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Mortality Prediction After Cardiac Surgery: Higgins Intensive Care Unit Admission Score Revisited
Linköping University, Department of Health, Medicine and Caring Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Thoracic and Vascular Surgery. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Anaesthesiology and Intensive Care in Norrköping.
Linköping University, Department of Health, Medicine and Caring Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Thoracic and Vascular Surgery.
Sahlgrenska University Hospital, Gothenburg, Sweden; Gothenburg University, Sweden.
Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, ANOPIVA US.ORCID iD: 0000-0002-5903-2918
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2020 (English)In: Annals of Thoracic Surgery, ISSN 0003-4975, E-ISSN 1552-6259, Vol. 110, no 5, p. 1589-1594Article in journal (Refereed) Published
Abstract [en]

Background. This study was performed to develop and validate a cardiac surgical intensive care risk adjustment model for mixed cardiac surgery based on a few preoperative laboratory tests, extracorporeal circulation time, and measurements at arrival to the intensive care unit. Methods. This was a retrospective study of admissions to 5 cardiac surgical intensive care units in Sweden that submitted data to the Swedish Intensive Care Registry. Admissions from 2008 to 2014 (n = 21,450) were used for model development, whereas admissions from 2015 to 2016 (n = 6463) were used for validation. Models were built using logistic regression with transformation of raw values or categorization into groups. Results. The final model showed good performance, with an area under the receiver operating characteristics curve of 0.86 (95% confidence interval, 0.83-0.89), a Cox calibration intercept of -0.16 (95% confidence interval, -0.47 to 0.19), and a slope of 1.01 (95% confidence interval, 0.89-1.13) in the validation cohort. Conclusions. Eleven variables available on admission to the intensive care unit can be used to predict 30-day mortality after cardiac surgery. The model performance was better than those of general intensive care risk adjustment models used in cardiac surgical intensive care and also avoided the subjective assessment of the cause of admission. The standardized mortality ratio improves over time in Swedish cardiac surgical intensive care. (C) 2020 by The Society of Thoracic Surgeons

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 110, no 5, p. 1589-1594
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:liu:diva-171661DOI: 10.1016/j.athoracsur.2020.03.036ISI: 000580647400058PubMedID: 32302658Scopus ID: 2-s2.0-85085165650OAI: oai:DiVA.org:liu-171661DiVA, id: diva2:1505395
Note

Funding Agencies|Link_oping University Institutional funds

Available from: 2020-11-30 Created: 2020-11-30 Last updated: 2021-10-04Bibliographically approved

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Engerström, LarsSjöberg, FolkeFredrikson, Mats

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Engerström, LarsFreter, WolfgangSjöberg, FolkeFredrikson, MatsWalther, Sten M.
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Department of Health, Medicine and Caring SciencesFaculty of Medicine and Health SciencesDepartment of Thoracic and Vascular SurgeryDepartment of Anaesthesiology and Intensive Care in NorrköpingDivision of Surgery, Orthopedics and OncologyDepartment of Hand and Plastic SurgeryANOPIVA USDivision of Inflammation and InfectionOccupational and Environmental Medicine CenterForum ÖstergötlandDivision of Diagnostics and Specialist Medicine
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Annals of Thoracic Surgery
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