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Lim L, Lee H, Jung CW, Sim D, Borrat X, Pollard TJ, Celi LA, Mark RG, Vistisen ST, Lee HC. INSPIRE, a publicly available research dataset for perioperative medicine. Sci Data 2024; 11:655. [PMID: 38906912 PMCID: PMC11192876 DOI: 10.1038/s41597-024-03517-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 06/13/2024] [Indexed: 06/23/2024] Open
Abstract
We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs in the operating theatre, general wards, and intensive care units (ICUs), laboratory results from six months before admission to six months after discharge, and medication during hospitalisation. Complications include total hospital and ICU length of stay and in-hospital death. We hope this dataset will inspire collaborative research and development in perioperative medicine and serve as a reproducible external validation dataset to improve surgical outcomes.
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Affiliation(s)
- Leerang Lim
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Hyeonhoon Lee
- Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Dayeon Sim
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Xavier Borrat
- Department of Anesthesia, Hospital Clinic de Barcelona, Barcelona, Spain
- Clinical Informatics Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Tom J Pollard
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Leo A Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Roger G Mark
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Simon T Vistisen
- Institute for Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Anaesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, South Korea.
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Hui V, Litton E, Edibam C, Geldenhuys A, Hahn R, Larbalestier R, Wright B, Pavey W. Using machine learning to predict bleeding after cardiac surgery. Eur J Cardiothorac Surg 2023; 64:ezad297. [PMID: 37669153 DOI: 10.1093/ejcts/ezad297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 07/29/2023] [Accepted: 09/03/2023] [Indexed: 09/07/2023] Open
Abstract
OBJECTIVES The primary objective was to predict bleeding after cardiac surgery with machine learning using the data from the Australia New Zealand Society of Cardiac and Thoracic Surgeons Cardiac Surgery Database, cardiopulmonary bypass perfusion database, intensive care unit database and laboratory results. METHODS We obtained surgical, perfusion, intensive care unit and laboratory data from a single Australian tertiary cardiac surgical hospital from February 2015 to March 2022 and included 2000 patients undergoing cardiac surgery. We trained our models to predict either the Papworth definition or Dyke et al.'s universal definition of perioperative bleeding. Our primary outcome was the performance of our machine learning algorithms using sensitivity, specificity, positive and negative predictive values, accuracy, area under receiver operating characteristics curve (AUROC) and area under precision-recall curve (AUPRC). RESULTS Of the 2000 patients undergoing cardiac surgery, 13.3% (226/2000) had bleeding using the Papworth definition and 17.2% (343/2000) had moderate to massive bleeding using Dyke et al.'s definition. The best-performing model based on AUPRC was the Ensemble Voting Classifier model for both Papworth (AUPRC 0.310, AUROC 0.738) and Dyke definitions of bleeding (AUPRC 0.452, AUROC 0.797). CONCLUSIONS Machine learning can incorporate routinely collected data from various datasets to predict bleeding after cardiac surgery.
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Affiliation(s)
- Victor Hui
- Department of Anaesthesia and Pain Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Heart Lung Research Institute of Western Australia, Perth, WA, Australia
| | - Edward Litton
- Department of Intensive Care, Fiona Stanley Hospital, Perth, WA, Australia
- School of Medicine, University of Western Australia, Perth, WA, Australia
| | - Cyrus Edibam
- Department of Intensive Care, Fiona Stanley Hospital, Perth, WA, Australia
| | - Agneta Geldenhuys
- Department of Cardiothoracic Surgery, Fiona Stanley Hospital, Perth, WA, Australia
| | - Rebecca Hahn
- Heart Lung Research Institute of Western Australia, Perth, WA, Australia
- Department of Cardiothoracic Surgery, Fiona Stanley Hospital, Perth, WA, Australia
| | - Robert Larbalestier
- Department of Cardiothoracic Surgery, Fiona Stanley Hospital, Perth, WA, Australia
| | - Brian Wright
- Department of Anaesthesia, Pain and Perioperative Medicine, Fiona Stanley Hospital, Perth, WA, Australia
| | - Warren Pavey
- Heart Lung Research Institute of Western Australia, Perth, WA, Australia
- Department of Anaesthesia, Pain and Perioperative Medicine, Fiona Stanley Hospital, Perth, WA, Australia
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