<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03295149</url>
  </required_header>
  <id_info>
    <org_study_id>RTC-2015-3446-1</org_study_id>
    <nct_id>NCT03295149</nct_id>
  </id_info>
  <brief_title>ScreenOX - An Automated At-home Screening Test for Adult Sleep Apnea Using Nocturnal Oximetry</brief_title>
  <acronym>ScreenOX</acronym>
  <official_title>New Out-of-center Paradigms to Simplify Sleep Apnea Diagnosis. Design and Development of an Automated Screening Test Based on Oximetry (ScreenOX)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Hospital del Río Hortega</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>OXIGEN salud</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Valladolid</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Five Flames Mobile</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Hospital del Río Hortega</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The sleep apnea-hypopnea syndrome (SAHS) is a respiratory disorder characterized by frequent&#xD;
      breathing cessations (apneas) or partial collapses (hypopneas) during sleep. SAHS is linked&#xD;
      with the most important causes of death in adults from industrialized countries. Metabolic&#xD;
      deregulation and cardiovascular and cerebrovascular diseases, such as atrial fibrillation,&#xD;
      stroke, myocardial infarction and sudden cardiac death, could affect people having untreated&#xD;
      SAHS. The gold standard method for SAHS diagnosis is in-hospital, technician-attended&#xD;
      nocturnal polysomnography (PSG). Nevertheless, this methodology is labor-intensive,&#xD;
      time-consuming, and relatively unavailable, especially in low-resource settings. These&#xD;
      drawbacks have led to large waiting lists, which delay diagnosis and treatment and limits its&#xD;
      effectiveness as single diagnostic method for SAHS. Blood oxygen saturation (SpO2) and pulse&#xD;
      rate (PR) from nocturnal pulse oximetry (NPO) provide relevant and essential information to&#xD;
      detect apneas. In addition, it is significantly less intrusive for patients and it can be&#xD;
      easily recorded at patients' home. In the same way, automated signal processing and pattern&#xD;
      recognition techniques have demonstrated to provide accurate tools able to detect and&#xD;
      effectively use this information. Therefore, the investigators hypothesize that automated&#xD;
      pattern recognition of at-home NPO recordings could provide reliable and efficient tools able&#xD;
      to simplify the management of SAHS. The aim of this study is two-fold: 1) to prospectively&#xD;
      assess the reliability and effectiveness of at-home NPO in the context of adult SAHS; 2) to&#xD;
      design, optimize and extensively assess the diagnostic performance of automated NPO-based&#xD;
      screening tools for SAHS. In order to achieve these goals, both PSG and NPO recordings are&#xD;
      carried out ambulatory and simultaneously at patient's home. A portable polysomnograph&#xD;
      (Embletta MPR, Natus) is used for standard PSG at home, whereas a portable wrist-worn pulse&#xD;
      oximeter (WristOX2 3150, Nonin) is used for ambulatory NPO. In addition, conventional in-lab&#xD;
      PSG and attended pulse oximetry are also performed simultaneously in the hospital facilities.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Participants are recruited from the specialized sleep outpatient facilities of the Río&#xD;
      Hortega University Hospital from Valladolid (Spain). All patients are referred from primary&#xD;
      care due to moderate-to-high clinical suspicion of suffering from sleep apnea-hypopnea&#xD;
      syndrome (SAHS). The final population is randomly split into two independent datasets: 1)&#xD;
      training set (50%), which is used to design and build/train the screening algorithms; and 2)&#xD;
      the test set (remaining 50%), which is used to further assess performance using unseen data.&#xD;
&#xD;
      The American Academy of Sleep Medicine rules are used to score respiratory events and to&#xD;
      obtain the apnea-hypopnea index (AHI) from ambulatory PSG at home, which is used to&#xD;
      definitively diagnose SAHS.&#xD;
&#xD;
      A portable wrist-worn pulse oximeter (WristOX2 3150, Nonin) is used for at-home NPO. Portable&#xD;
      NPO is carried out simultaneously to ambulatory PSG (Embletta MPR, Natus) at patient's home.&#xD;
      In addition, attended portable in-lab NPO (WristOX2 3150, Nonin) and in-lab PSG (E-Series,&#xD;
      Compumedics) are performed simultaneously in the hospital in a different consecutive/previous&#xD;
      night for comparison purposes. Participants are randomly assigned to carry out unattended&#xD;
      sleep studies at home before or after in-hospital recordings.&#xD;
&#xD;
      SpO2 and PR from NPO are recorded simultaneously at a sampling rate of 1 Hz (1 sample every&#xD;
      second). All recordings are saved to separate files and processed offline. An automatic&#xD;
      signal pre-processing stage is carried out to remove artifacts due to patient movements&#xD;
      (signal loss).&#xD;
&#xD;
      The signal processing methodology is divided into three automated stages: (i) feature&#xD;
      extraction, (ii) feature selection, and (iii) pattern recognition.&#xD;
&#xD;
      Firstly, NPO recordings are parameterized by means of a wide set of variables, which&#xD;
      previously demonstrated a high discriminative power in the context of SAHS detection. All&#xD;
      features are computed for each whole portable overnight recording. The following feature&#xD;
      subsets are composed:&#xD;
&#xD;
        -  Time domain statistics from SpO2 recordings. First to fourth-order statistical moments&#xD;
           in the time domain, i.e., arithmetic mean, variance, skewness and kurtosis, which&#xD;
           quantify central tendency, amount of dispersion, asymmetry, and peakedness,&#xD;
           respectively.&#xD;
&#xD;
        -  Time domain features from PR recordings: average, standard deviation, and root mean&#xD;
           square of standard deviation of the pulse-to-pulse interval time series.&#xD;
&#xD;
        -  Frequency domain statistics from SpO2 recordings. First to fourth-order statistical&#xD;
           moments, median frequency, and Shannon spectral entropy from the power spectral density&#xD;
           function.&#xD;
&#xD;
        -  Frequency domain statistics from PR recordings. First to fourth-order statistical&#xD;
           moments, median frequency, and Shannon spectral entropy from the power spectral density&#xD;
           function.&#xD;
&#xD;
        -  Conventional spectral measures from SpO2 recordings. Total signal power as well as peak&#xD;
           amplitude and relative power in the frequency range 0.014 - 0.033 Hz.&#xD;
&#xD;
        -  Conventional spectral measures from PR recordings. Normalized power in the low (0.04 -&#xD;
           0.15 Hz) and in the high (0.15 - 0.40 Hz) frequency bands, as well as the low frequency&#xD;
           to high frequency ratio (sympathovagal balance).&#xD;
&#xD;
        -  Nonlinear features from SpO2 recordings. Sample entropy, central tendency measure, and&#xD;
           Lempel - Ziv complexity, which measure irregularity, variability, and complexity of SpO2&#xD;
           recordings.&#xD;
&#xD;
        -  Nonlinear features from PR recordings. Sample entropy, central tendency measure, and&#xD;
           Lempel - Ziv complexity, which measure irregularity, variability, and complexity of PR&#xD;
           recordings.&#xD;
&#xD;
      Then, the optimum feature subset composed of the most relevant as well as complementary&#xD;
      variables are composed. In order to achieve this goal, the following feature selection&#xD;
      methods are applied:&#xD;
&#xD;
        -  Forward stepwise feature selection&#xD;
&#xD;
        -  Genetic algorithms&#xD;
&#xD;
        -  Fast correlation-based filter&#xD;
&#xD;
        -  Minimal-redundancy maximal-relevance criterion&#xD;
&#xD;
      Finally, the third stage corresponds to patter recognition. The aim of this stage is&#xD;
      two-fold: (i) to design and optimize binary classification-oriented models trained to discern&#xD;
      between SAHS negative and SAHS positive subjects using optimum features from NPO; (ii) to&#xD;
      design and optimize regression-oriented models trained to estimate the AHI using optimum&#xD;
      features from NPO. In order to achieve this goal, the following pattern recognition&#xD;
      algorithms are assessed:&#xD;
&#xD;
        -  Binary classification: logistic regression, artificial neural networks, Bayesian&#xD;
           networks, decision trees, ensemble learning (AdaBoost).&#xD;
&#xD;
        -  Regression models: multiple linear regression, artificial neural networks, Bayesian&#xD;
           networks, ensemble learning (least squares boosting).&#xD;
&#xD;
      These models are subsequently combined to optimize the following 2-stage screening protocol:&#xD;
      stage-1) true negative screening stage, which is aimed at detecting the maximum number of&#xD;
      non-SAHS subjects while minimizing the number of false negative patients (ideally 0% false&#xD;
      positive rate); stage-2) true positive screening stage, which is aimed at detecting (among&#xD;
      patients not identified as true negative in the first stage) the maximum number of true&#xD;
      positive patients while minimizing the number of false positive cases (ideally 0% false&#xD;
      positive rate). Both stages are complementary and they are implemented consecutively, such&#xD;
      that:&#xD;
&#xD;
        -  Patients identified as true negative in the first stage are referred to the sleep&#xD;
           specialist to finally discard SAHS taken into account symptoms, comorbidities and past&#xD;
           clinical history. These patients are no longer derived to the sleep unit unless&#xD;
           requested by the sleep specialist due to persistent and/or additional symptoms.&#xD;
&#xD;
        -  Patients identified as true positive in the second stage are referred to the sleep&#xD;
           specialist to finally confirm SAHS and decide the most suitable treatment option. These&#xD;
           patients are no longer derived to the sleep unit unless requested by the sleep&#xD;
           specialist.&#xD;
&#xD;
        -  Non-conclusive cases are finally derived to the sleep unit for a standard PSG in order&#xD;
           to confirm/discard SAHS.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">January 1, 2016</start_date>
  <completion_date type="Anticipated">December 2018</completion_date>
  <primary_completion_date type="Anticipated">April 2018</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Percentage of patients correctly classified</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>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) &lt;5 are considered no-SAHS subjects, with 5&lt;=AHI&lt;15 as mild SAHS patients, with 15&lt;=AHI&lt;30 moderate SAHS patients, and AHI&gt;=30 as severe SAHS patients.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Body mass index</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Average (median and interquartile range) body mass index (kg/m2) of the cohort.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Patients with chronic obstructive pulmonary disease</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Number of patients (n) with comorbid chronic obstructive pulmonary disease (COPD), according to standard definitions.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Patients with hypertension</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Number of patients (n) with comorbid arterial hypertension (HT), according to standard definitions.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived AHI</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Apnea-hypopnea index (events per hour) derived from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived time in REM sleep</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Percentage of time (%) in rapid eye movement (REM) sleep to the total sleep time derived from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived sleep efficiency</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Sleep efficiency (%) measured as the percentage of total sleep time to the total recording time derived from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived arousal index</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Number of arousals per hour of sleep (events per hour) derived from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived time in supine position</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Percentage of time (%) in supine position to the total sleep time derived from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived average SpO2</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Average overnight SpO2 (%) from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived minimum SpO2</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Minimum overnight SpO2 (%) from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home PSG-derived oxygen desaturation index of 3% (ODI3)</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Number of desaturations greater than or equal to 3% from baseline per hour of sleep (events per hor) from unattended PSG at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home NPO-derived ODI3</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home NPO-derived cumulative time below 90% (CT90)</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Percentage (%) of cumulative time with a saturation below 90% from unattended pulse oximetry at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home NPO-derived average SpO2</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Average saturation (%) from unattended pulse oximetry at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home NPO-derived minimum SpO2</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Minimum saturation (%) from unattended pulse oximetry at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home NPO-derived average pulse rate</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Average pulse rate (beats per minute) from unattended pulse oximetry at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>At-home NPO-derived minimum pulse rate</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Minimum pulse rate (beats per minute) from unattended pulse oximetry at patients' home.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Prevalence of SAHS</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Prevalence of SAHS (%) in the population under study according to at-home PSG.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Severity of SAHS</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Number of patients (n) with moderate-to-severe SAHS according to the at-home PSG-derived patient's AHI.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>NPO-derived ODI3 agreement</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>PSG-derived AHI agreement</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Optimum diagnostic performance - Area under the ROC curve</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Area under the receiver operating characteristics (ROC) curve of the optimum NPO-based binary classifier compared to standard at-home PSG.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Optimum diagnostic performance - Accuracy</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Accuracy (percentage, %) of the optimum NPO-based binary classifier compared to standard at-home PSG.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Optimum agreement - Intra-class correlation coefficient</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Intra-class correlation coefficient (ICC) between the optimum NPO-based estimated AHI and the actual AHI derived from at-home PSG.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Patient's Sleep quality</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Patients' sleep quality assessment using the Pittsburg questionnaire.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Patient's somnolence</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Patients' somnolence assessment using the Epworth questionnaire.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Patients' quality of life</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Patients' quality of life assessment using the Quebec sleep questionnaire (QSQ).</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Percentage of unsatisfactory recordings</measure>
    <time_frame>6 months after the inclusion of the last patient</time_frame>
    <description>Number of recordings (n) removed from the study due to reasons (either technical or human) related to unattended portable oximetry.</description>
  </secondary_outcome>
  <enrollment type="Anticipated">400</enrollment>
  <condition>Sleep Apnea Syndromes</condition>
  <eligibility>
    <study_pop>
      <textblock>
        Consecutive subjects derived to the sleep specialized outpatient facilities showing&#xD;
        moderate-to-high clinical suspicion of suffering from SAHS due to at least one of the&#xD;
        following symptoms: daytime hypersomnolence, loud snoring, nocturnal choking and&#xD;
        awakenings, and/or apneic events reported by the subject or the bedmate.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Men and women over 18 years old&#xD;
&#xD;
          -  Subjects derived from primary care to the sleep specialized outpatient facilities&#xD;
             showing moderate-to-high clinical suspicion of suffering from sleep apnea (daytime&#xD;
             hypersomnolence, loud snoring, nocturnal choking and awakenings, and/or apneic events)&#xD;
&#xD;
          -  Written informed consent signed&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Subjects under 18 years old&#xD;
&#xD;
          -  Subjects not signing the informed consent&#xD;
&#xD;
          -  Presence of any previously diagnosed sleep disorder: narcolepsy, insomnia, chronic&#xD;
             sleep deprivation, regular use of hypnotic or sedative medications and/or restless leg&#xD;
             syndrome.&#xD;
&#xD;
          -  Patients with the following chronic diseases: congestive heart failure, renal failure,&#xD;
             neuromuscular diseases, chronic respiratory failure.&#xD;
&#xD;
          -  Patients with &gt;50% of central apneas or the presence of Cheyne-Stokes respiration.&#xD;
&#xD;
          -  Previous continuous positive airway pressure (CPAP) treatment for SAHS diagnosis&#xD;
&#xD;
          -  A medical history that may interfere with the study objectives or, in the opinion of&#xD;
             the investigator, compromise the conclusions&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Félix Del Campo, PhD,MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Río Hortega University Hospital</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Félix Del Campo, PhD, MD</last_name>
    <phone>+34 983420400</phone>
    <phone_ext>85776</phone_ext>
    <email>fsas@telefonica.net</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Rosa Conde</last_name>
    <phone>+34 983420400</phone>
    <phone_ext>84400</phone_ext>
    <email>rconvi@saludcastillayleon.es</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Río Hortega University Hospital</name>
      <address>
        <city>Valladolid</city>
        <zip>47012</zip>
        <country>Spain</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Félix Del Campo, PhD, MD</last_name>
      <phone>+34 983420400</phone>
      <phone_ext>85776</phone_ext>
      <email>fsas@telefonica.net</email>
    </contact>
    <contact_backup>
      <last_name>Rosa Conde</last_name>
      <phone>+34 983420400</phone>
      <phone_ext>84400</phone_ext>
      <email>rconvi@saludcastillayleon.es</email>
    </contact_backup>
    <investigator>
      <last_name>Julio F De Frutos, PhD, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Carmen A Arroyo, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Andrea Crespo, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Daniel Álvarez, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Jordi Blanco</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Ana Mayoral, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Roberto Hornero, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Gonzalo C Gutiérrez-Tobal, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Jesús Poza, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Carlos Gómez, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>María García, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Víctor Ortega</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Sergio Morales</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>Spain</country>
  </location_countries>
  <link>
    <url>http://www.screenox.es/</url>
    <description>Web of the ScreenOX clinical trial</description>
  </link>
  <link>
    <url>http://www.saludcastillayleon.es/HRHortega/es</url>
    <description>Web of the Rio Hortega University Hospital, Spain</description>
  </link>
  <link>
    <url>https://www.oxigensalud.com/healthcare/index.aspx</url>
    <description>Web of OXIGEN salud. Respiratory therapy provider, Spain</description>
  </link>
  <link>
    <url>http://gib.tel.uva.es/</url>
    <description>Web of the Biomedical Engineering Group (GIB), University of Valladolid, Spain</description>
  </link>
  <verification_date>September 2017</verification_date>
  <study_first_submitted>September 18, 2017</study_first_submitted>
  <study_first_submitted_qc>September 22, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">September 27, 2017</study_first_posted>
  <last_update_submitted>September 28, 2017</last_update_submitted>
  <last_update_submitted_qc>September 28, 2017</last_update_submitted_qc>
  <last_update_posted type="Actual">October 2, 2017</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Hospital del Río Hortega</investigator_affiliation>
    <investigator_full_name>Félix del Campo Matías</investigator_full_name>
    <investigator_title>PhD, MD</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Apnea</mesh_term>
    <mesh_term>Sleep Apnea Syndromes</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03295149
org study id: RTC-2015-3446-1
nct id: NCT03295149
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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:

        -  Time domain statistics from SpO2 recordings. First to fourth-order statistical moments
           in the time domain, i.e., arithmetic mean, variance, skewness and kurtosis, which
           quantify central tendency, amount of dispersion, asymmetry, and peakedness,
           respectively.

        -  Time domain features from PR recordings: average, standard deviation, and root mean
           square of standard deviation of the pulse-to-pulse interval time series.

        -  Frequency domain statistics from SpO2 recordings. First to fourth-order statistical
           moments, median frequency, and Shannon spectral entropy from the power spectral density
           function.

        -  Frequency domain statistics from PR recordings. First to fourth-order statistical
           moments, median frequency, and Shannon spectral entropy from the power spectral density
           function.

        -  Conventional spectral measures from SpO2 recordings. Total signal power as well as peak
           amplitude and relative power in the frequency range 0.014 - 0.033 Hz.

        -  Conventional spectral measures from PR recordings. Normalized power in the low (0.04 -
           0.15 Hz) and in the high (0.15 - 0.40 Hz) frequency bands, as well as the low frequency
           to high frequency ratio (sympathovagal balance).

        -  Nonlinear features from SpO2 recordings. Sample entropy, central tendency measure, and
           Lempel - Ziv complexity, which measure irregularity, variability, and complexity of SpO2
           recordings.

        -  Nonlinear features from PR recordings. Sample entropy, central tendency measure, and
           Lempel - Ziv complexity, which measure irregularity, variability, and complexity of PR
           recordings.

      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:

        -  Forward stepwise feature selection

        -  Genetic algorithms

        -  Fast correlation-based filter

        -  Minimal-redundancy maximal-relevance criterion

      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:

        -  Binary classification: logistic regression, artificial neural networks, Bayesian
           networks, decision trees, ensemble learning (AdaBoost).

        -  Regression models: multiple linear regression, artificial neural networks, Bayesian
           networks, ensemble learning (least squares boosting).

      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:

        -  Patients identified as true negative in the first stage are referred to the sleep
           specialist to finally discard SAHS taken into account symptoms, comorbidities and past
           clinical history. These patients are no longer derived to the sleep unit unless
           requested by the sleep specialist due to persistent and/or additional symptoms.

        -  Patients identified as true positive in the second stage are referred to the sleep
           specialist to finally confirm SAHS and decide the most suitable treatment option. These
           patients are no longer derived to the sleep unit unless requested by the sleep
           specialist.

        -  Non-conclusive cases are finally derived to the sleep unit for a standard PSG in order
           to confirm/discard SAHS.
observational model: Cohort
time perspective: Prospective
measure: Percentage of patients correctly classified
time frame: 6 months after the inclusion of the last patient
description: 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.
measure: Body mass index
time frame: 6 months after the inclusion of the last patient
description: Average (median and interquartile range) body mass index (kg/m2) of the cohort.
measure: Patients with chronic obstructive pulmonary disease
time frame: 6 months after the inclusion of the last patient
description: Number of patients (n) with comorbid chronic obstructive pulmonary disease (COPD), according to standard definitions.
measure: Patients with hypertension
time frame: 6 months after the inclusion of the last patient
description: Number of patients (n) with comorbid arterial hypertension (HT), according to standard definitions.
measure: At-home PSG-derived AHI
time frame: 6 months after the inclusion of the last patient
description: Apnea-hypopnea index (events per hour) derived from unattended PSG at patients' home.
measure: At-home PSG-derived time in REM sleep
time frame: 6 months after the inclusion of the last patient
description: Percentage of time (%) in rapid eye movement (REM) sleep to the total sleep time derived from unattended PSG at patients' home.
measure: At-home PSG-derived sleep efficiency
time frame: 6 months after the inclusion of the last patient
description: Sleep efficiency (%) measured as the percentage of total sleep time to the total recording time derived from unattended PSG at patients' home.
measure: At-home PSG-derived arousal index
time frame: 6 months after the inclusion of the last patient
description: Number of arousals per hour of sleep (events per hour) derived from unattended PSG at patients' home.
measure: At-home PSG-derived time in supine position
time frame: 6 months after the inclusion of the last patient
description: Percentage of time (%) in supine position to the total sleep time derived from unattended PSG at patients' home.
measure: At-home PSG-derived average SpO2
time frame: 6 months after the inclusion of the last patient
description: Average overnight SpO2 (%) from unattended PSG at patients' home.
measure: At-home PSG-derived minimum SpO2
time frame: 6 months after the inclusion of the last patient
description: Minimum overnight SpO2 (%) from unattended PSG at patients' home.
measure: At-home PSG-derived oxygen desaturation index of 3% (ODI3)
time frame: 6 months after the inclusion of the last patient
description: Number of desaturations greater than or equal to 3% from baseline per hour of sleep (events per hor) from unattended PSG at patients' home.
measure: At-home NPO-derived ODI3
time frame: 6 months after the inclusion of the last patient
description: 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.
measure: At-home NPO-derived cumulative time below 90% (CT90)
time frame: 6 months after the inclusion of the last patient
description: Percentage (%) of cumulative time with a saturation below 90% from unattended pulse oximetry at patients' home.
measure: At-home NPO-derived average SpO2
time frame: 6 months after the inclusion of the last patient
description: Average saturation (%) from unattended pulse oximetry at patients' home.
measure: At-home NPO-derived minimum SpO2
time frame: 6 months after the inclusion of the last patient
description: Minimum saturation (%) from unattended pulse oximetry at patients' home.
measure: At-home NPO-derived average pulse rate
time frame: 6 months after the inclusion of the last patient
description: Average pulse rate (beats per minute) from unattended pulse oximetry at patients' home.
measure: At-home NPO-derived minimum pulse rate
time frame: 6 months after the inclusion of the last patient
description: Minimum pulse rate (beats per minute) from unattended pulse oximetry at patients' home.
measure: Prevalence of SAHS
time frame: 6 months after the inclusion of the last patient
description: Prevalence of SAHS (%) in the population under study according to at-home PSG.
measure: Severity of SAHS
time frame: 6 months after the inclusion of the last patient
description: Number of patients (n) with moderate-to-severe SAHS according to the at-home PSG-derived patient's AHI.
measure: NPO-derived ODI3 agreement
time frame: 6 months after the inclusion of the last patient
description: 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.
measure: PSG-derived AHI agreement
time frame: 6 months after the inclusion of the last patient
description: 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.
measure: Optimum diagnostic performance - Area under the ROC curve
time frame: 6 months after the inclusion of the last patient
description: Area under the receiver operating characteristics (ROC) curve of the optimum NPO-based binary classifier compared to standard at-home PSG.
measure: Optimum diagnostic performance - Accuracy
time frame: 6 months after the inclusion of the last patient
description: Accuracy (percentage, %) of the optimum NPO-based binary classifier compared to standard at-home PSG.
measure: Optimum agreement - Intra-class correlation coefficient
time frame: 6 months after the inclusion of the last patient
description: Intra-class correlation coefficient (ICC) between the optimum NPO-based estimated AHI and the actual AHI derived from at-home PSG.
measure: Patient's Sleep quality
time frame: 6 months after the inclusion of the last patient
description: Patients' sleep quality assessment using the Pittsburg questionnaire.
measure: Patient's somnolence
time frame: 6 months after the inclusion of the last patient
description: Patients' somnolence assessment using the Epworth questionnaire.
measure: Patients' quality of life
time frame: 6 months after the inclusion of the last patient
description: Patients' quality of life assessment using the Quebec sleep questionnaire (QSQ).
measure: Percentage of unsatisfactory recordings
time frame: 6 months after the inclusion of the last patient
description: Number of recordings (n) removed from the study due to reasons (either technical or human) related to unattended portable oximetry.
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Félix Del Campo, PhD,MD
role: Principal Investigator
affiliation: Río Hortega University Hospital
last name: Félix Del Campo, PhD, MD
phone: +34 983420400
phone ext: 85776
email: fsas@telefonica.net
last name: Rosa Conde
phone: +34 983420400
phone ext: 84400
email: rconvi@saludcastillayleon.es
facility: 
status: Recruiting
contact: 
contact backup: 
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country: Spain
url: http://www.screenox.es/
description: Web of the ScreenOX clinical trial
url: http://www.saludcastillayleon.es/HRHortega/es
description: Web of the Rio Hortega University Hospital, Spain
url: https://www.oxigensalud.com/healthcare/index.aspx
description: Web of OXIGEN salud. Respiratory therapy provider, Spain
url: http://gib.tel.uva.es/
description: Web of the Biomedical Engineering Group (GIB), University of Valladolid, Spain
responsible party type: Principal Investigator
investigator affiliation: Hospital del Río Hortega
investigator full name: Félix del Campo Matías
investigator title: PhD, MD
mesh term: Apnea
mesh term: Sleep Apnea Syndromes
sharing ipd: No