An observational study in Sleep Apnea Syndromes, sponsored by Hospital del Rio Hortega. Status unknown at 1 site in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2017-10-02.
Sponsored by Hospital del Rio Hortega · Observational
The sleep apnea-hypopnea syndrome (SAHS) is a respiratory disorder characterized by frequent breathing cessations (apneas) or partial collapses (hypopneas) during sleep. SAHS is linked with the most important causes of death in adults from industrialized countries. Metabolic deregulation and cardiovascular and cerebrovascular diseases, such as atrial fibrillation, stroke, myocardial infarction and sudden cardiac death, could affect people having untreated SAHS. The gold standard method for SAHS diagnosis is in-hospital, technician-attended nocturnal polysomnography (PSG). Nevertheless, this methodology is labor-intensive, time-consuming, and relatively unavailable, especially in low-resource settings. These drawbacks have led to large waiting lists, which delay diagnosis and treatment and limits its effectiveness as single diagnostic method for SAHS. Blood oxygen saturation (SpO2) and pulse rate (PR) from nocturnal pulse oximetry (NPO) provide relevant and essential information to detect apneas. In addition, it is significantly less intrusive for patients and it can be easily recorded at patients' home. In the same way, automated signal processing and pattern recognition techniques have demonstrated to provide accurate tools able to detect and effectively use this information. Therefore, the investigators hypothesize that automated pattern recognition of at-home NPO recordings could provide reliable and efficient tools able to simplify the management of SAHS. The aim of this study is two-fold: 1) to prospectively assess the reliability and effectiveness of at-home NPO in the context of adult SAHS; 2) to design, optimize and extensively assess the diagnostic performance of automated NPO-based screening tools for SAHS. In order to achieve these goals, both PSG and NPO recordings are carried out ambulatory and simultaneously at patient's home. A portable polysomnograph (Embletta MPR, Natus) is used for standard PSG at home, whereas a portable wrist-worn pulse oximeter (WristOX2 3150, Nonin) is used for ambulatory NPO. In addition, conventional in-lab PSG and attended pulse oximetry are also performed simultaneously in the hospital facilities.
Participants are recruited from the specialized sleep outpatient facilities of the Río Hortega University Hospital from Valladolid (Spain). All patients are referred from primary care due to moderate-to-high clinical suspicion of suffering from sleep apnea-hypopnea syndrome (SAHS). The final population is randomly split into two independent datasets: 1) training set (50%), which is used to design and build/train the screening algorithms; and 2) the test set (remaining 50%), which is used to further assess performance using unseen data.
The American Academy of Sleep Medicine rules are used to score respiratory events and to obtain the apnea-hypopnea index (AHI) from ambulatory PSG at home, which is used to definitively diagnose SAHS.
A portable wrist-worn pulse oximeter (WristOX2 3150, Nonin) is used for at-home NPO. Portable NPO is carried out simultaneously to ambulatory PSG (Embletta MPR, Natus) at patient's home. In addition, attended portable in-lab NPO (WristOX2 3150, Nonin) and in-lab PSG (E-Series, Compumedics) are performed simultaneously in the hospital in a different consecutive/previous night for comparison purposes. Participants are randomly assigned to carry out unattended sleep studies at home before or after in-hospital recordings.
SpO2 and PR from NPO are recorded simultaneously at a sampling rate of 1 Hz (1 sample every second). All recordings are saved to separate files and processed offline. An automatic signal pre-processing stage is carried out to remove artifacts due to patient movements (signal loss).
The signal processing methodology is divided into three automated stages: (i) feature extraction, (ii) feature selection, and (iii) pattern recognition.
Firstly, NPO recordings are parameterized by means of a wide set of variables, which previously demonstrated a high discriminative power in the context of SAHS detection. All features are computed for each whole portable overnight recording. The following feature subsets are composed:
Then, the optimum feature subset composed of the most relevant as well as complementary variables are composed. In order to achieve this goal, the following feature selection methods are applied:
Finally, the third stage corresponds to patter recognition. The aim of this stage is two-fold: (i) to design and optimize binary classification-oriented models trained to discern between SAHS negative and SAHS positive subjects using optimum features from NPO; (ii) to design and optimize regression-oriented models trained to estimate the AHI using optimum features from NPO. In order to achieve this goal, the following pattern recognition algorithms are assessed:
These models are subsequently combined to optimize the following 2-stage screening protocol: stage-1) true negative screening stage, which is aimed at detecting the maximum number of non-SAHS subjects while minimizing the number of false negative patients (ideally 0% false positive rate); stage-2) true positive screening stage, which is aimed at detecting (among patients not identified as true negative in the first stage) the maximum number of true positive patients while minimizing the number of false positive cases (ideally 0% false positive rate). Both stages are complementary and they are implemented consecutively, such that:
Consecutive subjects derived to the sleep specialized outpatient facilities showing moderate-to-high clinical suspicion of suffering from SAHS due to at least one of the following symptoms: daytime hypersomnolence, loud snoring, nocturnal choking and awakenings, and/or apneic events reported by the subject or the bedmate.
Exclusion Criteria:
Percentage of patients correctly classified
Percentage of patients (%) correctly classified/screened by the automated NPO-based screening test. At-home ambulatory PSG is used as the gold standard method for positive SAHS. Subjects with apnea-hypopnea index (AHI) \<5 are considered no-SAHS subjects, with 5\<=AHI\<15 as mild SAHS patients, with 15\<=AHI\<30 moderate SAHS patients, and AHI\>=30 as severe SAHS patients.
Time frame: 6 months after the inclusion of the last patient
Body mass index
Average (median and interquartile range) body mass index (kg/m2) of the cohort.
Time frame: 6 months after the inclusion of the last patient
Patients with chronic obstructive pulmonary disease
Number of patients (n) with comorbid chronic obstructive pulmonary disease (COPD), according to standard definitions.
Time frame: 6 months after the inclusion of the last patient
Patients with hypertension
Number of patients (n) with comorbid arterial hypertension (HT), according to standard definitions.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived AHI
Apnea-hypopnea index (events per hour) derived from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived time in REM sleep
Percentage of time (%) in rapid eye movement (REM) sleep to the total sleep time derived from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived sleep efficiency
Sleep efficiency (%) measured as the percentage of total sleep time to the total recording time derived from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived arousal index
Number of arousals per hour of sleep (events per hour) derived from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived time in supine position
Percentage of time (%) in supine position to the total sleep time derived from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived average SpO2
Average overnight SpO2 (%) from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived minimum SpO2
Minimum overnight SpO2 (%) from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home PSG-derived oxygen desaturation index of 3% (ODI3)
Number of desaturations greater than or equal to 3% from baseline per hour of sleep (events per hor) from unattended PSG at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home NPO-derived ODI3
Number of desaturations greater than or equal to 3% from baseline per hour of recording (events per hor) from unattended pulse oximetry at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home NPO-derived cumulative time below 90% (CT90)
Percentage (%) of cumulative time with a saturation below 90% from unattended pulse oximetry at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home NPO-derived average SpO2
Average saturation (%) from unattended pulse oximetry at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home NPO-derived minimum SpO2
Minimum saturation (%) from unattended pulse oximetry at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home NPO-derived average pulse rate
Average pulse rate (beats per minute) from unattended pulse oximetry at patients' home.
Time frame: 6 months after the inclusion of the last patient
At-home NPO-derived minimum pulse rate
Minimum pulse rate (beats per minute) from unattended pulse oximetry at patients' home.
Time frame: 6 months after the inclusion of the last patient
Prevalence of SAHS
Prevalence of SAHS (%) in the population under study according to at-home PSG.
Time frame: 6 months after the inclusion of the last patient
Severity of SAHS
Number of patients (n) with moderate-to-severe SAHS according to the at-home PSG-derived patient's AHI.
Time frame: 6 months after the inclusion of the last patient
NPO-derived ODI3 agreement
Mean difference (mean +/- 1.96 standard deviation interval) from the Bland and Altman agreement plot between unattended ODI3 from at-home NPO and supervised ODI3 from in-hospital NPO.
Time frame: 6 months after the inclusion of the last patient
PSG-derived AHI agreement
Mean difference (mean +/- 1.96 standard deviation interval) from the Bland and Altman agreement plot between unattended AHI from at-home PSG and supervised AHI from in-hospital PSG.
Time frame: 6 months after the inclusion of the last patient
Optimum diagnostic performance - Area under the ROC curve
Area under the receiver operating characteristics (ROC) curve of the optimum NPO-based binary classifier compared to standard at-home PSG.
Time frame: 6 months after the inclusion of the last patient
Optimum diagnostic performance - Accuracy
Accuracy (percentage, %) of the optimum NPO-based binary classifier compared to standard at-home PSG.
Time frame: 6 months after the inclusion of the last patient
Optimum agreement - Intra-class correlation coefficient
Intra-class correlation coefficient (ICC) between the optimum NPO-based estimated AHI and the actual AHI derived from at-home PSG.
Time frame: 6 months after the inclusion of the last patient
Patient's Sleep quality
Patients' sleep quality assessment using the Pittsburg questionnaire.
Time frame: 6 months after the inclusion of the last patient
Patient's somnolence
Patients' somnolence assessment using the Epworth questionnaire.
Time frame: 6 months after the inclusion of the last patient
Patients' quality of life
Patients' quality of life assessment using the Quebec sleep questionnaire (QSQ).
Time frame: 6 months after the inclusion of the last patient
Percentage of unsatisfactory recordings
Number of recordings (n) removed from the study due to reasons (either technical or human) related to unattended portable oximetry.
Time frame: 6 months after the inclusion of the last patient
Plan to share: No
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Hospital del Rio Hortega