The SingHealth Perioperative and Anesthesia Subject Area Registry (PASAR), a large-scale perioperative data mart and registry
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:77 |
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| Enthalten in: |
Korean journal of anesthesiology - 77(2024), 1 vom: 02. Feb., Seite 58-65 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Abdullah, Hairil Rizal [VerfasserIn] |
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| Links: |
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| Themen: |
Anesthesia |
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| Anmerkungen: |
Date Completed 05.02.2024 Date Revised 05.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.4097/kja.23580 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364273364 |
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| 500 | |a Date Revised 05.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a CONCLUSIONS: The PASAR provides a comprehensive and automated approach to gathering high-quality perioperative patient data | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Anesthesia | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Data science | |
| 650 | 4 | |a Intraoperative care | |
| 650 | 4 | |a Perioperative care | |
| 650 | 4 | |a Postoperative care | |
| 650 | 4 | |a Preoperative care | |
| 700 | 1 | |a Lim, Daniel Yan Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Ke, Yuhe |e verfasserin |4 aut | |
| 700 | 1 | |a Salim, Nur Nasyitah Mohamed |e verfasserin |4 aut | |
| 700 | 1 | |a Lan, Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Yizhi |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Mengling |e verfasserin |4 aut | |
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