Abstract
BackgroundRisk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist.ResultsThe most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90.ConclusionsThe application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.
Highlights
Risk stratification plays a central role in anesthetic evaluation
machine learning (ML) systems are well suitable for this context, where the possibility to collect a large number of data and the choice of the variable that is selected by the model itself, allows the discovery of new factors and a different interpretation of already known items
We proposed that artificial intelligence (AI) should become an essential technical and non-technical skill for the future anesthesiologists
Summary
Risk stratification plays a central role in anesthetic evaluation. We conducted a systematic review to understand the role of ML in the development of predictive postsurgical outcome models and risk stratification. Several scores have been published, from the most generic, like the American Society of Anesthesiologists Physical Status (ASA-PS) [4], to the most specific ones, as the European system for cardiac operative risk evaluation (EuroSCORE) [5] or the General Surgery Acute Kidney Injury Risk Index Classification System [6]. These scores have some limits, mainly due to the lack of tailored predictions. Considered an extension of traditional statistics, AI differs from standard approaches for its ability to learn from examples and mistakes, to improve continuously with the introduction of new data, and to create a model for individualized patient care [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
