The firstly visited department affects the acceptance of CPAP in patients with obstructive sleep apnea: A cohort study

Abstract

Background

Continuous positive airway pressure (CPAP) therapy is the first-line treatment for obstructive sleep apnea (OSA). However, the low acceptance rate of CPAP remains a challenging clinical issue. This study aimed to determine the factors that influence the acceptance rate of CPAP.

Methods

This retrospective cohort study was conducted at the sleep center of Shuang-Ho Hospital. Initially, 1186 OSA patients who received CPAP therapy between December 2013 and December 2017 were selected, and finally, 1016 patients were analyzed. All patients with OSA received CPAP therapy for at least 1 week, and their acceptance to treatment was subsequently recorded. Outcome measures included patients’ demographic and clinical characteristics (sex, age, BMI, comorbidities, history of smoking, and the medical specialist who prescribed CPAP treatment), polysomnography (PSG) results, and OSA surgical records.

Results

Patients with a lower CPAP acceptance rate were referred from otolaryngologists (acceptance rate of otolaryngology vs. others: 49.6% vs. 56.6%, p = .015), in addition to having a lower apnea–hypopnea index (AHI) (acceptance vs. non-acceptance: 55.83 vs. 40.79, p = .003), rapid eye movement AHI (REM-AHI) (acceptance vs. non-acceptance: 51.21 vs. 44.92, p = .014), and arousal index (acceptance vs. non-acceptance: 36.80 vs. 28.75, p = .011). The multiple logistic regression model showed that patients referred from otolaryngology had a lower CPAP acceptance rate (odds ratio 0.707, p = .0216) even after adjusting for age, sex, BMI, AHI, REM-AHI, arousal index, comorbidities, and smoking status.

Conclusions

Before their initial consultation, patients may already have their preferred treatment of choice, which is strongly linked to the type of medical specialists they visit, and consequently, affects their rate of acceptance to CPAP therapy. Therefore, physicians should provide personalized care to patients by exploring and abiding by their preferred treatment choices.

Keywords

Obstructive sleep apnea Continuous positive airway pressure Acceptance Shared decision making

Background

Obstructive sleep apnea (OSA) is a chronic condition caused by the episodic collapse and obstruction of the upper airway during sleep. The prevalence of OSA syndrome ranges from 3 to 7% among adult men and 2–5% among adult women in the general population [1]. Among OSA patients in the United States, 17% of men and 9% of women are 50‒70 years of age [2]. OSA patients have a higher risk of daytime sleepiness; cardiovascular, neurovascular, and metabolic disturbances; motor vehicle accidents; and decreased psychomotor speed [3–8]; however, many of them remain undiagnosed [9].

Several treatment options are available, including lifestyle changes [10, 11], oral appliance [12], pharmacological treatment [13], surgical treatment [14, 15], and continuous positive airway pressure (CPAP) therapy. CPAP therapy can prevent upper airway collapse and is the first-line treatment for OSA owing to its immediate effects and relatively lower complication rates [16–18]. CPAP reduces daytime sleepiness [19–22], has beneficial effects in cardiovascular and metabolic diseases [23–28], and improves health-related quality of life, mood, and neurocognitive function, especially in subgroups with higher treatment adherence [19, 29].

The efficacy of CPAP therapy is correlated with patients’ adherence [30, 31]. Nevertheless, low acceptance and adherence to CPAP therapy remain challenging issues in clinical practice. According to Yang et al. and Simon-Tuval et al., the acceptance rate of CPAP is approximately 40% [32, 33]. CPAP adherence is often defined as using the therapy for an average of 4 h per night for at least 70% of the nights monitored [34]. Studies have shown that 46–83% of patients are non-adherent to treatment [35, 36]. Further, a systematic review of 82 studies found that CPAP non-adherence affects at least one-third of treated patients and showed that CPAP adherence did not significantly improve in the past 20 years despite efforts in behavioral intervention and patient coaching [37].

Many studies have focused on CPAP adherence and its correlated factors, and have shown inconsistent results with factors including age, sex, race, severity of OSA, severity of symptoms, smoking status, and socioeconomic status [38–46]. However, only a few studies have mentioned factors related to CPAP acceptance [32, 33, 47], and the specialties of doctors who prescribe CPAP treatment are rarely discussed. Achieving shared decision-making depends on building good relationships in clinical encounters, so it is important for physicians to understand patients and share information. Therefore, we aimed to investigate and analyze the factors that cause a lower acceptance rate of CPAP in patients with OSA.

Methods

Data collection and sample

This retrospective cohort study initially included 1186 OSA patients treated with CPAP at least 1 week at Shuang-Ho Hospital (New Taipei City, Taiwan) between December 2013 and December 2017. Patients with symptoms of OSA underwent polysomnography (PSG) at Shuang-Ho Hospital, and those diagnosed with OSA were referred to the hospital's sleep center for CPAP treatment. Apart from otolaryngology, other outpatient departments, including pulmonology, cardiology, neurology, and psychiatry, also referred patients with OSA to the sleep center, and each patient received CPAP treatment for at least 1 week. In addition, patients were instructed on how to use the CPAP device correctly and followed up continuously for at least 3 months via telephone or mobile communication software by the sleep case manager of the sleep center to ensure adherence to the treatment regimen was achieved.

Eligibility criteria included patients aged 20 years or older, a record of CPAP use, and a diagnosis of OSA, which is defined as having an apnea–hypopnea index (AHI) of at least 5, based on the PSG records. Patients were excluded from the study if they were younger than 20 years of age, had an AHI of less than 5, had missing data on PSG records, or were referred from departments with fewer than 10 patient referrals to the sleep center.

The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Taipei Medical University (IRB Number: N202201092, 2022/4/14 of approval).

Assessment of baseline demographic and clinical characteristics

Patients’ medical history and clinical characteristics were comprehensively reviewed through their admission, outpatient, and surgical records, including their demographic characteristics (age, sex, and BMI), specialties of their referral doctors, smoking status, and presence of baseline comorbidities, including hypertension (HTN), diabetes mellitus (DM), cardiovascular disease (CVD), chronic kidney disease (CKD), hyperlipidemia (HLD), and chronic obstructive pulmonary disease (COPD). Cardiovascular disease was defined as a coronary artery disease or cerebrovascular accident. For patients who did not receive CPAP therapy, we tracked whether they had undergone OSA-related surgeries. OSA-related surgery was defined as uvulopalatopharyngoplasty (UPPP), uvulopalatal flap surgery (UPF), laser-assisted uvulopalatoplasty (LAUP), pharyngoplasty, tonsillectomy, adenoidectomy, septoplasty, submucosal resection of the nose, and submucosal turbinectomy.

Assessment of polysomnography results

PSG was conducted by certified respiratory therapists with at least 1 year of clinical experience. Standard monitoring devices included electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG) placed on the chin and bilateral anterior tibialis; position sensors, snore sensors, pulse oximetry, and thermistors to sense nasal and oral airflow; and plethysmography to sense thoracoabdominal movements.

PSG reports included the Epworth Sleepiness Scale (ESS) score, Pittsburgh Sleep Quality Index (PSQI), apnea–hypopnea index (AHI), rapid eye movement AHI (REM-AHI), non-rapid eye movement apnea–hypopnea index (NREM-AHI), sleep efficiency, mean oxygen saturation (mean SAT), mean heart rate (mean HR), arousal index, periodic limb movement index (PLMI), and pressure settings of the CPAP machine (90% pressure).

AHI was calculated as the number of apnea and hypopnea events per hour during sleep. REM-AHI was defined as the AHI during the rapid eye movement period. Sleep efficiency was defined as the ratio of the total sleep time to the total recording time. The 90% pressure was adjusted to eliminate 95% of apneas, hypopneas, and snoring to achieve an oxygen saturation of > 90% and AHI < 5 events/h in patients during REM sleep in a supine position.

Acceptance and non-acceptance

All enrolled patients commenced CPAP therapy for at least 1 week as a test trial. Patients who purchased the CPAP machine were defined as the acceptance group, and those who did not were placed in the non-acceptance group. For those who accepted CPAP, we further documented the date of purchase and for those who did not accept, we recorded the date they returned the CPAP device.

Statistical analyses

Continuous data were presented as mean ± standard deviation (SD) and tested by independent t test. Categorical data were presented as N (%) and tested by Chi-square test or Fisher's exact test. We further performed multivariate logistic regression to evaluate associations between factors chosen by this study and CPAP acceptance. Odds ratio (OR) and 95% confidence intervals (CI) were also calculated. Three models were established. Model 1 was adjusted by age, gender, and BMI. Model 2 was adjusted by covariates in Model 1, plus AHI, REM-AHI, and Arousal Index. Model 3 was adjusted by covariates in Model 2, plus HTN, DM, CVD, HLD, CKD, COPD, and smoking status. P value lower than 0.05 was regarded as statistically significant. Data were analyzed using SAS version 9.4 software (SAS Inc., Cary, NC, USA).

Results

Characteristics of the participants

A total of 1016 patients consisting of 825 men (81.2%) and 191 women (18.8%), mean age of 50.7 years, were enrolled for analysis (Fig. 1). A total of 546 (53.7%) patients accepted CPAP, and the baseline demographic and clinical characteristics of the acceptance group versus the non-acceptance group are shown in Table 1. We found that patients in the non-acceptance group were older (51.62 vs. 49.84, p = 0.031) and had lower body mass index (BMI) (28.54 vs. 29.33, p = 0.011). However, there were no significant differences in the baseline comorbidities between the two groups. Notably, the acceptance rate of CPAP therapy in patients referred by otolaryngologists was 49.6%, which was the lowest among all medical specialties who referred patients for CPAP treatment.

Fig. 1

Flow diagram of included patients. OSA obstructive sleep apnea, CPAP continuous positive airway pressure, AHI apnea–hypopnea index. *Specialty referring to < 10 patients: 2 from nephrology, 4 from physical medicine and rehabilitation

Table 1

Baseline demographic and clinical characteristics of the study participants

Characteristics	Acceptance N = 546	Non-acceptance N = 470	Univariate p value	Multivariate p value
Age, mean. (SD)	49.84 (12.9)	51.62 (13.2)	.031*	.143
Gender (male), %	83.3%	78.7%	.061	.114
BMI, mean. (SD)	29.33 (4.8)	28.54 (5.1)	.011*	.110
Smoking, %	31.9%	30.2%	.570	.814
Acceptance of specialties
Surgeon, ENT, no. (%)	206 (49.6%)	209 (50.4%)	.029*	.015*
Non-surgeon, no. (%)	340 (56.6%)	261 (43.4%)
PULMO no. (%)	209 (55.0%)	171 (45.0%)
NEURO, no. (%)	76 (58.5%)	54 (41.5%)
CV, no. (%)	41 (59.4%)	28 (40.6%)
PSY, no. (%)	14 (63.6%)	8 (36.4%)
Comorbidities
HTN, %	45.6%	44.9%	.820	.756
DM, %	17.4%	16.8%	.803	.619
CVD, %	22.0%	20.9%	.663	.597
HLD, %	30.4%	28.1%	.418	.576
CKD, %	2.0%	3.6%	.120	.115
COPD, %	6.0%	7.0%	.529	.659

Univariate p value: p value without any variable adjustment. Multivariate p value: p value with adjustment of the others variables listed in the table

SD standard deviation, BMI body mass index, no number, ENT otolaryngology, PULMO pulmonology, NEURO neurology, CV cardiology, PSY psychiatry, HTN hypertension, DM diabetes mellitus, CVD cardiovascular disease, HLD hypertension, CKD chronic kidney disease, COPD chronic obstructive pulmonary disease

The overnight PSG results of the acceptance and non-acceptance groups are shown in Table 2. Multivariable analysis revealed that the non-acceptance group had a lower AHI (40.79 vs. 55.83, p = 0.003), REM-AHI (44.92 vs. 51.21, p = 0.014), and arousal index (28.75 vs. 36.80, p = 0.011).

Table 2

Baseline polysomnography results of the study participants

Polysomnography, mean. (SD)	Acceptance N = 546	Non-acceptance N = 470	Univariate p value	Multivariate p value
PSQ	9.47 (3.8)	9.30 (3.8)	.615	.264
ESS	11.26 (5.5)	10.64 (5.3)	.208	.507
AHI	55.83 (24.5)	40.79 (23.5)	< .001	.003*
REM-AHI	51.21 (25.3)	44.92 (25.6)	< .001	.014*
NREM-AHI	53.86 (27.3)	38.76 (24.8)	< .001	.781
90% PRESSURE	10.87 (2.6)	10.02 (2.4)	< .001	.201
MEAN SAT	94.06 (3.0)	94.95 (2.5)	< .001	.808
MEAN HR	70.22 (9.9)	68.63 (9.3)	.009	.842
AROUSAL INDEX	36.80 (22.6)	28.75 (19.2)	< .001	*.011
SLEEP EFFICIENCY	75.56 (17.1)	75.39 (16.8)	.878	.384
PLM	2.69 (9.0)	3.15 (10.7)	.461	.575

Univariate p value: p value without any variable adjustment. Multivariate p value: p value with adjustment of the others variables listed in the table

SD standard deviation, PSQ Pittsburgh Sleep Quality Index, ESS Epworth sleepiness scale, AHI apnea–hypopnea index, REM-AHI apnea–hypopnea index during rapid eye movement sleep, NREM-AHI apnea–hypopnea index during non-rapid eye movement sleep, 90% PRESSURE average pressure measured during 90% of sleep, MEAN SAT mean blood oxygen saturation, MEAN HR mean heart rate, PLM periodic limb movement

Based on our results, the acceptance rate of CPAP therapy seemed to strongly correlate with the medical specialty that referred the patients for treatment. Hence, we performed a logistic model analysis to calculate the OR of otolaryngology patients who accepted CPAP (Table 3). In Model one, we adjusted the variables for baseline characteristics (sex, age, BMI). Model two was adjusted based on model one with the addition of PSG-related variables that significantly correlated with CPAP acceptance (AHI, REM-AHI, and arousal index). Model three was adjusted according to model two plus smoking status and presence of comorbidities (HTN, DM, CVD, HLD, CKD, and COPD). The OR was 0.707 (p = 0.0216, CI 0.526–0.950), which indicated that the odds of otolaryngology patients accepting CPAP treatment were significantly lower than those of non-otolaryngology patients, even after adjusting for all the factors above.

Table 3

Logistic model analysis to calculate the odds ratio of otolaryngology patients accepting CPAP

Variable	Odds ratio	p value	95% CI
Model 1	0.715	.0128	0.550‒0.931
Model 2	0.721	.0249	0.542‒0.960
Model 3	0.707	.0216	0.526‒0.950

Model 1 was adjusted for age, gender, BMI

Model 2 was adjusted for Model 1, AHI, REM-AHI and Arousal Index

Model 3 was adjusted for Model 2, HTN, DM, CVD, HLD, CKD, COPD and smoking

CI confidence interval

Analysis of otolaryngology versus other departments

Since patients referred from otolaryngology had a lower CPAP acceptance rate, we aimed to further analyze the differences between these patients and those referred from other medical specialties. The baseline demographic and clinical characteristics are shown in Table 4. We found that patients from the otolaryngology department were younger (46.97 vs. 53.22, p < 0.001), had lower BMI (28.49 vs. 29.29, p = 0.012), were more likely to be male (84.6% vs. 78.9%, p = 0.022), and had fewer smokers (24.8% vs. 35.4%, p < 0.001). Compared with other medical specialties, patients referred from otolaryngology also had significantly less comorbidities such as hypertension (30.4 vs. 55.6%, p < 0.001), diabetes mellitus (12.3% vs. 20.5%, p < 0.001), cardiovascular diseases (11.8% vs. 28.1%, p < 0.001), hyperlipidemia (21.9% vs. 34.4%, p < 0.001), and chronic obstructive pulmonary disease (1.2% vs. 10.1%, p < 0.001).

Table 4

Demographic and clinical characteristics of patients from otolaryngology versus other specialties

Characteristics	Otolaryngology N = 415	Others N = 601	Univariate p value
Age, mean (SD)	46.97 (11.8)	53.22 (13.3)	< .001
Gender (male), %	84.6%	78.9%	.022
BMI, mean (SD)	28.49 (5.0)	29.29 (4.9)	.012
Smoking, %	24.8%	35.4%	< .001
Comorbidities
HTN, %	30.4%	55.6%	< .001
DM, %	12.3%	20.5%	< .001
CVD, %	11.8%	28.1%	< .001
HLD, %	21.9%	34.4%	< .001
CKD, %	1.9%	3.3%	.180
COPD, %	1.2%	10.1%	< .001

The overnight PSG results of patients referred from otolaryngology versus patients referred from other specialties are shown in Table 5. We observed that patients referred from otolaryngology exhibited no significant difference in OSA severity compared to patients referred by other specialties, including variables that determine CPAP acceptance, such as AHI (p = 0.299), REM-AHI (p = 0.855), and arousal index (p = 0.080).

Table 5

Polysomnography results of patients from otolaryngology versus other specialties

Polysomnography, mean (SD)	Otolaryngology N = 415	Others N = 601	Univariate p value
PSQ	9.14 (3.6)	9.60 (3.9)	.194
ESS	10.76 (5.3)	11.13 (5.5)	.456
AHI	47.89 (26.1)	49.56 (24.5)	.299
REM-AHI	48.11 (25.7)	48.41 (25.6)	.855
NREM-AHI	47.36 (27.7)	46.54 (26.8)	.634
90% PRESSURE	10.37 (2.5)	10.55 (2.5)	.258
MEAN SAT	94.69 (2.8)	94.32 (2.8)	.039
MEAN HR	69.22 (9.6)	69.67 (9.7)	.469
AROUSAL INDEX	34.52 (22.8)	32.07 (20.5)	.080
SLEEP EFFICIENCY	77.08 (16.2)	74.38 (17.4)	.013
PLM	2.32 (8.7)	3.31 (10.5)	.102

SD standard deviation, PSQ Pittsburgh Sleep Quality Index, ESS Epworth Sleepiness Scale, AHI apnea–hypopnea index, REM-AHI apnea–hypopnea index during rapid eye movement sleep, NREM-AHI apnea–hypopnea index during non-rapid eye movement sleep, 90% PRESSURE average pressure measured during 90% of sleep, MEAN SAT mean blood oxygen saturation, MEAN HR mean heart rate, PLM periodic limb movement

From the analysis of the population accepting CPAP, it was found that patients referred by the otolaryngology department had a higher proportion of seeking surgical intervention compared to other departments. This difference is statistically significant, with percentages of 19.2% versus 7.5%, respectively (p < 0.001).

We further analyzed whether patients underwent OSA surgery after refusing CPAP therapy and discovered that a higher ratio of patients from otolaryngology underwent OSA surgery (20.6% vs. 6.5%, p < 0.001, Fig. 2). Subsequently, we combined the data of patients who underwent CPAP or OSA surgery as receiving treatment for OSA and found no significant difference between the two groups (Fig. 2).

Fig. 2

Ratios of accepting CPAP, receiving surgery, and acquiring treatment in otolaryngology patients versus other specialties. CPAP continuous positive airway pressure

According to the baseline demographic characteristics and PSG results of the patients who underwent OSA surgery, there was no significant difference between the groups (Table 6).

Table 6

Demographic characteristics and polysomnography results of patients receiving surgery after refusing CPAP therapy

	Otolaryngology N = 43	Others N = 17	Univariate p value
Characteristics
Age, mean (SD)	46.86 (11.08)	50.76 (12.58)	.241
Gender (male), %	81.4%	70.6%	.570
BMI, mean (SD)	28.32 (5.54)	28.70 (5.82)	.813
Smoking, %	27.9%	47.1%	.156
HTN, %	41.9%	47.1%	.714
DM, %	18.6%	5.9%	.400
CVD, %	14.0%	23.5%	.608
HLD, %	20.9%	41.2%	.203
CKD, %	2.3%	0.0%	.628
COPD, %	0.0%	0.0%	nil
Polysomnography, mean (SD)
PSQ	9.71 (3.94)	8.86 (2.34)	.593
ESS	10.50 (5.75)	10.43 (5.26)	.977
AHI	46.19 (25.19)	39.06 (21.28)	.308
REM-AHI	54.50 (29.46)	44.61 (27.81)	.277
NREM-AHI	44.99 (27.08)	35.33 (23.88)	.204
90% PRESSURE	10.12 (2.63)	10.06 (2.28)	.929
MEAN SAT	94.92 (1.82)	94.75 (2.60)	.776
MEAN HR	71.26 (10.37)	70.91 (9.62)	.906
AROUSAL INDEX	34.24 (22.54)	24.61 (14.82)	.110
SLEEP EFFICIENCY	74.79 (16.51)	71.12 (21.97)	.484
PLM	3.83 (17.00)	4.94 (14.27)	.814

SD standard deviation, BMI body mass index, HTN hypertension, DM diabetes mellitus, CVD cardiovascular disease, HLD hyperlipidemia, CKD chronic kidney disease, COPD chronic obstructive pulmonary disease, PSQ Pittsburgh Sleep Quality Index, ESS Epworth Sleepiness Scale, AHI apnea–hypopnea index, REM-AHI apnea–hypopnea index during rapid eye movement sleep, NREM-AHI apnea–hypopnea index during non-rapid eye movement sleep, 90% PRESSURE average pressure measured during 90% of the sleep, MEAN SAT mean blood oxygen saturation, MEAN HR mean heart rate, PLM periodic limb movement

Discussion

The present study showed that OSA patients with lower CPAP acceptance tend to be referred to the sleep center by otolaryngologists and had milder OSA severity (specifically lower AHI, REM-AHI, and arousal index), lower BMI, and older age. After excluding patients who initially visited the otolaryngology department, we found that patients from other specialties exhibited similar trends to the original data (Additional file 1: Table S1 and Table S2).

A total of 53.7% (546/1016) of patients accepted CPAP in the present study, which was substantially higher compared to the 40.1% in Simon et al. (n = 162, mean age 54.9 y/o) and 39.7% in Yang et al. (n = 315, mean age 56.7 y/o). This may be attributed to the younger age (50.7 y/o) of our cohort. Furthermore, instructions and assistance were provided to patients by the case manager of the sleep center via a mobile communication software on the correct use of the device in order to maximize their acceptance of CPAP treatment.

Previous studies have consistently demonstrated that greater OSA severity is associated with higher CPAP adherence [33, 38, 39, 42–44, 46, 48]. However, demographic characteristics, including sex, age, BMI, comorbidities, and smoking, showed inconsistent results [45]. In addition, we found that BMI and age significantly influenced acceptance of CPAP. Several studies have shown that higher ESS scores and more severe OSA symptoms are associated with higher CPAP adherence [33, 39–41, 44, 46]. Our results displayed similar trends but without statistical significance. Moreover, it is noteworthy that comorbidities and PLMI, which have not been discussed in previous studies, did not appear to be significant predictors of acceptance of CPAP.

We further compared the demographic and clinical characteristics of patients referred by otolaryngologists with those of patients referred from other specialties. Patients referred from otolaryngology had a lower BMI, fewer comorbidities, fewer smokers, and more young men; however, the PSG results were similar between the two groups (Tables 4, 5). According to previous studies, OSA severity is highly correlated with CPAP acceptance [33, 38, 39, 42–44, 46, 48]. Nevertheless, our study showed that otolaryngology patients had a lower acceptance of CPAP but demonstrated the same OSA severity as patients from other specialties (Table 5).

Similar to our results, Salas et al. showed that patients referred from otolaryngology had fewer comorbidities, lower BMI, and younger age, except for having the same OSA severity as other specialties [49]. Furthermore, after adjusting for multiple variables that may affect the CPAP acceptance rate, such as age, BMI, AHI, REM-AHI, and arousal index, the OR of CPAP acceptance in patients from otolaryngology was still significantly lower than that in patients from other departments (OR = 0.707, p = 0.0216, Table 3).

Three possible reasons may explain the disparity in patients’ choice of medical specialties for the treatment of OSA: the intention for surgery, surgical risks involved, and knowledge about the treatment.

First, our study showed that patients from otolaryngology who did not accept CPAP showed higher incidences of undergoing surgery afterwards than non-otolaryngology OSA patients (20.6% vs. 6.5%, p < 0.001, Fig. 2). Furthermore, we analyzed the ratio of OSA-related treatments, including CPAP or OSA surgery, and found no significant difference between patients from otolaryngology and those from other departments (p = 0.848, Fig. 2). This may indicate that both groups shared a similar intent for treatment, and a substantial portion of otolaryngology patients who refused CPAP treatment may have undergone surgery. In addition, we analyzed the AHI of the patients who underwent surgery and found no significant difference between the groups (p = 0.308, Table 6). This demonstrates that the two groups had similar indications for OSA surgery. In summary, patients’ intention to undergo surgery may contribute to their preference for otolaryngology clinics when seeking treatment for OSA. Second, our results were consistent with those of Salas et al., which showed that patients from otolaryngology had a lower risk of general anesthesia and surgical treatment [49]. Hence, we infer that patients’ surgical risks may affect their preference for specialties when they seek treatment. Third, previous studies showed that patients’ knowledge and beliefs about the disease affected their adherence to treatment [36, 50, 51]. Gulati et al. [52] pointed out that patients receiving surgery for OSA, compared to those who did not, had a higher ratio of independently doing research for OSA treatment prior to their consultation with the surgeon. This would explain why patients’ knowledge about the variety of treatment options plays a crucial role in their adherence and decision-making, which may also determine their choice of medical specialties.

Patients who visit different departments may have different treatment preferences for OSA. According to the principle of shared decision making [53, 54], increasing patient participation in discussions on treatment options may provide a better therapeutic effect.

Our study has several advantages. First, to the best of our knowledge, this is the first study to investigate the rate of acceptance of CPAP in patients referred by different specialties. Second, our sample size was relatively large (n = 1016) compared to previous studies. Third, we selected a more stringent definition of CPAP acceptance in our study, where “Acceptance” was defined as patients who purchased CPAP machines after their test trial, which is also the same definition adopted by Yang and Simon-Tuval [32, 33]. Compared to Rauscher et al., which only had a 3-day CPAP test trial [47], our definition of CPAP acceptance may better reflect the patients’ actual intent.

This study has several limitations. First, this was a retrospective cohort study; therefore, potential participants were excluded because of missing data, and the baseline data of the otolaryngology versus non-otolaryngology groups may be different. Second, few patients were referred from certain medical specialties. For example, patients from the Department of Psychiatry had a higher acceptance of CPAP compared to other specialties; however, no statistical significance was noted. Lastly, some patients in the non-acceptance group may purchase CPAP via alternative routes; as a result, this may have caused a selection bias. In order to minimize this bias, the medical records of the non-acceptance group were carefully examined during their subsequent visits to the hospital to ensure that no CPAP treatment had been initiated. Moreover, patients from the non-acceptance group were constantly followed up by the case manager of the sleep center regarding their subsequent treatment for OSA, and whether they received treatment elsewhere or if they were using other respiratory devices.

Conclusions

Shared decision making is crucial for achieving therapeutic success in clinical practice. In the sleep center, patients with OSA who initially visited otolaryngology clinics had a lower acceptance rate for CPAP. Therefore, besides adhering to clinical guidelines, physicians should provide individualized care through shared decision-making and identifying patients’ preferred treatment.

Footnotes

Acknowledgements

Not applicable.

Author contributions

Drs. Lin and Wu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. CSW: concept and design, statistical analysis, drafting of the manuscript. DHKC: drafting of the manuscript. YK: drafting of the manuscript. CHB: statistical analysis. PYC: administrative, technical, and material support. WTL: administrative, technical, and material support. YCL: concept and design, statistical analysis, acquisition, analysis, or interpretation of data, drafting of the manuscript, administrative, technical, and material support. All authors approved the final version of the manuscript for publication.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Availability of data and materials

The datasets analyzed in the current study are available from the corresponding author upon reasonable request.

Declarations

Abbreviations

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

Punjabi

The epidemiology of adult obstructive sleep apnea

Proc Am Thorac Soc 2008 5 2 136 143 18250205 2645248 10.1513/pats.200709-155MG

Peppard

Young

Barnet

Palta

Hagen

Hla

Increased prevalence of sleep-disordered breathing in adults

Am J Epidemiol 2013 177 9 1006 1014 23589584 3639722 10.1093/aje/kws342

Veasey

Rosen

Obstructive sleep apnea in adults

N Engl J Med 2019 380 15 1442 1449 30970189 10.1056/NEJMcp1816152

Engleman

Kingshott

Martin

Douglas

Cognitive function in the sleep apnea/hypopnea syndrome (SAHS)

Sleep 2000 23 Suppl 4 S102 S108 10893080

Jean-Louis

Zizi

Clark

Brown

McFarlane

Obstructive sleep apnea and cardiovascular disease: role of the metabolic syndrome and its components

J Clin Sleep Med 2008 4 3 261 272 18595441 2546461 10.5664/jcsm.27191

King

Cuellar

Obstructive sleep apnea as an independent stroke risk factor: a review of the evidence, stroke prevention guidelines, and implications for neuroscience nursing practice

J Neurosci Nurs 2016 48 3 133 142 27136407 10.1097/JNN.0000000000000196

Yaggi

Concato

Kernan

Lichtman

Brass

Mohsenin

Obstructive sleep apnea as a risk factor for stroke and death

N Engl J Med 2005 353 19 2034 2041 1:CAS:528:DC%2BD2MXht1SrtrnO 16282178 10.1056/NEJMoa043104

Connor

Whitlock

Norton

Jackson

The role of driver sleepiness in car crashes: a systematic review of epidemiological studies

Accid Anal Prev 2001 33 1 31 41 1:STN:280:DC%2BD3M7ksFOnsg%3D%3D 11189120 10.1016/S0001-4575(00)00013-0

Lindberg

Gislason

Epidemiology of sleep-related obstructive breathing

Sleep Med Rev 2000 4 5 411 433 1:STN:280:DC%2BD2s%2FhsFejtA%3D%3D 17210275 10.1053/smrv.2000.0118

10.

Peppard

Young

Palta

Dempsey

Skatrud

Longitudinal study of moderate weight change and sleep-disordered breathing

JAMA 2000 284 23 3015 3021 1:STN:280:DC%2BD3M%2FotVKjuw%3D%3D 11122588 10.1001/jama.284.23.3015

11.

Penzel

Möller

Becker

Knaack

Peter

Effect of sleep position and sleep stage on the collapsibility of the upper airways in patients with sleep apnea

Sleep 2001 24 1 90 95 1:STN:280:DC%2BD3M7pt1Cnug%3D%3D 11204057 10.1093/sleep/24.1.90

12.

Sutherland

Vanderveken

Tsuda

Marklund

Gagnadoux

Kushida

Oral appliance treatment for obstructive sleep apnea: an update

J Clin Sleep Med 2014 10 2 215 227 24533007 3899326 10.5664/jcsm.3460

13.

Hedner

Zou

Drug therapy in obstructive sleep apnea

Sleep Med Clin 2018 13 2 203 217 29759271 10.1016/j.jsmc.2018.03.004

14.

Kent

Stanley

Aurora

Levine

Gottlieb

Spann

Referral of adults with obstructive sleep apnea for surgical consultation: an American Academy of Sleep Medicine clinical practice guideline

J Clin Sleep Med 2021 17 12 2499 2505 34351848 8726361 10.5664/jcsm.9592

15.

Carvalho

Hsia

Capasso

Surgical therapy of obstructive sleep apnea: a review

Neurotherapeutics 2012 9 4 710 716 22915293 3480570 10.1007/s13311-012-0141-x

16.

Qaseem

Holty

Owens

Dallas

Starkey

Shekelle

Management of obstructive sleep apnea in adults: a clinical practice guideline from the American College of Physicians

Ann Intern Med 2013 159 7 471 483 24061345

17.

Patil

Ayappa

Caples

Kimoff

Patel SR Harrod

Treatment of adult obstructive sleep apnea with positive airway pressure: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment

J Clin Sleep Med 2019 15 2 301 334 30736888 6374080 10.5664/jcsm.7638

18.

Genta

Kaminska

Edwards

Ebben

Krieger

Tamisier

The importance of mask selection on continuous positive airway pressure outcomes for obstructive sleep apnea. An Official American Thoracic Society workshop report

Ann Am Thorac Soc 2020 17 10 1177 1185 33000960 7640631 10.1513/AnnalsATS.202007-864ST

19.

Labarca

Saavedra

Dreyse

Jorquera

Barbe

Efficacy of CPAP for improvements in sleepiness, cognition, mood, and quality of life in elderly patients with OSA: Systematic review and meta-analysis of randomized controlled trials

Chest 2020 158 2 751 764 32289311 10.1016/j.chest.2020.03.049

20.

Engleman

Martin

Kingshott

Mackay

Deary

Douglas

Randomised placebo controlled trial of daytime function after continuous positive airway pressure (CPAP) therapy for the sleep apnoea/hypopnoea syndrome

Thorax 1998 53 5 341 345 1:STN:280:DyaK1czntlGitQ%3D%3D 9708223 1745223 10.1136/thx.53.5.341

21.

Montserrat

Ferrer

Hernandez

Farré

Vilagut

Navajas

Effectiveness of CPAP treatment in daytime function in sleep apnea syndrome: a randomized controlled study with an optimized placebo

Am J Respir Crit Care Med 2001 164 4 608 613 1:STN:280:DC%2BD3MritVaksA%3D%3D 11520724 10.1164/ajrccm.164.4.2006034

22.

Monasterio

Vidal

Duran

Ferrer

Carmona

Barbé

Effectiveness of continuous positive airway pressure in mild sleep apnea-hypopnea syndrome

Am J Respir Crit Care Med 2001 164 6 939 943 1:STN:280:DC%2BD3MrmtF2jsw%3D%3D 11587974 10.1164/ajrccm.164.6.2008010

23.

Durán-Cantolla

Aizpuru

Martínez-Null

Barbé-Illa

Obstructive sleep apnea/hypopnea and systemic hypertension

Sleep Med Rev 2009 13 5 323 331 19515590 10.1016/j.smrv.2008.11.001

24.

Pepperell

Ramdassingh-Dow

Crosthwaite

Mullins

Jenkinson

Stradling JR

Ambulatory blood pressure after therapeutic and subtherapeutic nasal continuous positive airway pressure for obstructive sleep apnoea: a randomised parallel trial

Lancet 2002 359 9302 204 210 11812555 10.1016/S0140-6736(02)07445-7

25.

Becker

Jerrentrup

Ploch

Grote

Penzel

Sullivan

Effect of nasal continuous positive airway pressure treatment on blood pressure in patients with obstructive sleep apnea

Circulation 2003 107 1 68 73 12515745 10.1161/01.CIR.0000042706.47107.7A

26.

Cuhadaroğlu

Utkusavaş

Oztürk

Salman

Ece

Effects of nasal CPAP treatment on insulin resistance, lipid profile, and plasma leptin in sleep apnea

Lung 2009 187 2 75 81 19127383 10.1007/s00408-008-9131-5

27.

Comondore

Cheema

Fox

Butt

John Mancini

Fleetham

The impact of CPAP on cardiovascular biomarkers in minimally symptomatic patients with obstructive sleep apnea: a pilot feasibility randomized crossover trial

Lung 2009 187 1 17 22 18795367 10.1007/s00408-008-9115-5

28.

Labarca

Reyes

Jorquera

Dreyse

Drake

CPAP in patients with obstructive sleep apnea and type 2 diabetes mellitus: systematic review and meta-analysis

Clin Respir J 2018 12 8 2361 2368 30073792 10.1111/crj.12915

29.

Giles TL, Lasserson TJ, Smith BJ, White J, Wright J, Cates CJ. Continuous positive airways pressure for obstructive sleep apnoea in adults. Cochrane Database Syst Rev. 2006(1):Cd001106.

30.

Weaver

Maislin

Dinges

Bloxham

George

Greenberg

Relationship between hours of CPAP use and achieving normal levels of sleepiness and daily functioning

Sleep 2007 30 6 711 719 17580592 1978355 10.1093/sleep/30.6.711

31.

Bratton

Gaisl

Wons

Kohler

CPAP vs mandibular advancement devices and blood pressure in patients with obstructive sleep apnea: a systematic review and meta-analysis

JAMA 2015 314 21 2280 2293 1:CAS:528:DC%2BC28XntFSqu7s%3D 26624827 10.1001/jama.2015.16303

32.

Simon-Tuval

Reuveni

Greenberg-Dotan

Oksenberg

Tal

Tarasiuk

Low socioeconomic status is a risk factor for CPAP acceptance among adult OSAS patients requiring treatment

Sleep 2009 32 4 545 552 19413149 2663865 10.1093/sleep/32.4.545

33.

Yang

Lin

Lan

Huang

Lim

Factors affecting CPAP acceptance in elderly patients with obstructive sleep apnea in Taiwan

Respir Care 2013 58 9 1504 1513 23431309 10.4187/respcare.02176

34.

Kribbs

Pack

Kline

Smith

Schwartz

Schubert

Objective measurement of patterns of nasal CPAP use by patients with obstructive sleep apnea

Am Rev Respir Dis 1993 147 4 887 895 1:STN:280:DyaK3s3itFShtg%3D%3D 8466125 10.1164/ajrccm/147.4.887

35.

Cistulli

Armitstead

Pepin

Woehrle

Nunez

Benjafield

Short-term CPAP adherence in obstructive sleep apnea: a big data analysis using real world data

Sleep Med 2019 59 114 116 30799254 6589354 10.1016/j.sleep.2019.01.004

36.

Weaver

Grunstein

Adherence to continuous positive airway pressure therapy: the challenge to effective treatment

Proc Am Thorac Soc 2008 5 2 173 178 18250209 2645251 10.1513/pats.200708-119MG

37.

Rotenberg

Murariu

Pang

Trends in CPAP adherence over twenty years of data collection: a flattened curve

J Otolaryngol Head Neck Surg 2016 45 1 43 27542595 4992257 10.1186/s40463-016-0156-0

38.

Krieger

Kurtz

Petiau

Sforza

Trautmann

Long-term compliance with CPAP therapy in obstructive sleep apnea patients and in snorers

Sleep 1996 19 9 Suppl S136 S143 1:STN:280:DyaK2s7os12ntA%3D%3D 9122571 10.1093/sleep/19.suppl_9.S136

39.

McArdle

Devereux

Heidarnejad

Engleman

Mackay

Douglas

Long-term use of CPAP therapy for sleep apnea/hypopnea syndrome

Am J Respir Crit Care Med 1999 159 4 Pt 1 1108 1114 1:STN:280:DyaK1M3htVemsg%3D%3D 10194153 10.1164/ajrccm.159.4.9807111

40.

Pelletier-Fleury

Rakotonanahary

Fleury

The age and other factors in the evaluation of compliance with nasal continuous positive airway pressure for obstructive sleep apnea syndrome. A Cox's proportional hazard analysis

Sleep Med 2001 2 3 225 232 1:STN:280:DC%2BC2sbksVamtQ%3D%3D 11311685 10.1016/S1389-9457(00)00063-0

41.

Sin

Mayers

Man

Pawluk

Long-term compliance rates to continuous positive airway pressure in obstructive sleep apnea: a population-based study

Chest 2002 121 2 430 435 11834653 10.1378/chest.121.2.430

42.

Kohler

Smith

Tippett

Stradling JR

Predictors of long-term compliance with continuous positive airway pressure

Thorax 2010 65 9 829 832 20805182 10.1136/thx.2010.135848

43.

Campos-Rodriguez

Martinez-Alonso

Sanchez-de-la-Torre

Barbe

Long-term adherence to continuous positive airway pressure therapy in non-sleepy sleep apnea patients

Sleep Med 2016 17 1 6 26847966 10.1016/j.sleep.2015.07.038

44.

Jacobsen

Eriksen

Hansen

Erlandsen

Thorup

Damgård

Determinants for adherence to continuous positive airway pressure therapy in obstructive sleep apnea

PLoS ONE 2017 12 12 29253872 5734784 10.1371/journal.pone.0189614

45.

Mehrtash

Bakker

Ayas

Predictors of continuous positive airway pressure adherence in patients with obstructive sleep apnea

Lung 2019 197 2 115 121 1:STN:280:DC%2BB3cnovVKlsA%3D%3D 30617618 10.1007/s00408-018-00193-1

46.

Van Ryswyk

Anderson

Antic

Barbe

Bittencourt

Freed

Predictors of long-term adherence to continuous positive airway pressure in patients with obstructive sleep apnea and cardiovascular disease

Sleep 2019 42 10 zsz152 31587046 10.1093/sleep/zsz152

47.

Rauscher

Popp

Wanke

Zwick

Acceptance of CPAP therapy for sleep apnea

Chest 1991 100 4 1019 1023 1:STN:280:DyaK38%2Fgs1OhsA%3D%3D 1914550 10.1378/chest.100.4.1019

48.

Florés

Martinez-Alonso

Sánchezde-la-Torre

Aldomà

Galera

Barbé

Predictors of long-term adherence to continuous positive airway pressure in patients with obstructive sleep apnoea and acute coronary syndrome

J Thorac Dis 2018 10 Suppl 1 S124 S134 29445536 5803060 10.21037/jtd.2017.12.128

49.

Salas

Dreyse

Contreras

Nazar

Astorquiza

Cabezon

Differences in patients derived from otolaryngology and other specialties with sleep apnea

J Otolaryngol Head Neck Surg 2019 48 1 53 31640800 6805651 10.1186/s40463-019-0373-4

50.

Olsen

Smith

Oei

Douglas

Health belief model predicts adherence to CPAP before experience with CPAP

Eur Respir J 2008 32 3 710 717 1:STN:280:DC%2BD1crmvVOgug%3D%3D 18417510 10.1183/09031936.00127507

51.

Wild

Engleman

Douglas

Espie

Can psychological factors help us to determine adherence to CPAP? A prospective study

Eur Respir J 2004 24 3 461 465 1:STN:280:DC%2BD2cvltVSqsA%3D%3D 15358707 10.1183/09031936.04.00114603

52.

Gulati

Stephens

Cai

Chang

Characterizing decisional conflict in patients presenting to sleep surgery clinic and an exploration of resource limitations

Laryngoscope 2021 131 10 2384 2390 34152601 10.1002/lary.29695

53.

Stacey

Légaré

Lewis

Barry

Bennett

Eden

Decision aids for people facing health treatment or screening decisions

Cochrane Database Syst Rev 2017 4 4 Cd001431 28402085

54.

Elwyn

Frosch

Thomson

Joseph-Williams

Lloyd

Kinnersley

Shared decision making: a model for clinical practice

J Gen Intern Med 2012 27 10 1361 1367 22618581 3445676 10.1007/s11606-012-2077-6