Design and development of the first electronic multicentric data registry of obstructive sleep apnea in Italy: clinical workflow integrated with digital data management and artificial intelligence

Design and development of the first electronic multicentric data registry of obstructive sleep apnea in Italy: clinical workflow integrated with digital data management and artificial intelligence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design and development of the first electronic multicentric data registry of obstructive sleep apnea in Italy: clinical workflow integrated with digital data management and artificial intelligence Chiara Maria Palo, Valentina Tibollo, Francesco Fanfulla, Vanessa Tang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8968393/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Obstructive Sleep Apnea (OSA) is a high prevalent breathing condition, well characterized in Europe. However, data on the Italian population are still lacking in clinical presentation, polysomnographic characteristics, and comorbidities. The Italian PheNotypes Obstructive Sleep Apnea Study (IPNOS) aims to build the first electronic national registry for OSA research, investigating a population of more than 5,000 patients with suspected OSA. Methods The electronic platform REDCap (Research Electronic Data Capture) has been chosen to develop the registry. Analyzing key user, digital and ethical requirements, we designed a tool that complies with current regulations on health data management and able to host a multicenter data collection. Customized computer routines ensure automatic data import from different hospital information systems and real-time quality checks. Results The IPNOS registry currently includes 27 electronic forms and 803 variables. To date, data from more than 5,300 patients have been collected across 16 Italian sleep centers. Patients can complete digitally questionnaires with automatic saving of responses within the REDCap platform, thereby reducing the burden associated with manual data transcription. An artificial intelligence tool enables the automated extraction of structured data from text polysomnography reports for direct integration into the registry. Conclusions The IPNOS registry is a new, interoperable platform for OSA research in Italy. It ensures multicenter collaboration, guaranteeing an easy expansion of the centers’ network. This registry lays the groundwork for future studies and strengthens Italy’s contribution in global sleep medicine. Trial registration ClinicalTrials.gov, NCT06872242. Registered 12 March 2025. Retrospectively registered. Sleep medicine Obstructive sleep apnea Multicentric study Electronic data collection REDCap platform Artificial intelligence Patient reported outcomes Data governance Data management e-infrastructure. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Obstructive sleep apnea (OSA) is a high prevalent disorder with substantial clinical impact [ 1 ]. In Italy, approximately 12 million people experience sleep disorders, and OSA may affect up to 15% of individuals over the age of 40 [ 2 – 3 ]. The European Sleep Apnea Database (ESADA) study provided the European profile of OSA in recent years [ 4 – 5 ]. Marked differences have been described across European macro-regions in terms of symptoms and associated complications [ 6 ]. However, the Italian population remains largely unexplored, with limited evidence on the prevalence of OSA, its clinical presentation, and associated comorbidities. Moreover, substantial variability exists across the country in terms of diagnostic availability, technological resources, and scientific approaches to the management of this condition. This heterogeneity may become even more pronounced in the near future, given the rapid expansion of centers involved in sleep disorders. 1.1 The project The Italian Phenotypes Obstructive Sleep Apnea Study (IPNOS) consortium – an Italian network of sleep medicine centers endorsed by the Italian Academy of Sleep Medicine (AIMS) [ 7 ] – was established to investigate OSA in Italy. The consortium is structured according to a hub-and-spoke model, with four centers hubs (including the coordinating center and three principal units) and twelve spokes centers that support the recruitment process, as shown in Fig. 1 . The main coordinating center is Istituto Auxologico Italiano in Milan, which collaborates with three leading centers: Istituti Clinici Scientifici Maugeri in Pavia (Northern Italy), IRCCS Neuromed Pozzilli in Isernia (Central Italy), and Ospedali Riuniti Villa Sofia-Cervello in Palermo (Southern Italy). This organizational core (the hubs ) is connected to the AIMS network, which includes other twelve centers. The full list of sleep medicine centers involved in the IPNOS consortium is provided in Supplementary Materials – section 1 . The IPNOS study [ 8 ] is supported by a grant of Italian Ministry of Health (PNRR-MAD-2022-12375812) [ 9 ] and firstly approved by the internal Ethics Committee (EC) of Istituto Auxologico Italiano of Milan on December 21th 2022 (protocol number 2022_12_21_01). This study aims to analyze patients with suspected OSA through a national electronic registry designed to collect standardized clinical data across multiple centers. The objectives of the registry are as follows: i) to establish the largest registry of Italian patients with OSA; ii) to conduct cross-sectional, prospective, or long-term follow-up studies aimed at improving diagnosis and treatment; iii) to promote education, and standardization of procedures among participating Italian centers; iv) to foster collaborative links with other databases, such as ESADA. 2. Methods The methodological approach used during the creation of the IPNOS registry is structured as shown in Fig. 2 . An inventory of user needs, information technology (IT) and ethical requirements has been conducted to identify the most suitable IT platform available to develop the IPNOS registry, also considering the restrictions of current regulations [ 10 – 11 ]. A dedicated multidisciplinary working team has been established, including: i) clinicians responsible for the clinical management of the disorder; ii) engineers and computer scientists providing the IT competencies required during the registry design; iii) statisticians supporting the analysis of the collected data [ 12 ]. The methodology followed during registry’s development is shown in Fig. 3 . 2.1 Requirements evaluation and variables definition The development of an electronic registry in healthcare requires protection of data ownership and controlled use and sharing of patient information, as defined in the General Data Protection Regulation (GDPR) [ 10 , 13 ]. The Data Protection Officer (DPO) guarantees compliance with regulations by each center and data security is strongly ensured through the following measures: i) avoid storing highly sensitive data from patients; ii) keep track of every type of action performed on data (logging); iii) limit visibility on data; iv) guarantee the Findable, Accessible, Interoperable and Reusable (FAIR) principles and data minimization [ 14 , 15 ]; v) ensure pseudonymization of data [ 16 ]. The respective ECs have approved a data management framework whereby each center has access exclusively to its own data. Full access to the complete dataset is restricted to the study’s coordinating investigators. An external company [ 17 ], contracted within the IPNOS study to manage the platform hosting the registry, has been formally designated as the Data Processor, due to its comprehensive visibility over all centers’ data for maintenance purposes. Each participating center acts as an independent Data Controller for its own data and has executed specific agreements to formally appoint the company in this role. Registry users can be categorized as data providers and data collectors, each with distinct requirements. Data providers – including patients, research laboratories, clinicians, and hospital information systems – require a system capable of accommodating multiple data formats, integrating heterogeneous data sources, and capturing patient-reported outcomes. Data collectors – such as clinicians, laboratory technicians, patients, and IT staff – require rapid, structured, and intuitive data entry processes. IT operators additionally require a highly customizable digital platform with access to database queries and application programming interfaces (APIs) [ 18 ]. Through close collaboration between healthcare professionals and technical staff, the full set of variables to be collected has been defined. Clinical data, validated and customized questionnaires were structured for longitudinal collection. These steps constituted the foundation for the selection and development of the registry platform. 2.2 Platform selection Among different open-source electronic data collection (EDC) platforms, Research Electronic Data Capture (REDCap) has emerged as one of the most robust and established solutions, combining high reliability with a stable governance model and comprehensive documentation [ 19 ]. More details about REDCap platform are provided in Supplementary Materials – sections 2 . 2.2.1 REDCap in multicenter studies In case of multicenter studies, the Data Access Groups (DAGs) functionality of REDCap allows us to manage the different centers involved. Each center is identified by a representative name and a unique code and it is associated with its respective users. All centers’ data are stored on a common REDCap-based server, as shown in Fig. 4 . 2.3 Platform design The REDCap registry design phase began in January 2024 and took approximately six months to complete. Before proceeding with eCRFs implementation, the virtual environment to host REDCap has been set with firewalls to protect data access and the activation of periodic backup procedures. More details about the virtual environment set are provided in Supplementary Materials – section 3 . eCRFs has been designed using the REDCap's Online Designer functionality. Through various REDCap specific features, it has been possible to limit missing data and apply customized compilation logic to data collection. Before starting the collection of real data, the registry has been tested by healthcare personnel to highlight any issues, shortcomings, or improvements to be made. After validation, the test platform was deactivated, and the production environment has been updated with all IPNOS users (Fig. 5 ). 3. Results The IPNOS electronic data registry is active since October 8, 2024. It is the first IT platform in Italy to host such a large collection of data on OSA patients. Data are entered in tabular format, with rows representing pseudonymized patients and columns corresponding to eCRF variables. The data registry structure consists of 27 eCRFs and 803 variables, which can be grouped into the main topics shown in the tables below (Table 1 and Table 2 ). Table 1 eCRFs of the IPNOS data registry. Patient History eCRF Variables’ number Personal Data 26 Driving History 24 Symptoms and Signs 37 Clinical Assessment Comorbidities 74 Drugs 6 Anthropometric Measurements 13 Clinical Exams Cardiological Assessment 46 Blood Chemistry 69 Sleep Study 38 Sleep History 74 Pulmonary Function Tests 32 Screening ABPM 12 Therapies Therapy Prescription 77 Questionnaires & Tests Anamnestic Questionnaire 44 Psychomotor Vigilance Task 13 Table 2 Validated questionnaires of the IPNOS data registry. eCRF Variables’ number Epworth Sleepness Scale 12 Insomnia Severity Index (ISI) 11 Morningness-Eveningness Questionnaire (MEQr) 9 Pittsburgh Sleep Quality Index (PSQI) 35 Hospital Anxiety Depression Scale (HADS) 20 Fatigue Severity Scale (FSS) 14 CGI/PGI Severity Scale 4 CGI/PGI Improvement Scale 4 Restless Legs Syndrome Rating Scale (IRLS) 12 Difficulties in Emotion Regulation Scale (DERS) 30 Perth Emotional Reactivity Scale (PERS) 28 Montreal Cognitive Assessment (MOCA) 12 Several eCRFs have been configured as “repeatable,” allowing them to be completed multiple times for the same participant. The repeatable Drugs eCRF is designed to collect data for one medication at a time, including its Anatomical Therapeutic Chemical (ATC) code. REDCap supports the linkage of eCRF variables to ontologies available through the National Center for Biomedical Ontology (NCBO) BioPortal, an online repository hosting more than 450 biomedical ontologies [ 20 ]. Biomedical ontologies provide standardized, machine-readable vocabularies that enhance semantic interoperability. Within REDCap, integration with BioPortal enables the automatic adoption of standardized terminology and has been implemented for the ATC code variable in the Drugs eCRF. Patients undergo multiple clinical evaluations. Polysomnography, considered the gold standard for diagnosing sleep disorders such as OSA [ 21 ], records parameters including brain activity, heart rate, respiratory patterns, oxygen saturation, and snoring during sleep. Information about prescribed therapy for the treatment of OSA (Positive Airway Pressure - PAP, weight loss, etc.) are also collected. Various validated and customized sleep questionnaires are completed directly by patients. Validated questionnaires consist of standardized items that generate a final score, which is automatically calculated by the registry using the Calculated Field functionality. Finally, the Psychomotor Vigilance Task (PVT) is administered to assess patients’ psychomotor reactivity [ 22 ]. IPNOS data collection is organized across multiple time points, specifically including a baseline assessment and a follow-up evaluation conducted at least 6 months after the initial visit. Additional follow-up time points can be readily incorporated as needed. At present, 16 centers and 65 authorized users contribute data to the registry, each operating under predefined role-based access privileges. 3.1 Electronic patient reported outcomes Traditionally, sleep questionnaires are administered to patients in paper form during their initial visit. However, paper-based patient-reported outcomes are associated with higher rates of missing data, transcription errors, and limited standardization To address these limitations, digital questionnaires have been implemented to standardize patient responses and enable direct data capture within the registry, thereby reducing the burden of manual transcription from paper forms. By leveraging the Survey functionality within REDCap, it has been possible to configure all the questionnaires for online administration. The majority of items have been set as mandatory fields significantly decreasing the proportion of missing data. The digital questionnaires have been administered on-site during hospital visits, optimizing patient waiting times through the use of tablets provided by the facilities. Patients received support from dedicated healthcare personnel, who were available to assist both content-related questions and technical issues. This approach has been implemented in more than 100 patients at the Maugeri Institute in Pavia and Auxologico in Milan, the centers with the highest enrollment rates. Among the 150 critical issues identified, 30% were technical in nature (primarily related to tablet usage), whereas 60% concerned difficulties in interpreting questionnaire content. These findings led to targeted revisions and partial refinement of several questionnaire sections. 3.2 Quality checks of data collection The availability of high-quality data is essential for conducting robust analyses and generating reliable results supported by sound scientific evidence [ 23 – 24 ]. REDCap provides predefined validation rules to identify missing data, out-of-range values, and errors in automated calculations. In addition, customized rules have been implemented using the Data Quality module to detect more specific inconsistencies and omissions. To date, 47 ad hoc data quality rules and 58 dedicated reports have been implemented to investigate specific characteristics of the study population. A data manager has been appointed to supervise ongoing data quality activities, and a designated monitor figure is responsible for conducting periodic quality checks and resolving identified issues. 3.3 Overall system architecture The developed electronic registry is part of a more complex IT architecture that ensures interoperability among different systems, as shown in Fig. 6 . REDCap also includes a Data Import Tool that supports the upload of Comma-Separated Values (CSV) files formatted according to platform specifications. This functionality has been used to integrate the registry with hospital information systems, enabling the automated import of pre-existing data through customized procedures. In particular, a dedicated routine was developed to import blood chemistry test results already available in digital format from external systems. For administration of the PVT test, each center has been equipped with a dedicated workstation on which the specific test software is installed. This software generates a CSV output file, that is subsequently formatted according to REDCap requirements and uploaded via the Data Import Tool. By contrast, all questionnaires and sleep assessment scales completed digitally by patients using Survey functionality required neither manual nor automated CSV-based data entry. Patients responses are captured and archived in real time within the platform in a structured and standardized format. 4. Discussion The present paper describes the methodology and decision-making process underlying the development of the first electronic data registry in Italy dedicated to sleep medicine, with a specific focus on OSA disorder. The registry has demonstrated its capacity to centralize heterogeneous data while ensuring consistency, completeness, and accessibility across diverse clinical workflows. It standardizes data collection among all participating centers, thereby minimizing challenges related to data harmonization and integration. The electronic data capture framework reduces transcription errors, facilitates longitudinal follow-up, and enables rapid aggregation and analysis of results. 4.1 Limitations Despite these advances, the initial phase of data collection still frequently relies on paper-based methods. The use of EDC tools requires basic digital literacy, which may represent a barrier, particularly among elderly patients, in the absence of caregivers or dedicated support staff. Furthermore, paper-based data collection does not require dedicated IT infrastructure, which may be limited in hospital settings due to financial and organizational constraints. Consequently, the time and operational burden associated with manual data entry procedures by healthcare professionals remain substantial. Although electronic tools such as REDCap streamline and accelerate data entry processes, dedicated personnel for paper-to-electronic transcription are often still required. The hybrid strategy employed in the IPNOS study, which combines both paper-based and digital data collection, represents a pragmatic solution to mitigate these challenges [ 25 ]. 4.2 Future perspectives To further reduce manual data entry, the current architecture is being enhanced through the implementation of a system capable of autonomously processing Portable Document Format (PDF) documents containing unstructured information. This process generates a structured file containing the data extracted from the original PDF, which can then be automatically adapted and input into REDCap. Within the IPNOS study, this solution has been developed to analyze the PDF text reports produced by the polysomnography examinations, which typically produce unstructured PDF document for each patient. This system is currently under development by Auxologico and Maugeri research teams in collaboration with an external company specialized in medical systems and devices [ 26 ]. The system implements the Reliable Extractive Question Answering (REQA) algorithm, which streamlines the creation of objects representing structured report templates [ 27 ], thus fostering a certifiable interpretation of the information contained within the relevant reports. It employs generative Artificial Intelligence (AI) and advanced Natural Language Processing (NLP) preprocessing pipelines and language models to effectively parse documents and respond to user-defined inquiries. The REQA system produces a JavaScript Object Notation (JSON) or CSV output, associating a confidence score to the extracted information. The flow described is presented in Fig. 7 . Figure 8 shows the workflow followed for each patient: at each center, every polysomnography produces a PDF report, which is provided as input to REQA algorithm to extract the relevant sleep data in a structured format. A developed IT routine processes the output file and formats it as required by REDCap. The incorporation of AI tools into Aux Reader introduces a significant innovation in IPNOS data collection. We would like to further enhance the ability of patients to digitally complete all IPNOS questionnaires. The tested tablet-based questionnaires can only be completed while patients are in the hospital. Furthermore, it is always necessary to identify dedicated personnel responsible for managing this activity, taking away time from other outpatient activities. For this reason, further steps will allow patients to fill out questionnaires digitally directly from home, via email, or on their smartphones. To do this, the registry can be integrated with MyCap, a free mobile application for patients that can be installed on iOS and Android devices to capture patient-reported results for any REDCap project [ 28 ]. Patients will be able to receive the list of questionnaires directly on the app installed on their devices, and their responses will be immediately synchronized and stored into the registry via a Secure Sockets Layer (SSL) connection. Patients create a 6-digit Personal Identification Number (PIN) that is used to open the app, ensuring maximum protection for their collected data. The use of MyCap offers significant advantages, but requires further ethical considerations as it involves managing patient healthcare data outside the hospital. MyCap is currently being reviewed by the DPO in order to reformulate the EC procedures necessary for its use. To enhance the registry’s applicability in future follow-up and multicenter studies and facilitate its use in international research, it will be standardized with globally recognized frameworks such as HL7 Fast Healthcare Interoperability Resources (FHIR) and Observational Medical Outcomes Partnership (OMOP). 5. Conclusions Sleep medicine is a rapidly evolving field, and in recent years Italian research centers have increasingly contributed to its international advancement. In this context, access to a large, structured, and interoperable data source is a strategic asset: it enhances research quality, promotes multicenter collaboration, and facilitates the comparison of findings with those from other international settings. The IPNOS registry is an innovative and valuable resource that can help bridge the current gap in detailed national data on OSA phenotypes, thus strengthening Italy’s role within the international research community. The creation of a shared and interoperable database therefore responds to an immediate need for standardization and quality, but at the same time, the methodology presented here lays the foundation for a sustainable research model that can be replicated in other research fields. Abbreviations OSAs: Obstructive Sleep Apnea Syndrome. ESADA: European Sleep Apnea Database. IPNOS: Italian PheNotypes Obstructive Sleep Apnea Study. AIMS: Italian Academy of Sleep Medicine. IT: Information Technology. GDPR: General Data Protection Regulation. IRCCS: Scientific Hospital and Care Institutes. DPO: Data Protection Officer. EC: Ethics Committees. FAIR: Findable, Accessible, Interoperable, Reusable. API: Application Programming Interface. PVT: Psychomotor Vigilance Task. EDC: Electronic Data Collection. eCRF: Electronic Case Report Form. REDCap: Research Electronic Data Collection. DAGs: Data Access Groups. NCBO: National Center for Biomedical Ontology. PAP: Positive Airway Pressure. CSV: Comma-Separated Values. REQA: Reliable Extractive Question Answering. AI: Artificial Intelligence. NLP: Natural Language Processing. JSON: JavaScript Object Notation. SSL: Secure Sockets Layer. PIN: Personal Identification Number. ATC: Anatomical Therapeutic Chemical Classification. FHIR: Fast Healthcare Interoperability Resources. OMOP: Observational Medical Outcomes Partnership. Declarations Ethics approval and consent to participate The study protocol was approved by the internal Ethics Committee of Istituto Auxologico Italiano of Milan on December 21th 2022 (protocol number 2022_12_21_01). Informed consent was obtained from all subjects in accordance with the Declaration of Helsinki. Consent for publication Informed consent for publication was obtained from all subjects in accordance with the Declaration of Helsinki. Availability of data and materials The data supporting the results of this study are used under license for the ongoing study and not publicly available. The data are available from the authors upon reasonable request and with the permission of Carolina Lombardi, Istituto Auxologico Italiano IRCCS, Milan, Italy. Competing interests The authors declare that they have no competing interests. Funding This publication was funded by the Italian Ministry of Health under the National Recovery and Resilience Plan (PNRR), Project “Italian PheNotypes Obstructive sleep Apnea Study (IPNOS)”, Grant No. PNRR-MAD-2022-12375812. Authors’ contributions The authors confirm contribution to the paper as follows: study conception and design: All Authors. Analysis and interpretation of results: All Authors. Draft manuscript preparation: CMP, VT. All authors reviewed the results and approved the final version of the manuscript. Acknowledgements Not applicable. References Gell LK, Mehta K, Esmaeili N, Taranto-Montemurro L, Sands SA, Pittman SD, et al. Performance evaluation of a ring-worn pulse oximeter for the identification and monitoring of obstructive sleep apnea. Front Sleep. 2025;4:1549272. 10.3389/frsle.2025.1549272 . Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ, et al. 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Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 25 Mar, 2026 Editor invited by journal 02 Mar, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 27 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8968393","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612418868,"identity":"9c8c9b2b-efd0-4872-9c04-94dca97e7b4e","order_by":0,"name":"Chiara Maria Palo","email":"","orcid":"","institution":"Istituti Clinici Scientifici Maugeri","correspondingAuthor":false,"prefix":"","firstName":"Chiara","middleName":"Maria","lastName":"Palo","suffix":""},{"id":612418870,"identity":"0d5e1f06-48b6-4ec4-b482-0aa3a7434c51","order_by":1,"name":"Valentina Tibollo","email":"","orcid":"","institution":"Istituti Clinici Scientifici Maugeri","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Tibollo","suffix":""},{"id":612418872,"identity":"103457e6-d36e-4613-b671-1c59a8cd9b10","order_by":2,"name":"Francesco Fanfulla","email":"data:image/png;base64,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","orcid":"","institution":"Istituti Clinici Scientifici Maugeri","correspondingAuthor":true,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Fanfulla","suffix":""},{"id":612418879,"identity":"357c2fe7-3958-4b72-8013-5e2908101639","order_by":3,"name":"Vanessa Tang","email":"","orcid":"","institution":"Istituti Clinici Scientifici Maugeri","correspondingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Tang","suffix":""},{"id":612418881,"identity":"f6b0ac4c-5a2d-44de-aedf-3ce779c63abf","order_by":4,"name":"Riccardo Bellazzi","email":"","orcid":"","institution":"University of Pavia","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Bellazzi","suffix":""},{"id":612418883,"identity":"72657e22-6db3-4e0b-a5c8-bcc84a21ac00","order_by":5,"name":"Andrea Romigi","email":"","orcid":"","institution":"Istituto Neurologico Mediterraneo","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Romigi","suffix":""},{"id":612418886,"identity":"d901f890-d0e0-4c9b-b868-bd512384b0a7","order_by":6,"name":"Maria Rosaria Bonsignore","email":"","orcid":"","institution":"Ospedale Vincenzo Cervello","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Rosaria","lastName":"Bonsignore","suffix":""},{"id":612418889,"identity":"39028548-bf7b-46e3-8828-33c0a429cd57","order_by":7,"name":"Fabio Colombo","email":"","orcid":"","institution":"Istituto Auxologico Italiano","correspondingAuthor":false,"prefix":"","firstName":"Fabio","middleName":"","lastName":"Colombo","suffix":""},{"id":612418895,"identity":"9b54f3b3-c0c3-48cc-badc-ad13101d06b8","order_by":8,"name":"Carolina Lombardi","email":"","orcid":"","institution":"Istituto Auxologico Italiano","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Lombardi","suffix":""}],"badges":[],"createdAt":"2026-02-25 13:39:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8968393/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8968393/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105574724,"identity":"ba846424-7f4b-47af-8458-c728c3656a05","added_by":"auto","created_at":"2026-03-27 13:36:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46721,"visible":true,"origin":"","legend":"\u003cp\u003eIPNOS hub \u0026amp; spokemodel\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/211549d38ebdec7d7f69c28f.png"},{"id":105575080,"identity":"e987d99c-7449-4d37-8bdf-80c82b76253d","added_by":"auto","created_at":"2026-03-27 13:37:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56386,"visible":true,"origin":"","legend":"\u003cp\u003eOverall methodological approach followed during IPNOS registry’s development\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/d3edd4e7bf7967f281311639.png"},{"id":105575311,"identity":"052e7540-080d-4dd8-b740-5ceddbfe9f42","added_by":"auto","created_at":"2026-03-27 13:38:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143985,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological phases followed during registry development\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/f0f11d044e8be512ce84e414.png"},{"id":105574962,"identity":"4a847378-5e9a-474f-a743-c96e7d917c93","added_by":"auto","created_at":"2026-03-27 13:37:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144906,"visible":true,"origin":"","legend":"\u003cp\u003eREDCap operating model in multicenter studies\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/3b8b8ce85eaf6ab51322a1bf.png"},{"id":105575138,"identity":"76ea314d-fa89-4fe4-9139-698ccb5da994","added_by":"auto","created_at":"2026-03-27 13:37:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84219,"visible":true,"origin":"","legend":"\u003cp\u003eREDCap data registry validation phase\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/b5d38ebbf68277b17cabea3f.png"},{"id":105574747,"identity":"ba831f99-43b3-4b5e-9ffa-2b7baf950aaa","added_by":"auto","created_at":"2026-03-27 13:36:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135148,"visible":true,"origin":"","legend":"\u003cp\u003eIPNOS infrastructure\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/d8af4fcfd248b483bb2bdb74.png"},{"id":105575102,"identity":"667359b0-4611-471e-b7cd-2903794dbacc","added_by":"auto","created_at":"2026-03-27 13:37:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135209,"visible":true,"origin":"","legend":"\u003cp\u003ePolysomnographic reports’ processing with Aux Reader solution\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/70eaf3994e8034ddab02f656.png"},{"id":105575456,"identity":"16c1845e-a11d-4776-9544-60e31aa91863","added_by":"auto","created_at":"2026-03-27 13:39:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":210541,"visible":true,"origin":"","legend":"\u003cp\u003eData processing flow from patient polysomnography to structured data entry\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/fb83a3f51f24bf70547eae9e.png"},{"id":105752207,"identity":"5d87e5c2-6e0d-480e-88f1-10aac9a97d3f","added_by":"auto","created_at":"2026-03-30 15:55:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1611385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/012298ec-4987-49dc-ac36-abb51b3beb0a.pdf"},{"id":105574746,"identity":"90c5eefe-8d7f-4820-8a7e-ec707975a4bf","added_by":"auto","created_at":"2026-03-27 13:36:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22568,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8968393/v1/7093623727d8c5c0a7663a60.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and development of the first electronic multicentric data registry of obstructive sleep apnea in Italy: clinical workflow integrated with digital data management and artificial intelligence","fulltext":[{"header":"1. Background","content":"\u003cp\u003eObstructive sleep apnea (OSA) is a high prevalent disorder with substantial clinical impact [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Italy, approximately 12\u0026nbsp;million people experience sleep disorders, and OSA may affect up to 15% of individuals over the age of 40 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe European Sleep Apnea Database (ESADA) study provided the European profile of OSA in recent years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Marked differences have been described across European macro-regions in terms of symptoms and associated complications [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the Italian population remains largely unexplored, with limited evidence on the prevalence of OSA, its clinical presentation, and associated comorbidities. Moreover, substantial variability exists across the country in terms of diagnostic availability, technological resources, and scientific approaches to the management of this condition. This heterogeneity may become even more pronounced in the near future, given the rapid expansion of centers involved in sleep disorders.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 The project\u003c/h2\u003e \u003cp\u003eThe Italian Phenotypes Obstructive Sleep Apnea Study (IPNOS) consortium \u0026ndash; an Italian network of sleep medicine centers endorsed by the Italian Academy of Sleep Medicine (AIMS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] \u0026ndash; was established to investigate OSA in Italy. The consortium is structured according to a \u003cem\u003ehub-and-spoke\u003c/em\u003e model, with four centers hubs (including the coordinating center and three principal units) and twelve spokes centers that support the recruitment process, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe main coordinating center is Istituto Auxologico Italiano in Milan, which collaborates with three leading centers: Istituti Clinici Scientifici Maugeri in Pavia (Northern Italy), IRCCS Neuromed Pozzilli in Isernia (Central Italy), and Ospedali Riuniti Villa Sofia-Cervello in Palermo (Southern Italy). This organizational core (the \u003cem\u003ehubs\u003c/em\u003e) is connected to the AIMS network, which includes other twelve centers. The full list of sleep medicine centers involved in the IPNOS consortium is provided in \u003cem\u003eSupplementary Materials \u0026ndash;\u003c/em\u003e section \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe IPNOS study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] is supported by a grant of Italian Ministry of Health (PNRR-MAD-2022-12375812) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and firstly approved by the internal Ethics Committee (EC) of Istituto Auxologico Italiano of Milan on December 21th 2022 (protocol number 2022_12_21_01). This study aims to analyze patients with suspected OSA through a national electronic registry designed to collect standardized clinical data across multiple centers. The objectives of the registry are as follows: i) to establish the largest registry of Italian patients with OSA; ii) to conduct cross-sectional, prospective, or long-term follow-up studies aimed at improving diagnosis and treatment; iii) to promote education, and standardization of procedures among participating Italian centers; iv) to foster collaborative links with other databases, such as ESADA.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe methodological approach used during the creation of the IPNOS registry is structured as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn inventory of user needs, information technology (IT) and ethical requirements has been conducted to identify the most suitable IT platform available to develop the IPNOS registry, also considering the restrictions of current regulations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A dedicated multidisciplinary working team has been established, including: i) clinicians responsible for the clinical management of the disorder; ii) engineers and computer scientists providing the IT competencies required during the registry design; iii) statisticians supporting the analysis of the collected data [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The methodology followed during registry\u0026rsquo;s development is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Requirements evaluation and variables definition\u003c/h2\u003e \u003cp\u003eThe development of an electronic registry in healthcare requires protection of data ownership and controlled use and sharing of patient information, as defined in the General Data Protection Regulation (GDPR) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The Data Protection Officer (DPO) guarantees compliance with regulations by each center and data security is strongly ensured through the following measures: i) avoid storing highly sensitive data from patients; ii) keep track of every type of action performed on data (logging); iii) limit visibility on data; iv) guarantee the Findable, Accessible, Interoperable and Reusable (FAIR) principles and data minimization [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; v) ensure pseudonymization of data [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe respective ECs have approved a data management framework whereby each center has access exclusively to its own data. Full access to the complete dataset is restricted to the study\u0026rsquo;s coordinating investigators. An external company [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], contracted within the IPNOS study to manage the platform hosting the registry, has been formally designated as the Data Processor, due to its comprehensive visibility over all centers\u0026rsquo; data for maintenance purposes. Each participating center acts as an independent Data Controller for its own data and has executed specific agreements to formally appoint the company in this role.\u003c/p\u003e \u003cp\u003eRegistry users can be categorized as data providers and data collectors, each with distinct requirements. Data providers \u0026ndash; including patients, research laboratories, clinicians, and hospital information systems \u0026ndash; require a system capable of accommodating multiple data formats, integrating heterogeneous data sources, and capturing patient-reported outcomes. Data collectors \u0026ndash; such as clinicians, laboratory technicians, patients, and IT staff \u0026ndash; require rapid, structured, and intuitive data entry processes. IT operators additionally require a highly customizable digital platform with access to database queries and application programming interfaces (APIs) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough close collaboration between healthcare professionals and technical staff, the full set of variables to be collected has been defined. Clinical data, validated and customized questionnaires were structured for longitudinal collection. These steps constituted the foundation for the selection and development of the registry platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Platform selection\u003c/h2\u003e \u003cp\u003eAmong different open-source electronic data collection (EDC) platforms, Research Electronic Data Capture (REDCap) has emerged as one of the most robust and established solutions, combining high reliability with a stable governance model and comprehensive documentation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. More details about REDCap platform are provided in \u003cem\u003eSupplementary Materials \u0026ndash;\u003c/em\u003e sections \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 REDCap in multicenter studies\u003c/h2\u003e \u003cp\u003eIn case of multicenter studies, the Data Access Groups (DAGs) functionality of REDCap allows us to manage the different centers involved. Each center is identified by a representative name and a unique code and it is associated with its respective users. All centers\u0026rsquo; data are stored on a common REDCap-based server, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Platform design\u003c/h2\u003e \u003cp\u003eThe REDCap registry design phase began in January 2024 and took approximately six months to complete. Before proceeding with eCRFs implementation, the virtual environment to host REDCap has been set with firewalls to protect data access and the activation of periodic backup procedures. More details about the virtual environment set are provided in \u003cem\u003eSupplementary Materials \u0026ndash;\u003c/em\u003e section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eeCRFs has been designed using the REDCap's Online Designer functionality. Through various REDCap specific features, it has been possible to limit missing data and apply customized compilation logic to data collection.\u003c/p\u003e \u003cp\u003eBefore starting the collection of real data, the registry has been tested by healthcare personnel to highlight any issues, shortcomings, or improvements to be made. After validation, the test platform was deactivated, and the production environment has been updated with all IPNOS users (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe IPNOS electronic data registry is active since October 8, 2024. It is the first IT platform in Italy to host such a large collection of data on OSA patients. Data are entered in tabular format, with rows representing pseudonymized patients and columns corresponding to eCRF variables.\u003c/p\u003e \u003cp\u003eThe data registry structure consists of 27 eCRFs and 803 variables, which can be grouped into the main topics shown in the tables below (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eeCRFs of the IPNOS data registry.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient History\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eeCRF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u0026rsquo; number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersonal Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDriving History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymptoms and Signs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnthropometric Measurements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Exams\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiological Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood Chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulmonary Function Tests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScreening ABPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy Prescription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuestionnaires \u0026amp; Tests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnamnestic Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychomotor Vigilance Task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidated questionnaires of the IPNOS data registry.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeCRF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u0026rsquo; number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpworth Sleepness Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia Severity Index (ISI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorningness-Eveningness Questionnaire (MEQr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index (PSQI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Anxiety Depression Scale (HADS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue Severity Scale (FSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGI/PGI Severity Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGI/PGI Improvement Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestless Legs Syndrome Rating Scale (IRLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulties in Emotion Regulation Scale (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerth Emotional Reactivity Scale (PERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMontreal Cognitive Assessment (MOCA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeveral eCRFs have been configured as \u0026ldquo;repeatable,\u0026rdquo; allowing them to be completed multiple times for the same participant. The repeatable Drugs eCRF is designed to collect data for one medication at a time, including its Anatomical Therapeutic Chemical (ATC) code. REDCap supports the linkage of eCRF variables to ontologies available through the National Center for Biomedical Ontology (NCBO) BioPortal, an online repository hosting more than 450 biomedical ontologies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Biomedical ontologies provide standardized, machine-readable vocabularies that enhance semantic interoperability. Within REDCap, integration with BioPortal enables the automatic adoption of standardized terminology and has been implemented for the ATC code variable in the Drugs eCRF.\u003c/p\u003e \u003cp\u003ePatients undergo multiple clinical evaluations. Polysomnography, considered the gold standard for diagnosing sleep disorders such as OSA [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], records parameters including brain activity, heart rate, respiratory patterns, oxygen saturation, and snoring during sleep. Information about prescribed therapy for the treatment of OSA (Positive Airway Pressure - PAP, weight loss, etc.) are also collected.\u003c/p\u003e \u003cp\u003eVarious validated and customized sleep questionnaires are completed directly by patients. Validated questionnaires consist of standardized items that generate a final score, which is automatically calculated by the registry using the Calculated Field functionality. Finally, the Psychomotor Vigilance Task (PVT) is administered to assess patients\u0026rsquo; psychomotor reactivity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIPNOS data collection is organized across multiple time points, specifically including a baseline assessment and a follow-up evaluation conducted at least 6 months after the initial visit. Additional follow-up time points can be readily incorporated as needed.\u003c/p\u003e \u003cp\u003eAt present, 16 centers and 65 authorized users contribute data to the registry, each operating under predefined role-based access privileges.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Electronic patient reported outcomes\u003c/h2\u003e \u003cp\u003eTraditionally, sleep questionnaires are administered to patients in paper form during their initial visit. However, paper-based patient-reported outcomes are associated with higher rates of missing data, transcription errors, and limited standardization\u003c/p\u003e \u003cp\u003eTo address these limitations, digital questionnaires have been implemented to standardize patient responses and enable direct data capture within the registry, thereby reducing the burden of manual transcription from paper forms. By leveraging the Survey functionality within REDCap, it has been possible to configure all the questionnaires for online administration. The majority of items have been set as mandatory fields significantly decreasing the proportion of missing data. The digital questionnaires have been administered on-site during hospital visits, optimizing patient waiting times through the use of tablets provided by the facilities. Patients received support from dedicated healthcare personnel, who were available to assist both content-related questions and technical issues.\u003c/p\u003e \u003cp\u003eThis approach has been implemented in more than 100 patients at the Maugeri Institute in Pavia and Auxologico in Milan, the centers with the highest enrollment rates. Among the 150 critical issues identified, 30% were technical in nature (primarily related to tablet usage), whereas 60% concerned difficulties in interpreting questionnaire content. These findings led to targeted revisions and partial refinement of several questionnaire sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Quality checks of data collection\u003c/h2\u003e \u003cp\u003eThe availability of high-quality data is essential for conducting robust analyses and generating reliable results supported by sound scientific evidence [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. REDCap provides predefined validation rules to identify missing data, out-of-range values, and errors in automated calculations. In addition, customized rules have been implemented using the Data Quality module to detect more specific inconsistencies and omissions. To date, 47 ad hoc data quality rules and 58 dedicated reports have been implemented to investigate specific characteristics of the study population. A data manager has been appointed to supervise ongoing data quality activities, and a designated monitor figure is responsible for conducting periodic quality checks and resolving identified issues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Overall system architecture\u003c/h2\u003e \u003cp\u003eThe developed electronic registry is part of a more complex IT architecture that ensures interoperability among different systems, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eREDCap also includes a Data Import Tool that supports the upload of Comma-Separated Values (CSV) files formatted according to platform specifications. This functionality has been used to integrate the registry with hospital information systems, enabling the automated import of pre-existing data through customized procedures. In particular, a dedicated routine was developed to import blood chemistry test results already available in digital format from external systems.\u003c/p\u003e \u003cp\u003eFor administration of the PVT test, each center has been equipped with a dedicated workstation on which the specific test software is installed. This software generates a CSV output file, that is subsequently formatted according to REDCap requirements and uploaded via the Data Import Tool.\u003c/p\u003e \u003cp\u003eBy contrast, all questionnaires and sleep assessment scales completed digitally by patients using Survey functionality required neither manual nor automated CSV-based data entry. Patients responses are captured and archived in real time within the platform in a structured and standardized format.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present paper describes the methodology and decision-making process underlying the development of the first electronic data registry in Italy dedicated to sleep medicine, with a specific focus on OSA disorder.\u003c/p\u003e \u003cp\u003eThe registry has demonstrated its capacity to centralize heterogeneous data while ensuring consistency, completeness, and accessibility across diverse clinical workflows. It standardizes data collection among all participating centers, thereby minimizing challenges related to data harmonization and integration. The electronic data capture framework reduces transcription errors, facilitates longitudinal follow-up, and enables rapid aggregation and analysis of results.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations\u003c/h2\u003e \u003cp\u003eDespite these advances, the initial phase of data collection still frequently relies on paper-based methods. The use of EDC tools requires basic digital literacy, which may represent a barrier, particularly among elderly patients, in the absence of caregivers or dedicated support staff. Furthermore, paper-based data collection does not require dedicated IT infrastructure, which may be limited in hospital settings due to financial and organizational constraints.\u003c/p\u003e \u003cp\u003eConsequently, the time and operational burden associated with manual data entry procedures by healthcare professionals remain substantial. Although electronic tools such as REDCap streamline and accelerate data entry processes, dedicated personnel for paper-to-electronic transcription are often still required.\u003c/p\u003e \u003cp\u003eThe hybrid strategy employed in the IPNOS study, which combines both paper-based and digital data collection, represents a pragmatic solution to mitigate these challenges [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Future perspectives\u003c/h2\u003e \u003cp\u003eTo further reduce manual data entry, the current architecture is being enhanced through the implementation of a system capable of autonomously processing Portable Document Format (PDF) documents containing unstructured information. This process generates a structured file containing the data extracted from the original PDF, which can then be automatically adapted and input into REDCap.\u003c/p\u003e \u003cp\u003eWithin the IPNOS study, this solution has been developed to analyze the PDF text reports produced by the polysomnography examinations, which typically produce unstructured PDF document for each patient. This system is currently under development by Auxologico and Maugeri research teams in collaboration with an external company specialized in medical systems and devices [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The system implements the Reliable Extractive Question Answering (REQA) algorithm, which streamlines the creation of objects representing structured report templates [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], thus fostering a certifiable interpretation of the information contained within the relevant reports. It employs generative Artificial Intelligence (AI) and advanced Natural Language Processing (NLP) preprocessing pipelines and language models to effectively parse documents and respond to user-defined inquiries. The REQA system produces a JavaScript Object Notation (JSON) or CSV output, associating a confidence score to the extracted information. The flow described is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the workflow followed for each patient: at each center, every polysomnography produces a PDF report, which is provided as input to REQA algorithm to extract the relevant sleep data in a structured format. A developed IT routine processes the output file and formats it as required by REDCap. The incorporation of AI tools into Aux Reader introduces a significant innovation in IPNOS data collection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe would like to further enhance the ability of patients to digitally complete all IPNOS questionnaires. The tested tablet-based questionnaires can only be completed while patients are in the hospital. Furthermore, it is always necessary to identify dedicated personnel responsible for managing this activity, taking away time from other outpatient activities.\u003c/p\u003e \u003cp\u003eFor this reason, further steps will allow patients to fill out questionnaires digitally directly from home, via email, or on their smartphones. To do this, the registry can be integrated with MyCap, a free mobile application for patients that can be installed on iOS and Android devices to capture patient-reported results for any REDCap project [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Patients will be able to receive the list of questionnaires directly on the app installed on their devices, and their responses will be immediately synchronized and stored into the registry via a Secure Sockets Layer (SSL) connection. Patients create a 6-digit Personal Identification Number (PIN) that is used to open the app, ensuring maximum protection for their collected data. The use of MyCap offers significant advantages, but requires further ethical considerations as it involves managing patient healthcare data outside the hospital. MyCap is currently being reviewed by the DPO in order to reformulate the EC procedures necessary for its use.\u003c/p\u003e \u003cp\u003eTo enhance the registry\u0026rsquo;s applicability in future follow-up and multicenter studies and facilitate its use in international research, it will be standardized with globally recognized frameworks such as HL7 Fast Healthcare Interoperability Resources (FHIR) and Observational Medical Outcomes Partnership (OMOP).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eSleep medicine is a rapidly evolving field, and in recent years Italian research centers have increasingly contributed to its international advancement. In this context, access to a large, structured, and interoperable data source is a strategic asset: it enhances research quality, promotes multicenter collaboration, and facilitates the comparison of findings with those from other international settings. The IPNOS registry is an innovative and valuable resource that can help bridge the current gap in detailed national data on OSA phenotypes, thus strengthening Italy\u0026rsquo;s role within the international research community.\u003c/p\u003e \u003cp\u003eThe creation of a shared and interoperable database therefore responds to an immediate need for standardization and quality, but at the same time, the methodology presented here lays the foundation for a sustainable research model that can be replicated in other research fields.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOSAs: Obstructive Sleep Apnea Syndrome. ESADA: European Sleep Apnea Database. IPNOS: Italian PheNotypes Obstructive Sleep Apnea Study. AIMS: Italian Academy of Sleep Medicine. IT: Information Technology. GDPR: General Data Protection Regulation. IRCCS: Scientific Hospital and Care Institutes. DPO: Data Protection Officer. EC: Ethics Committees. FAIR: Findable, Accessible, Interoperable, Reusable. API: Application Programming Interface. PVT: Psychomotor Vigilance Task. EDC: Electronic Data Collection. eCRF: Electronic Case Report Form. REDCap: Research Electronic Data Collection. DAGs: Data Access Groups. NCBO: National Center for Biomedical Ontology. PAP: Positive Airway Pressure. CSV: Comma-Separated Values. REQA: Reliable Extractive Question Answering. AI: Artificial Intelligence. NLP: Natural Language Processing. JSON: JavaScript Object Notation. SSL: Secure Sockets Layer. PIN: Personal Identification Number. ATC: Anatomical Therapeutic Chemical Classification. FHIR: Fast Healthcare Interoperability Resources. OMOP: Observational Medical Outcomes Partnership.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study protocol was approved by the internal Ethics Committee of Istituto Auxologico Italiano of Milan on December 21th 2022 (protocol number 2022_12_21_01).\u0026nbsp;Informed consent was obtained from all subjects in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eInformed consent for publication was obtained from all subjects in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data supporting the results of this study are used under license for the ongoing study and not publicly available. The data are available from the authors upon reasonable request and with the permission of Carolina Lombardi, Istituto Auxologico Italiano IRCCS, Milan, Italy.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis publication was funded by the Italian Ministry of Health under the National Recovery and Resilience Plan (PNRR), Project \u0026ldquo;Italian PheNotypes Obstructive sleep Apnea Study (IPNOS)\u0026rdquo;, Grant No. PNRR-MAD-2022-12375812.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eThe authors confirm contribution to the paper as follows: study conception and design: All Authors. Analysis and interpretation of results: All Authors. Draft manuscript preparation: CMP, VT. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGell LK, Mehta K, Esmaeili N, Taranto-Montemurro L, Sands SA, Pittman SD, et al. Performance evaluation of a ring-worn pulse oximeter for the identification and monitoring of obstructive sleep apnea. Front Sleep. 2025;4:1549272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/frsle.2025.1549272\u003c/span\u003e\u003cspan address=\"10.3389/frsle.2025.1549272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. 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Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sleep medicine, Obstructive sleep apnea, Multicentric study, Electronic data collection, REDCap platform, Artificial intelligence, Patient reported outcomes, Data governance, Data management, e-infrastructure.","lastPublishedDoi":"10.21203/rs.3.rs-8968393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8968393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eObstructive Sleep Apnea (OSA) is a high prevalent breathing condition, well characterized in Europe. However, data on the Italian population are still lacking in clinical presentation, polysomnographic characteristics, and comorbidities. The Italian PheNotypes Obstructive Sleep Apnea Study (IPNOS) aims to build the first electronic national registry for OSA research, investigating a population of more than 5,000 patients with suspected OSA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe electronic platform REDCap (Research Electronic Data Capture) has been chosen to develop the registry. Analyzing key user, digital and ethical requirements, we designed a tool that complies with current regulations on health data management and able to host a multicenter data collection. Customized computer routines ensure automatic data import from different hospital information systems and real-time quality checks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe IPNOS registry currently includes 27 electronic forms and 803 variables. To date, data from more than 5,300 patients have been collected across 16 Italian sleep centers. Patients can complete digitally questionnaires with automatic saving of responses within the REDCap platform, thereby reducing the burden associated with manual data transcription. An artificial intelligence tool enables the automated extraction of structured data from text polysomnography reports for direct integration into the registry.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe IPNOS registry is a new, interoperable platform for OSA research in Italy. It ensures multicenter collaboration, guaranteeing an easy expansion of the centers\u0026rsquo; network. This registry lays the groundwork for future studies and strengthens Italy\u0026rsquo;s contribution in global sleep medicine.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eClinicalTrials.gov, NCT06872242. Registered 12 March 2025. 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