New Out-of-center Paradigms to Simplify Sleep Apnea Diagnosis. Design and Development of an Automated Screening Test Based on Oximetry (ScreenOX)
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 400
- 试验地点
- 1
- 主要终点
- Percentage of patients correctly classified
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Men and women over 18 years old
- •Subjects derived from primary care to the sleep specialized outpatient facilities showing moderate-to-high clinical suspicion of suffering from sleep apnea (daytime hypersomnolence, loud snoring, nocturnal choking and awakenings, and/or apneic events)
- •Written informed consent signed
排除标准
- •Subjects under 18 years old
- •Subjects not signing the informed consent
- •Presence of any previously diagnosed sleep disorder: narcolepsy, insomnia, chronic sleep deprivation, regular use of hypnotic or sedative medications and/or restless leg syndrome.
- •Patients with the following chronic diseases: congestive heart failure, renal failure, neuromuscular diseases, chronic respiratory failure.
- •Patients with >50% of central apneas or the presence of Cheyne-Stokes respiration.
- •Previous continuous positive airway pressure (CPAP) treatment for SAHS diagnosis
- •A medical history that may interfere with the study objectives or, in the opinion of the investigator, compromise the conclusions
结局指标
主要结局
Percentage of patients correctly classified
时间窗: 6 months after the inclusion of the last patient
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.
次要结局
- Body mass index(6 months after the inclusion of the last patient)
- Patients with chronic obstructive pulmonary disease(6 months after the inclusion of the last patient)
- Patients with hypertension(6 months after the inclusion of the last patient)
- At-home PSG-derived AHI(6 months after the inclusion of the last patient)
- At-home PSG-derived time in supine position(6 months after the inclusion of the last patient)
- At-home PSG-derived average SpO2(6 months after the inclusion of the last patient)
- At-home PSG-derived time in REM sleep(6 months after the inclusion of the last patient)
- At-home PSG-derived sleep efficiency(6 months after the inclusion of the last patient)
- At-home PSG-derived arousal index(6 months after the inclusion of the last patient)
- At-home PSG-derived minimum SpO2(6 months after the inclusion of the last patient)
- At-home PSG-derived oxygen desaturation index of 3% (ODI3)(6 months after the inclusion of the last patient)
- At-home NPO-derived ODI3(6 months after the inclusion of the last patient)
- At-home NPO-derived cumulative time below 90% (CT90)(6 months after the inclusion of the last patient)
- At-home NPO-derived average SpO2(6 months after the inclusion of the last patient)
- At-home NPO-derived minimum SpO2(6 months after the inclusion of the last patient)
- At-home NPO-derived average pulse rate(6 months after the inclusion of the last patient)
- At-home NPO-derived minimum pulse rate(6 months after the inclusion of the last patient)
- Prevalence of SAHS(6 months after the inclusion of the last patient)
- Severity of SAHS(6 months after the inclusion of the last patient)
- NPO-derived ODI3 agreement(6 months after the inclusion of the last patient)
- PSG-derived AHI agreement(6 months after the inclusion of the last patient)
- Optimum diagnostic performance - Area under the ROC curve(6 months after the inclusion of the last patient)
- Optimum diagnostic performance - Accuracy(6 months after the inclusion of the last patient)
- Optimum agreement - Intra-class correlation coefficient(6 months after the inclusion of the last patient)
- Patient's Sleep quality(6 months after the inclusion of the last patient)
- Patient's somnolence(6 months after the inclusion of the last patient)
- Patients' quality of life(6 months after the inclusion of the last patient)
- Percentage of unsatisfactory recordings(6 months after the inclusion of the last patient)
研究者
Félix del Campo Matías
PhD, MD
Hospital del Río Hortega