week |
| Prashad48 | 2013 | USA | Factors associated with CPAP adherence | Qualitative | 12–18 years | 15/21 (71.4%) | Obesity, developmental disabilities | 25.4 (24.6) | Adolescent and family experience |
| Roberts49 | 2016 | USA | CPAP use and facial change | Retrospective cohort | 0–18 years | 57/100 (57%) | NA | 17.61 (19.80)*, 12.03 (19.12)** | CPAP may change facial growth |
| Simon50 | 2012 | USA | Factors associated with CPAP adherence | Cross-sectional | 8–17 years | NA | Obesity | 22.59 (25.48)*, 14.63 (19.28)** | Age, OSA symptoms |
| Tovichien51 | 2022 | Thailand | CPAP versus APAP adherence | Prospective | NA | 17 (63%) | Obesity, Duchenne muscular diseases, allergic rhinitis, Down syndrome, asthma | 24 (19) | Age, sex, AHI, oronasal mask, lower maternal education, discomfort from CPAP, lack of clinical benefits |
Notes: aMean (SD) or median (1st-3rd quartile range); *Indicating adherent group; **Indicating non-adherent group.
Abbreviations: NA, not available; NAFLD, nonalcoholic fatty liver disease; MPS, mucopolysaccharidosis; APAP, automatic CPAP.
Table 4.
Characteristics and Outcomes of Pediatric Studies with Obstructive Sleep Apnea (OSA) on Compliance of a Continuous Positive Airway Pressure Machine (CPAP): Included Studies for a Meta-Analysis
| Study | Devices | Data | Criteria for Adherence | Duration of CPAP Use | Total Cases, n | Adherence, n (%) | Non-Adherence, n (%) |
|---|---|---|---|---|---|---|---|
| Alebraheem40 | NA | Device download | Usage >4 h/night for >50% of nights | 3–12 months | 14 | 6 (42.9%) | 8 (57.1%) |
| Avis41 | NA | Device download | Usage >4 h/night | 3 months | 20 | 11 (55%) | 9 (45%) |
| Beebe42 | NA | Parent report | Percent of sleep time with CPAP >21% | 4 months | 13 | 7 (53.8%) | 6 (46.2%) |
| Hawkins43 | NA | DME providers | Usage >4 h/night for >70% of nights | >1 year | 140 | 69 (49%) | 71 (51%) |
| Katz44 | CPAP (42.9%), BPAP (57.1%) | Machine download | Usage >4 h/night for >50% of nights | 1 year | 14 | 11 (78.6%) | 3 (21.4%) |
| Lynch45 | ResMed | Smartcards | Usage >4 h/night for >50% of nights | 3 months | 25 | 15 (60%) | 10 (40%) |
| Nathan46 | CPAP (92.2%), BPAP (7.8%) | Case note | >4 days/week | 6 months | 51 | 21 (41.2%) | 68 (37%) |
| Nixon47 | Fisher and Paykel Healthcare, ResMed | Device download | Use >1 h/night for >6 nights/week | 3 months | 30 | 10 (33.3%) | 20 (66.7%)* |
| Prashad48 | Phillips Respironics | Encore Pro2 | Tertiles of number of minutes used/night | 1 month | 14 | 7 (50%) | 7 (50%) |
| Roberts49 | Philips Respironics | EncoreAnywhere | Usage >4 h/night, 70% of nights | 6 months | 100 | 50 (50%) | 50 (50%) |
| Simon50 | APAP, CPAP, BPAP | Device download | Usage >4 h/night | 6 months | 49 | 19 (38.8%) | 30 (61.2%) |
| Tovichien51 | APAP, CPAP | Device download | Usage >4 h/night, 70% of nights | 1 year | 27 | 11 (40.7%) | 16 (59.3%) |
Note: *Indicating intermittent users.
Abbreviations: NA, not available; DME, durable medical equipment; APAP, automatic CPAP; BPAP, bi-level positive airway pressure machine; h, hours.
The adherence rate was assessed using data downloaded from CPAP devices either online or from the device in most studies, while CPAP adherence was defined by case notes, clinical data, parent report, or durable medical equipment (DME) providers as shown in Tables 2 and 4.24,42,43,46 The adherence criteria differed slightly among studies but was mostly defined as usage of four hours or more per night for 50–70% of nights. For the included studies without meta-analysis calculation (Tables 1 and 2), the adherence rate was 46.53% (9,883/21,240) from 13 studies based on usage of four hours or more per night (Table 2). The adherence rate for included studies for meta-analysis was 47.69% (237/497). In total, the adherence rate in children with OSA was 46.56% (10,120/21,737). The average CPAP usage in four studies (Table 2) ranged from 2.8 to 7.0 hours/night. Note that three studies compared clinical factors per specific group: developmental disabilities,25 family members with CPAP therapy,32 and age group.18 Additionally, one study compared those with CPAP adherence to intermittent users, who were categorized as non-adherent.47
Figure 4.
Calculation of ethnicity (Caucasian) of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.41–43,45
There were 11 calculations of factors predictive of CPAP adherence: age, sex, ethnicity, BMI, obesity, income, sleep efficiency, AHI, severity of OSA, residual AHI, and lowest oxygen saturation level (Figures 2-12). Three factors in regard to children who were adherent and those who were non-adherent with CPAP use were identified as age (Figure 2), BMI (Figure 5), and AHI (Figure 9). Children with OSA with effective adherence to CPAP use were younger and had lower BMIs than those with non-adherence. The mean differences (95% CI) of these two factors were −1.42 years (−2.07, −0.77) and −6.55 kg/m2 (−11.80, −1.30), respectively, as shown in Figures 2 and 5. Children with OSA with satisfactory adherence to CPAP had significantly higher AHIs than those who were non-adherent, with a mean difference of 4.75 events/hour (95% CI of 1.07, 8.43 events/hour) as shown in Figure 9. Regarding the quality of the included studies (Table 5), most had a score of satisfactory (8/10; 80%). Two studies were not evaluated due to non-cross-sectional study design.
Figure 2.
Calculation of age (years) of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.40–43,45,48,49,51
Figure 3.
Calculation of sex of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.40–49,51
Figure 5.
Calculation of body mass index (kg/m2) of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.40–42,45,49
Figure 6.
Calculation of obesity of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.40,46,48,51
Figure 7.
Calculation of income of family less than 20,000 USD of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.41,45
Figure 8.
Calculation of sleep efficiency (%) during polysomnography of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.41,49
Figure 9.
Calculation of apnea-hypopnea index (events/hour) of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.40,42,43,45,48–50
Figure 10.
Calculation of severe obstructive sleep apnea (OSA) of pediatric patients with OSA on adherence of a continuous positive airway pressure machine.43,45,46
Figure 11.
Calculation of residual apnea-hypopnea index (events/hour) of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.40,43
Figure 12.
Calculation of lowest oxygen level (%) during polysomnography of pediatric patients with obstructive sleep apnea on adherence of a continuous positive airway pressure machine.43,48,49
Table 5.
Study Quality Evaluation by the Newcastle-Ottawa Scale Adapted for Cross-Sectional Studies of the Included Studies of Pediatric Studies with Obstructive Sleep Apnea on Compliance of a Continuous Positive Airway Pressure Machine: Included Studies for a Meta-Analysis
| Study | Year | Study Design | Selection Process (4) | Comparability (2) | Outcome Measures (3) | Total (9) | Interpretation |
|---|---|---|---|---|---|---|---|
| Alebraheem | 2018 | Qualitative | - | - | - | - | NA |
| Avis | 2019 | Prospective | 3 | 0 | 3 | 6 | Satisfactory |
| Beebe | 2011 | Prospective | 2 | 1 | 1 | 4 | Satisfactory |
| Hawkins | 2016 | Retrospective | 3 | 0 | 3 | 6 | Satisfactory |
| Katz | 2020 | Prospective | 1 | 0 | 3 | 4 | Satisfactory |
| Lynch | 2019 | Prospective | 3 | 0 | 3 | 6 | Satisfactory |
| Nathan | 2013 | Retrospective | 4 | 0 | 3 | 7 | Good |
| Nixon | 2011 | Retrospective | 3 | 0 | 3 | 6 | Satisfactory |
| Prashad | 2013 | Qualitative | - | - | - | - | NA |
| Roberts | 2016 | Retrospective cohort | 3 | 0 | 3 | 6 | Satisfactory |
| Simon | 2012 | Cross-sectional | 1 | 0 | 2 | 3 | Unsatisfactory |
| Tovichien | 2022 | Prospective | 3 | 0 | 3 | 6 | Satisfactory |
Note: NA, not available due to qualitative study design.
Discussion
This meta-analysis showed that the adherence rate in children with OSA was 46.56% with CPAP usage of four hours or more based on 21,737 children with OSA. Age, BMI, and AHI were related to adequate CPAP adherence.
Compared with the previous meta-analysis, we summarized the adherence rate based on a large study population. Meta-analysis calculations were added for 11 factors, and we added 11 articles published after 2019 that were not included in the previous systematic review.12 After including more studies in the present meta-analysis, we found significant results in regard to age, BMI, and AHI on CPAP adherence in children with OSA (Figures 2, 5 and 9) and nearly significant results regarding sex with a 95% CI of 0.46, 1.00 (Figure 3). The previous meta-analysis was unable to declare significance in regard to age (not performed), BMI (difference −0.14; 95% CI: −0.47, 0.19), or AHI (difference 4.32; 95% CI: −0.61, 9.26).12 As previously noted, these three factors were significant predictors of CPAP adherence in this study. These differences between the two meta-analysis studies may be due to more studies included in the present analysis. However, we did not perform an analysis on CPAP vs BPAP, as in the previous meta-analysis, as we were unable to find appropriate data for calculation in the two studies.26,35 In our study, one study reported significant adherence between CPAP vs BPAP with an unadjusted odds ratio of 3.60 (2.39–5.10).46 However, this was not significant after being adjusted for other factors. Additionally, we were unable to analyze the developmental delay factor as data were not sufficient for the calculation. Only the study by Kang et al reported findings in regard to developmental delay, while the Hawkins study had incomplete data on this factor, resulting in the inability to calculate as a meta-analysis.25,43 Finally, this present study included only published articles, unlike the previous report, which included abstract articles.12
As previously reported, children with OSA who were younger were adherent with CPAP. The adherent group was −1.85 years younger than the non-adherent group. The younger patients may have had caregivers, usually their mother, who facilitated their CPAP treatment. By contrast, older children may have more barriers to wearing the CPAP device during sleep. A study conducted with adolescent patients with an average age of 13.2 years found that older children may have several barriers to CPAP use because they are away from home more often (47%); they may simply want to forget about having OSA (43.1%); they may not feel well (42.0%); or they may forget to use the device (39.2%).50 In addition, a lack of help from a parent or other caregiver to use CPAP at night represented another barrier in 31.4%.50
Similar to age, BMI was negatively correlated with CPAP adherence (Figure 5). In other words, those with lower BMI had better CPAP adherence than those with higher BMI. As shown in Figure 5, the significance of BMI was driven by the study by Lynch et al.45 A previous report found that high BMI was related to mask leakage (r = 0.579; p < 0.001) resulting in poor CPAP adherence.52 Obese children may have a higher risk for mask leakage, leading to poor adherence as high mask leakage was correlated with poor adherence (r = −0.376; p = 0.008).52
Regarding polysomnographic factors, only AHI was related to CPAP adherence in children with OSA. Previous studies in adult patients with OSA found that lowest oxygen saturation differed significantly between the adherence group and the non-adherence group (80.33% vs 85.18%; p value = 0.017) as well as duration of oxygen saturation lower than 90% (10.0% vs 5.0%; p = 0.013).53,54 Four studies showed that AHI was related to CPAP adherence in adult patients.53–56 Adult patients with oxygen desaturation index (ODI), an index comparable to AHI, of over 60 times/hour had a CPAP adherence rate of 90.8% at 10 years. The ODI or AHI also had an adjusted hazard ratio for CPAP adherence of 0.97 (p < 0.001). As previously reported,57 severity of OSA may be associated with more sleepiness, leading to better adherence to CPAP but not other polysomnographic factors such as oxygen level or sleep efficiency (Figures 11 and 12).
Above, we have discussed why AHI was associated with CPAP adherence in adults. In children, our analysis found that six of seven studies in the meta-analysis calculation had non-significant higher AHI in the adherent group than the non-adherent group; the study by Alebraheem et al was the exception (Figure 9). Additionally, another study identified non-significant higher AHI in the adherent group as well (20.5 vs 17.5 events/h; p 0.651).51 Note that the study by Tovichien et al was not included as they reported median AHI. After totaling up, the adherent group had greater significant value than the non-adherent group. Unlike in adults, further studies may be required to explain why AHI is significantly related to CPAP adherence. One study found that less severe OSA was related to low adherence to CPAP with a relative risk of 0.97.19
This study also added data about the adherence rate in children with OSA in a large sample (n = 22,075), which was not reported in the previous meta-analysis. The adherence rate was almost 50% but may be higher in specific populations, eg, 90% in children with developmental delays,25,43 87% in children with obesity or craniofacial anomalies,28 or in those using BPAP (91–100%).27,46 This adherence rate was slightly lower than that previously reported at 56.9%.11 These differences may be due to sample size numbers and included studies. The previous study had 1,079 participants from 20 studies, while this study had 22,075 participants from 34 studies.11 Several studies conducted with adults found that CPAP adherence rate ranged from 34% to 83%.58–62 Similar to children with OSA, there are several predictors of good adherence, such as age, sex, education level, and AHI.61–65
This study has several limitations. First, there are no standard criteria for CPAP adherence in children with OSA, resulting in different adherence criteria (Table 2). Second, some factors are not analyzed as they do not meet the calculation criteria for meta-analysis and data are not available for some populations, for example, Black children or children with asthma.18 Finally, interventions to facilitate CPAP adherence or OSA consequences or related conditions such as quality of life or cardiovascular diseases were not studied.66–72
Conclusions
Young age, low BMI, and high AHI are associated with satisfactory CPAP adherence in children with OSA. Although these factors were significant, the overall quality of evidence was fair, and the duration of study was generally about six months with no interventions. Further studies with longer durations or interventions should be considered. However, these factors may be helpful for clinicians in order to initiate CPAP therapy in potential patients for satisfactory adherence. Additionally, further interventions may be important for these groups of patients to facilitate CPAP adherence. Based on the large sample size, the CPAP adherence rate for children with OSA was 46.56%.
Funding Statement
This study was granted by Faculty of Medicine, Khon Kaen University, Thailand (Grant Number SY66001).
Patient and Public Involvement
Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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