The SingHealth Perioperative and Anesthesia Subject Area Registry (PASAR), a large-scale perioperative data mart and registry.

Publisher: Korean Society of Anesthesiologists Country of Publication: Korea (South) NLM ID: 101502451 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2005-7563 (Electronic) Linking ISSN: 20056419 NLM ISO Abbreviation: Korean J Anesthesiol Subsets: MEDLINE

Original Publication: Seoul : Korean Society of Anesthesiologists

Data Warehousing* ; Anesthesia*; Humans ; Middle Aged ; Elective Surgical Procedures ; Patient Admission ; Registries

Background: To enhance perioperative outcomes, a perioperative registry that integrates high-quality real-world data throughout the perioperative period is essential. Singapore General Hospital established the Perioperative and Anesthesia Subject Area Registry (PASAR) to unify data from the preoperative, intraoperative, and postoperative stages. This study presents the methodology employed to create this database.
Methods: Since 2016, data from surgical patients have been collected from the hospital electronic medical record systems, de-identified, and stored securely in compliance with privacy and data protection laws. As a representative sample, data from initiation in 2016 to December 2022 were collected.
Results: As of December 2022, PASAR data comprise 26 tables, encompassing 153,312 patient admissions and 168,977 operation sessions. For this period, the median age of the patients was 60.0 years, sex distribution was balanced, and the majority were Chinese. Hypertension and cardiovascular comorbidities were also prevalent. Information including operation type and time, intensive care unit (ICU) length of stay, and 30-day and 1-year mortality rates were collected. Emergency surgeries resulted in longer ICU stays, but shorter operation times than elective surgeries.
Conclusions: The PASAR provides a comprehensive and automated approach to gathering high-quality perioperative patient data.

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AISG-100E-2020-055 National Research Foundation Singapore; I2101E0002 RIE2025 Industry Alignment Fund

Keywords: Anesthesia; Big data; Data science; Intraoperative care; Perioperative care; Postoperative care; Preoperative care

Date Created: 20231107 Date Completed: 20240205 Latest Revision: 20240611

20240611

PMC10834714

10.4097/kja.23580

37935575