In order to detect the presence or absence of clinical improvement after the end of the protocol, one of the authors not involved with the treatment Lucila Bizari Fernandes do Prado (LBFP) contacted the patients by telephone asking the following question: “Did your main complaint improve with the use of the IOMRA?” When the patient had difficulty understanding the question, the researcher provided a detailed explanation so that the patient could completely understand his/her health status possibly resulting from OSAS (snoring, fatigue, and sleepiness, or any other symptom that made the patient to see a sleep specialist). Twenty of the 188 patients did not show clinical improvement of the sleep related symptoms that they arrived with and comprised the Study Group. The Control Group consisted of 20 patients drawn among the remaining 168 patients with the same characteristics as the Study Group, except for the fact that they had reported clinical improvement of the sleep related symptoms after treatment. When more than one patient fulfilled the criteria of the control group the inclusion were done by drawing lots.
The two groups were matched for gender, age, body mass index (BMI), and apnea–hypopnea index (Table 1
Table 1Demographic data and apnea–hypopnea index pre- and post treatment showing no differences between groups.
In addition to signing a free informed consent form, the two groups were submitted at home to the Johns Hopkins questionnaire for the diagnosis of sleep disorders, the Beck Inventory for the evaluation of depression, the State-Trait Anxiety Inventory for the evaluation of anxiety, and the Epworth Sleepiness Scale for the assessment of excessive daytime sleepiness.
Reliability and factor analysis of the Epworth Sleepiness Scale.
Biaggio AMB, Natalino L. Manual para o Inventário de Ansiedade Traço-Estado (IDATE). Rio de Janeiro: Centro Educacional de Psicologia Aplicada—CEPA; 1979.
Validation of a Portuguese version of the Beck Depression Inventory and the State-Trait Anxiety Inventory in Brazilian subjects.
Inventário de depressão de Beck: propriedades psicométricas da versão em português.
- Beck A.T.
- Ward C.H.
- Mendelson M.
- et al.
An inventory for measuring depression.
All patients in this study provided information about their general health status through the one or more of the following: (1) chart review, (2) clinical questionnaire attached to the sleep questionnaire, (3) clinical interview, and (4) phone interview. We did not have problem with this research step, because patients that reported improvement were very determined in their opinion and those that felt that they did not improved also stated their opinion vigorously and took the chance to ask for new treatment options available. We choose a phone call (made by one research (LBFP) not involved directly with the treatment) to ask the patients if they felt improvement with IOMRAs treatment because we think this procedure allows more freedom to the patient to give his/her sincere opinion far from his/her health care professional.
Insomnia was diagnosed if the patients reported that they have problem of initiating or maintaining sleep for at least 5 days per week and for more than 6 months. We grouped in the category Gastric Disease those patients with heart burning, endoscopic diagnosis of hiatus hernia, gastritis, esophagitis, and other gastric related complaints; in the category Rheumatic Disease patients with osteoarthritis, and all sort of degenerative aging-related joint disease affecting mainly the knees, lumbar column and hip joint; in the category Neurological Disease patients with headache, migraine, vertigo complaints, history of seizure or epilepsy (we did not have patients with degenerative disease, stroke, cerebelar ataxia or any other severe neurological condition); in the category Psychiatric Disease patients in treatment for or with recent history of depression, anxiety, panic disorder or other psychiatric condition.
Variables like BMI, race, educational level, rotation shift, smoking, drinking, caffeine ingestion, medications, sedatives, insufficient sleep among others were obtained mostly throughout our sleep and clinical questionnaire, but also by chart review or personal interview as mentioned above. We did not use the phone interview to collect those data, because it would take too much time and also could ensue misinformation.
To be included in the study all patients should attend two essential conditions: (1) to have a clinical and laboratorial polysomnographic diagnosis of mild (5–15 events per hour), moderate (16–30 events per hour) or severe (more than 30 events per hour) OSAS or of increased upper airway resistance syndrome according to the criteria of the American Academy of Sleep Medicine.
The Report of an American Academy of Sleep Medicine Task Force
Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research.
; and (2) to have a follow up polysomnography at the end of IOMRAs’ titration showing less than 10 events per hour. The main complaints of these patients were snoring which disturbed the partner, fatigue, and excessive daytime sleepiness.
The IOMRAs were fabricated with two dental plates consisting of self-polymerizing acrylic resin, one for the upper arch and one for the lower arch with complete coverage of the occlusal sides of the teeth. A stainless-steel device containing a thread that permits millimeter adjustments connected the two plates in such a way that when activating the thread of the metallic device the lower plate was moved forward, promoting mandibular advancement.
Statistical analysis was divided into two parts: descriptive and inferential analysis. Descriptive analysis permitted the determination of relevant interacting factors (combination of two variables). Inferential analysis was divided into two steps: first, univariate analysis was performed to determine the association between each variable and improvement using the χ2
or Fisher exact test to identify which variables showed the strongest association with improvement upon this type of analysis.
Categorial data analysis.
Next, multiple inferential analysis was performed as follows: an intermediate model was constructed using a stepwise logistic regression model, with an entry probability of 0.15 and an exit probability of 0.25.
Applied logistic regression.
Possible interactions suggested by the descriptive analysis, which showed an association with the variables of the intermediate model were then investigated. The variables considered to enter the final model were: BMI, race, educational level, rotation shift, working at night, smoking, drinking, caffeine ingestion, snacks at night, diseases, use of medications, sedatives, insufficient sleep, insomnia, restless leg syndrome, and periodic limb movements in sleep (PLMS).
Possible interactions effects were investigated observing the relationship between lack of improvement and each independent variable at different levels of other independent variables. We seek for changes in the odds ratio that we considered important when comparing the levels at the second independent variable but without any fixed rule due to the fact that we concern other aspect: clinical relevance of the two independent variables. Doing these we tried to exclude spurious associations, always taking in account that the sample size may not be sufficient to show relevant interactions. The combinations of variables considered to be relevant upon descriptive analysis were BMI and drinking, BMI and insomnia, drinking and gastric disease, drinking and insomnia, heart and rheumatologic disease, gastric disease and insufficient sleep, neurological and psychiatric disease, neurological disease and insufficient sleep, insufficient sleep and insomnia, and psychiatric disease and use of medications.
Since insomnia was the only variable remaining in the intermediate model (), no interacting factors were investigated and therefore the final model contained only the variable insomnia.
Univariate analysis revealed a strong association between the lack of improvement and the following variables: drinking (), caffeine ingestion (), gastric disease (), rheumatologic disease (), neurological disease (), and insomnia (). The variable PLMS was discarded because it was only present in three individuals. Since one possible hypothesis for unsuccessful treatment is the general health status, improvement-related accumulation of the following main diseases obtained by univariate analysis was also investigated: gastric, rheumatologic and neurological diseases, and insomnia. The association between improvement and the accumulation of diseases was determined using a simple logistic regression model. Accumulation of diseases was defined as a disease index and was calculated by the following formula: disease index=gastric disease+neurological disease+rheumatologic disease+insomnia, where the presence or absence of each disease was scored as 1 or 0, respectively. The disease index construction was based on our intuition that some of the diseases studied have accumulative contribution to the lack of improvement, i.e., a person with just one of the diseases would have a better improvement when comparing with a person with two or more of the established diseases. The diseases were chosen using the univariate analysis and selecting the diseases with P-values lower than 0.20 when studying the relationship between these variables and the lack of improvement: gastric disease (), rheumatologic disease (), neurological disease (0.127) and insomnia ().