Cardiogenic shock and machine learning: A systematic review on prediction through clinical decision support softwares. Issue 11 (31st August 2021)

• Record Type: Journal Article
• Authors: Aleman, Rene; Patel, Sinal; Sleiman, Jose; Navia, Jose; Sheffield, Cedric; Brozzi, Nicolas A.
• Is Part Of: Journal of cardiac surgery. Volume 36:Issue 11(2021)
• Page Start: 4153
• Page End: 4159
• Publication Date: 2021-08-31
• Subjects: area under the curve -- cardiogenic shock -- early detection -- machine learning -- receiving operating characteristics -- systematic review; Heart -- Surgery -- Periodicals; 617.412005
• DOI: 10.1111/jocs.15934
• Languages: English
• ISSNs: 0886-0440

Abstract

Abstract: Background and Aim: Cardiogenic shock (CS) withholds a significantly high mortality rate between 40% and 60% despite advances in diagnosis and medical/surgical intervention. To date, machine learning (ML) is being implemented to integrate numerous data to optimize early diagnostic predictions and suggest clinical courses. This systematic review summarizes the area under the curve (AUC) receiver operating characteristics (ROCs) accuracy for the early prediction of CS. Methods: A systematic review was conducted within databases of PubMed, ScienceDirect, Clinical Key/MEDLINE, Embase, GoogleScholar, and Cochrane. Cohort studies that assessed the accuracy of early detection of CS using ML software were included. Data extraction was focused on AUC–ROC values directed towards the early detection of CS. Results: A total of 943 studies were included for systematic review. From the reviewed studies, 2.2% ( N = 21) evaluated patient outcomes, of which 14.3% ( N = 3) were assessed. The collective patient cohort ( N = 698) consisted of 314 (45.0%) females, with an average age and body mass index of 64.1 years and 28.1 kg/m 2, respectively. Collectively, 159 (22.8%) mortalities were reported following early CS detection. Altogether, the AUC–ROC value was 0.82 ( α = .05), deeming it of superb sensitivity and specificity. Conclusions: From the present comprehensively gathered data, this study accounts the use of ML software for the early detection of CS in a clinical setting as a valid tool to predict patients at risk of CS. The complexity of ML and its parallel lack of clinical evidence implies that further prospective randomized control trials are needed to draw definitive conclusions before standardizing the use of these technologies. Brief Summary: The catastrophic risk of developing CS continues to be a concern in the management of critical cardiac care. The use of ML predictive models have the potential to provide the accurate and necessary feedback for the early detection and proper management of CS. This systematic review summarizes the AUC–ROCs accuracy for the early prediction of CS. Highlights: Cardiogenic shock's prevalence continues to increase despite emerging diagnostic tools. Machine learning has the potential to provide pre‐emptive feedback for adequate management and outcomes improvement in cardiogenic shock patients. Machine learning predictive models are in an early development stage yet pose a promising future as clinical diagnostic tools in critical cardiac care. Once machine learning predictive models are comprehensively developed in a clinical setting, it could provide accurate and efficient clinical decision support for health care providers.