Preoperative risk assessment tools for morbidity after cardiac surgery: a systematic review
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Author(s)
Sanders, Julie
Makariou, Nicole
Tocock, Adam
Magboo, Rosalie
Thomas, Ashley
Aitken, Leanne M
Griffith University Author(s)
Year published
2022
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BACKGROUND: Postoperative morbidity places considerable burden on health and resources. Thus, strategies to identify, predict, and reduce postoperative morbidity are needed. AIMS: To identify and explore existing preoperative risk assessment tools for morbidity after cardiac surgery. METHODS: Electronic databases (including MEDLINE, CINAHL, and Embase) were searched to December 2020 for preoperative risk assessment models for morbidity after adult cardiac surgery. Models exploring one isolated postoperative morbidity and those in patients having heart transplantation or congenital surgery were excluded. Data extraction and ...
View more >BACKGROUND: Postoperative morbidity places considerable burden on health and resources. Thus, strategies to identify, predict, and reduce postoperative morbidity are needed. AIMS: To identify and explore existing preoperative risk assessment tools for morbidity after cardiac surgery. METHODS: Electronic databases (including MEDLINE, CINAHL, and Embase) were searched to December 2020 for preoperative risk assessment models for morbidity after adult cardiac surgery. Models exploring one isolated postoperative morbidity and those in patients having heart transplantation or congenital surgery were excluded. Data extraction and quality assessments were undertaken by two authors. RESULTS: From 2251 identified papers, 22 models were found. The majority (54.5%) were developed in the USA or Canada, defined morbidity outcome within the in-hospital period (90.9%), and focused on major morbidity. Considerable variation in morbidity definition was identified, with morbidity incidence between 4.3% and 52%. The majority (45.5%) defined morbidity and mortality separately but combined them to develop one model, while seven studies (33.3%) constructed a morbidity-specific model. Models contained between 5 and 50 variables. Commonly included variables were age, emergency surgery, left ventricular dysfunction, and reoperation/previous cardiac surgery, although definition differences across studies were observed. All models demonstrated at least reasonable discriminatory power [area under the receiver operating curve (0.61-0.82)]. CONCLUSION: Despite the methodological heterogeneity across models, all demonstrated at least reasonable discriminatory power and could be implemented depending on local preferences. Future strategies to identify, predict, and reduce morbidity after cardiac surgery should consider the ageing population and those with minor and/or multiple complex morbidities.
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View more >BACKGROUND: Postoperative morbidity places considerable burden on health and resources. Thus, strategies to identify, predict, and reduce postoperative morbidity are needed. AIMS: To identify and explore existing preoperative risk assessment tools for morbidity after cardiac surgery. METHODS: Electronic databases (including MEDLINE, CINAHL, and Embase) were searched to December 2020 for preoperative risk assessment models for morbidity after adult cardiac surgery. Models exploring one isolated postoperative morbidity and those in patients having heart transplantation or congenital surgery were excluded. Data extraction and quality assessments were undertaken by two authors. RESULTS: From 2251 identified papers, 22 models were found. The majority (54.5%) were developed in the USA or Canada, defined morbidity outcome within the in-hospital period (90.9%), and focused on major morbidity. Considerable variation in morbidity definition was identified, with morbidity incidence between 4.3% and 52%. The majority (45.5%) defined morbidity and mortality separately but combined them to develop one model, while seven studies (33.3%) constructed a morbidity-specific model. Models contained between 5 and 50 variables. Commonly included variables were age, emergency surgery, left ventricular dysfunction, and reoperation/previous cardiac surgery, although definition differences across studies were observed. All models demonstrated at least reasonable discriminatory power [area under the receiver operating curve (0.61-0.82)]. CONCLUSION: Despite the methodological heterogeneity across models, all demonstrated at least reasonable discriminatory power and could be implemented depending on local preferences. Future strategies to identify, predict, and reduce morbidity after cardiac surgery should consider the ageing population and those with minor and/or multiple complex morbidities.
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Journal Title
European Journal of Cardiovascular Nursing
Volume
21
Issue
7
Copyright Statement
© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits
unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Subject
Cardiovascular medicine and haematology
Nursing
Cardiac surgery
Morbidity outcome
Postoperative morbidity
Preoperative risk
Risk prediction models