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Machine learning in perioperative medicine: a systematic review

Journal of Anesthesia, Analgesia and Critical Care volume 2, Article number: 2 (2022) Cite this article

Abstract

Background

Risk 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.

Methods

Following 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.

Results

The 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.

Conclusions

The 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.

Background

Risk stratification is a central part of the anesthetic evaluation. In fact, through the identification of high-risk patients, it is possible to conduct a specific risk/benefit analysis, to reduce the risk of unexpected complications, to achieve a targeted perioperative optimization, to carefully plan the anesthesiologic management, and to provide an accurate and precise informed consent [1,2,3].

Over time, 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]. Unfortunately, these scores have some limits, mainly due to the lack of tailored predictions.

In the last decade, the interest about artificial intelligence (AI), including machine learning (ML) methods, have seen an exponential increase [2]. 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].

Thanks to the growing informatization of health systems, large amounts of data have become available. The implementation of new technologies and the development of prediction algorithms paved the way for novel possibilities to exploit these huge data collections. Among the several branches of healthcare in which ML aroused enthusiasm, its application in perioperative medicine is showing promising results. In fact, in consideration of its specific characteristics, this analytical technique is suitable for the creation of predictive models, specifically concerning the optimization of resources and the development of warning score systems [8, 9]. The application of these algorithms allows early detection and prediction of acute critical illness, facilitating the management of high-risk patients [10].

More recently, COVID-19 pandemic lighted on the importance of AI-based models for the fast development of algorithms that could integrate readily available data, helping the hospital systems and the clinicians in optimal patient care [11].

The use of ML techniques for the creation of predictive models of perioperative complications is in continuous expansion.

The aim of our review is to clarify the role of ML in perioperative settings, evaluating currently available predictive outcome models, the types of ML algorithms used more frequently, and their proved efficacy.

Methods

Literature search

This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines (http://prisma-statement.org/documents/PRISMA_2020_checklist.pdf).

The authors performed a systematic literature search of Scopus, CINAHL, the Cochrane Library, PubMed, MeSH, MEDLINE, and Embase, from 1 January 2015 to 30 March 2021, using different combinations of the following terms: “risk prediction,” “surgery,” “machine learning,” “ICU,” “anesthesia,” and “perioperative.”

Specifically, ((((((("risk prediction"[All Fields]) AND ("surgery"[All Fields])) AND ("machine learning"[All Fields])) OR (risk prediction)) ) AND (machine learning)) AND (ICU)) OR (risk prediction)) AND (machine learning)) AND (anesthesia); ((((((((("risk prediction"[All Fields]) AND ("surgery"[All Fields])) AND ("machine learning"[All Fields])) OR (risk prediction)) ) AND (machine learning)) AND (ICU)) OR (risk prediction)) AND (machine learning)) AND (anesthesia) OR (((((((("risk"[All Fields]) AND ("surgery"[All Fields])) AND ("machine learning"[All Fields])) OR (risk)) ) AND (machine learning)) AND (ICU)) OR (risk)) AND (machine learning)) AND (anesthesia); ((postoperative) AND machine learning) AND (intensive care admission).

In the last 10 years, there was an exponential increase in literature concerning the application of AI in medicine. Therefore, we decided to perform the search in this time frame to include more homogeneous and easily comparable studies. We included studies if they evaluated ML models in surgical settings for the prediction of perioperative risk. Both prospective and retrospective studies were eligible for inclusion. The following types of study were excluded: papers published prior to 2015, papers concerning outpatient settings, animal studies, pediatric population, and studies written in languages other than English. Furthermore, primary study evaluating strictly surgical outcomes, and systematic reviews were considered uneligible.

Data extraction and quality assessment

The primary aim of our study was to assess the main perioperative outcomes in which ML methods are used, and their efficacy among different algorithms.

Two reviewers independently screened the selected articles, and a third reviewer resolved any discrepancies.

To assess the reporting quality of all included studies, we used the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklist [12]. In fact, it provides guidance for extracting relevant information and calculating summary scores to determine adherence of primary prediction model to the TRIPOD.

Two independent reviewers assessed for each selected study the compliance with the items described in the checklist. Moreover, to facilitate data extraction and scoring, the studies were analyzed according to the study design, predictor selection, outcome assessment, applied model, and its validation. The checklist includes 22 main items, of which ten are divided in sub items, all with four potential answer options: “yes,” “not,” “referenced,” “not applicable.” After adequately fulfilling each item of the checklist, the adherence to the TRIPOD is automatically calculated. We established different levels of adherence to TRIPOD, setting a scale from 0 to 100%, assuming that a research was more accurate with higher adherence to tripod checklist.

Results

One hundred forty-seven papers were identified through database searching. After the removal of the duplicates, 89 articles were screened, and 43 were found to be ineligible after reading the abstracts. Out of the 46 full text reviewed articles, 10 were excluded because of inadequate clinical setting or because concerning pediatric population. Finally, 36 articles were included for the review (Fig. 1).

Fig. 1
figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA flow chart) illustrating the process of selecting eligible publications for inclusion in the systematic review

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Outlines all characteristics of the final selected articles (Table 1) [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48], including the design, cohort, and objective of each study, as well as the ML methods used and the best performance.

Table 1 Overview of papers included in our analysis
Full size table

Our analyses pointed out that more than 95% of included studies were published after 2018, and almost entirely performed in USA and Asia (Fig. 2).

Fig. 2
figure 2

Geographical distribution of articles publications. The USA is the main country where publications came from, followed by China and Korea

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The quality of the studies selected for the review was acceptable, with 75% of studied showing an adherence rate to TRIPOD more than 60% (Fig. 3). Specifically, in the first section of the checklist (Title and Abstract), a mean of 42% of studies adhere to tripod item. Concerning the methods section, all the articles defined the study design, or the source of data, while 53% of papers described the handling of missing data. In the results section, measures applied and models used were not always appropriated in the included studies, specifically 8% of papers presented the full prediction model and explained how to use it, while 19% of studies reported performance measures for the prediction model (Rev #2, comm #3).

Fig. 3
figure 3

Frequency of adherence to TRIPOD checklist

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Nearly all manuscripts discussed about the limitations of the study and gave an overall interpretation of results.

The use of these new technologies to analyze perioperative complications has been tested in almost all types of surgery (general, cardiac, orthopedic, neurosurgical, vascular). Variables and predictors were properly listed and described in all the articles. ML methods were used mainly to predict the following outcomes: mortality (n = 12), cardiovascular complications (n = 11), acute kidney injury (AKI; n = 9), surgical complications (n = 7), intensive care unit admission (ICU; n = 6), respiratory complications (n = 6), length of stay (n = 5), venous thromboembolism (VTE; n = 4), neurological complications (n = 4), sepsis (n =3), pain (n = 2), and post-operative nausea and vomiting (PONV; n = 1) (Fig. 4).

Fig. 4
figure 4

Main outcomes (preoperative/intraoperative) considered in our analysis

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As stated before, most of studies considered preoperative variables, like demographic, medical history, clinical and laboratory values evaluation, to calculate perioperative risk. Conversely, several studies evaluated intraoperative variables, as electroencephalography (EEG) pattern [34], or intraoperative vital signs [13, 15, 22, 24, 46, 47], for a real-time prediction of overly deep sedation, post-induction and intraoperative hypotension, hypoxemia, and intraoperative bradycardia.

Supervised models were used in most of cases (Fig. 5). 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). Deep learning, decision trees, and Naïve Bayes were other models commonly applied in the included manuscripts.

Fig. 5
figure 5

Presentation of the main types of machine learning methods used in the analysis of our studies

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In the totality of reviewed papers, ML algorithms proved to be effective in outcome prediction. Half of the selected studies compared different types of ML to identify the best performing method. Gradient boosting and random forest were found to be the models with the highest accuracy, achieving an area under the curve (AUC) greater than 0.90 in most of cases. Moreover, a few studies compared automatically obtained algorithms to conventional scores, revealing the outperformance of ML models [25].

Discussion

The number of manuscripts regarding ML implementation in health care settings is steadily increasing over the last few years, as clearly suggested by a recently published review on AI utility to provide decision support to clinicians in ICU setting [49, 50].

In fact, the availability of electronic health records, and the diffusion of Big Data systems have enabled new possibilities in data collection and storage. The interpretation of this amount of data with traditional methods could not only be extremely complicated, but even reductive. In this regard, the advent of AI-based technologies has opened up new perspectives, providing a different form of research [51].

Anesthesia and assessment of perioperative risk appear to be excellent fields to develop and apply ML systems, as reported in literature [52, 53], and confirmed by our research. The identification of modifiable risk factors and the subsequent optimization of the preoperative phase appear to be a crucial factor to decrease the incidence of post-operative complications [54]. Furthermore, risk stratification allows the acquisition of an adequate informed consent and an accurate anesthesiologic planning, tailored to each patient. 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. Thus, the availability of interpretations and predictions in real time could allow to enter a new era of anesthesia.

From a practical point of view, the method starts with multi-source data extrapolated and collected; subsequently, they are placed in ML systems able to return interpretative and predictive models, providing suitable tools for daily technologies with validated scores. Among conventional scores, the one used more frequently for comparison is the ASA-PS Classification System that has been in use for over 60 years. Comparing existing scores with new models is an essential step to understand whether this investment of time and resources could finally improve the perioperative risk stratification.

Moreover, in addition to the risk of post-operative complications, ML would also be able to answer more complex questions and create models capable of providing early predictions of adverse events, thus enabling a perioperative optimization.

The results that emerge from this systematic analysis are promising. In studies that compared ML models with traditional scores, most confirmed their outperformance. In particular, the use of AI-based technologies provided excellent results regarding events of great interest in the field of Anesthesia, as post-induction hypotension and post-intubation hypoxia [13], or the risk of AKI or delirium after surgery [19, 27, 55].

Finally, it is interesting to underline that not only clinical outcomes are relevant, but also administrative ones, as length of hospital stay, or need for recovery in intensive care settings, that may have a great relapse into hospital logistics and in economic strategies (Fig. 6). A systematic use of AI might allow the achievement of innovative results in other fields as well, such as scientific research and health organization, especially when associated with other data management technologies such as Big Data and Blockchain.

Fig. 6
figure 6

Importance of acquisition of data quality for application of AI in different fields such as research, clinical practice, and health system organization

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Among several ML algorithms currently applied, Gradient boosting and random forest were found to be the models with the best performance and the highest accuracy, achieving an area under the curve (AUC) greater than 0.90 (Ref #2, comm #3). Still, it is not possible to make a uniform evaluation and draw conclusions about the best algorithm for predictive models of perioperative complications, because of the heterogeneity of settings and the difference in the algorithms evaluated. The lack of uniformity of the included studies prevented us from performing a meta-analysis using univariate and multivariate random effect models (Ref #2, comm #3). Moreover, the models in most of the studies lack an external validation.

Further, even if we practically use AUC as an evaluation criterion, we acknowledge its limits in the setting of AI, especially in case of unbalanced dataset. Note that other criteria can also be used to evaluate ML models, such as model relevance, efficiency, and interpretability [56]. However, to achieve high-quality and high-quantity data sets, it is of paramount importance the screening of each step of the process, from data collection to ML model selection and its algorithm

(Rev #2, comm #3, comm #4).

Despite their growing diffusion, the use of these technologies in perioperative medicine is raising limitations and challenges. Along with technological progress, data quality will inevitably become increasingly important. A viable choice could be blockchain technology, to ensure adequate quality and enable secure data sharing. Its implementation could allow the safe management of large files and consequently the approval of algorithms that are progressively developed [57].

Furthermore, as recently reported for ICU-setting [50], despite the potential role of AI to improve clinical outcomes, the vast majority of developed models remain within the testing and prototyping environment. A uniform and structured approach could enable the implementation and safe delivery of AI technologies in ICU and overall, in health care settings.

Finally, the creation of predictive scores should guarantee precise rules. Unfortunately, these technologies are so innovative that the evaluation of their performance is not always so linear. Therefore, a new version of the TRIPOD statement specific for AI/ML systems (TRIPOD-ML) is currently under development. It will focus on the introduction of ML prediction algorithms to establish methodological and reporting standards for ML studies in health care [58].

Technologies are becoming more and more present in health-care settings. Both clinical and organizational decision-making processes can take advantage of these technologies. Nevertheless, high-quality studies are needed to demonstrate the real impact of ML in this context.

Our research group is starting a study that aims to validate a safe discharge score from the PACU (post-anesthesia care unit) using AI techniques; the score will no longer be generic, but based on the local clinical reality and on the specific population. Similarly, we are working on the application of AI algorithms in OR (operating room) management settings, developing a prospective trial “Bloc-op” (NCT 05106621), in collaboration with the engineering department, to optimize OR organization and resources allocation. We believe that multidisciplinary collaboration is essential to integrate AI technologies into routine clinical practice, thus leading to a great improvement in the quality of care.

We proposed that AI should become an essential technical and non-technical skill for the future anesthesiologists. In order to achieve this goal, a primary focus should be the education and training of physicians and researchers, who need to be adequately prepared on the uses and limitations of AI based technologies (Rev #2, comm #4).

Conclusions

This systematic review shows the potential role of ML in perioperative medicine, and particularly in the creation of models for the prediction of perioperative risk. Our results are encouraging.

Undoubtedly, the exploitation of a large amount of data is possible solely thanks to the application of AI. ML algorithms offer increasingly precise solutions in terms of optimization of the perioperative risk. A personalized risk/benefit analysis can result in an accurate prediction in terms of length of hospital stay and ICU recovery, thus positively influencing patient management and health costs.

Further research is needed to develop a framework standardizing AI evaluation measures, and this will be possible with interdisciplinary approaches, allowing to constantly improve high-quality care.

Availability of data and materials

Not applicable

Abbreviations

ML:

Machine learning

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-analyses

ICU:

Intensive care unit

TRIPOD:

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis

AKI:

Acute kidney injury

LR:

Logistic regression

ANNs :

Artificial neural networks

SVM:

Support vector machines

ASA-PS:

American Society of Anaesthesiologists Physical Status

EuroSCORE:

European system for cardiac operative risk evaluation

AI:

Artificial intelligence

EEG:

Electroencephalography

AUC:

Area under the curve

TRIPOD-ML:

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-Machine Learning

PACU:

Post-anesthesia care unit

OR:

Operating room

References

  1. Bose S, Talmor D. (2018) Who is a high-risk surgical patient? Curr Opin Crit Care;24(6):547-553. doi: https://doi.org/10.1097/MCC.0000000000000556. PMID: 30308542.

  2. Rajkomar A, Dean J, Kohane I. (2019) Machine Learning in Medicine. N Engl J Med. 380(14):1347-1358. doi: https://doi.org/10.1056/NEJMra1814259. PMID: 30943338.

  3. Wijeysundera DN (2015) Predicting outcomes: Is there utility in risk scores? Can J Anaesth 2016 63(2):148–158. https://doi.org/10.1007/s12630-015-0537-2 Epub PMID: 26670801

    Article  Google Scholar 

  4. Mayhew D, Mendonca V, Murthy BVS (2019) A review of ASA physical status - historical perspectives and modern developments. Anaesthesia 74(3):373–379. https://doi.org/10.1111/anae.14569 Epub 2019 Jan 15. PMID: 30648259

    Article  CAS  PubMed  Google Scholar 

  5. Nashef SA, Roques F, Michel P, Gauducheau E, Lemeshow S, Salamon R. (1999). European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothorac Surg. 16(1):9-13. doi: https://doi.org/10.1016/s1010-7940(99)00134-7. PMID: 10456395.

  6. Kheterpal S, Tremper KK, Heung M, Rosenberg AL, Englesbe M, Shanks AM, Campbell DA Jr. (2009). Development and validation of an acute kidney injury risk index for patients undergoing general surgery: results from a national data set. Anesthesiology. 110(3):505-515. doi: https://doi.org/10.1097/ALN.0b013e3181979440. PMID: 19212261.

  7. Beam AL, Kohane IS. (2017). Big Data and Machine Learning in Health Care. JAMA ;319(13):1317-1318. doi: https://doi.org/10.1001/jama.2017.18391. PMID: 29532063.

  8. Lauritsen SM, Kristensen M, Olsen MV, Larsen MS, Lauritsen KM, Jørgensen MJ, Lange J, Thiesson B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nat Commun; 11(1):3852. doi: https://doi.org/10.1038/s41467-020-17431-x. PMID: 32737308; PMCID: PMC7395744.

  9. Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. (2021). Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open;4(3):e212240. doi: https://doi.org/10.1001/jamanetworkopen.2021.2240. PMID: 33783520; PMCID: PMC8010590.

  10. Hyland SL, Faltys M, Hüser M, Lyu X, Gumbsch T, Esteban C, Bock C, Horn M, Moor M, Rieck B, Zimmermann M, Bodenham D, Borgwardt K, Rätsch G, Merz TM (2020) Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med 26(3):364–373. https://doi.org/10.1038/s41591-020-0789-4 Epub 2020 Mar 9. PMID: 32152583

    Article  CAS  PubMed  Google Scholar 

  11. Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics 52(4):200–202. https://doi.org/10.1152/physiolgenomics.00029.2020 Epub. PMID: 32216577; PMCID: PMC7191426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med;162(1):W1-73. doi: https://doi.org/10.7326/M14-0698. PMID: 25560730.

  13. Kendale S, Kulkarni P, Rosenberg AD, Wang J. (2018). Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology;129(4):675-688. doi: https://doi.org/10.1097/ALN.0000000000002374. PMID: 30074930.

  14. Fernandes MPB, Armengol de la Hoz M, Rangasamy V, Subramaniam B (2021) Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery. J Cardiothorac Vasc Anesth 35(3):857–865. https://doi.org/10.1053/j.jvca.2020.07.029 Epub 2020 Jul 12. PMID: 32747203

    Article  PubMed  Google Scholar 

  15. Cherifa M, Blet A, Chambaz A, Gayat E, Resche-Rigon M, Pirracchio R. (2020). Prediction of an acute hypotensive episode during an ICU hospitalization with a super learner machine-learning algorithm. Anesth Analg; 130(5):1157-1166. doi: https://doi.org/10.1213/ANE.0000000000004539. PMID: 32287123.

  16. Flechet M, Falini S, Bonetti C, Güiza F, Schetz M, Van den Berghe G, Meyfroidt G (2019) Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Crit Care 23(1):282. https://doi.org/10.1186/s13054-019-2563-x PMID: 31420056; PMCID: PMC6697946

    Article  PubMed  PubMed Central  Google Scholar 

  17. Nudel J, Bishara AM, de Geus SWL, Patil P, Srinivasan J, Hess DT, Woodson J (2021) Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc 35(1):182–191. https://doi.org/10.1007/s00464-020-07378-x Epub 2020 Jan 17. PMID: 31953733

    Article  PubMed  Google Scholar 

  18. Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, Jones A, Ashworth S, Beale R, Brett SJ, Singer M, Ercole A (2018) Optimal intensive care outcome prediction over time using machine learning. PLoS One 13(11):e0206862. https://doi.org/10.1371/journal.pone.0206862 PMID: 30427913; PMCID: PMC6241126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lee HC, Yoon HK, Nam K, Cho YJ, Kim TK, Kim WH, Bahk JH (2018) Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med 7(10):322. https://doi.org/10.3390/jcm7100322 PMID: 30282956; PMCID: PMC6210196

    Article  PubMed Central  Google Scholar 

  20. Bai P, Zhou Y, Liu Y, Li G, Li Z, Wang T, Guo X (2020, 2020) Risk Factors of Cerebral Infarction and Myocardial Infarction after Carotid Endarterectomy Analyzed by Machine Learning. Comput Math Methods Med:6217392. https://doi.org/10.1155/2020/6217392 PMID: 33273961; PMCID: PMC7683166

  21. Solomon SC, Saxena RC, Neradilek MB, Hau V, Fong CT, Lang JD, Posner KL, Nair BG. (2020) Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension. Anesth Analg;130(5):1201-1210. doi: https://doi.org/10.1213/ANE.0000000000004636. PMID: 32287127.

  22. Ko S, Jo C, Chang CB, Lee YS, Moon YW, Youm JW, Han HS, Lee MC, Lee H, Ro DH (2020) A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1007/s00167-020-06258-0 Epub ahead of print. PMID: 32880677

  23. Lu Y, Forlenza E, Cohn MR, Lavoie-Gagne O, Wilbur RR, Song BM, Krych AJ, Forsythe B. (2020). Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc 2021;29(9):2958-2966. doi: https://doi.org/10.1007/s00167-020-06321-w. Epub. PMID: 33047150.

  24. Maheshwari K, Buddi S, Jian Z, Settels J, Shimada T, Cohen B, Sessler DI, Hatib F (2021) Performance of the Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients. J Clin Monit Comput 35(1):71–78. https://doi.org/10.1007/s10877-020-00463-5 Epub 2020 Jan 27. PMID: 31989416; PMCID: PMC7889685

    Article  PubMed  Google Scholar 

  25. Hill BL, Brown R, Gabel E, Rakocz N, Lee C, Cannesson M, Baldi P, Olde Loohuis L, Johnson R, Jew B, Maoz U, Mahajan A, Sankararaman S, Hofer I, Halperin E (2019) An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. Br J Anaesth 123(6):877–886. https://doi.org/10.1016/j.bja.2019.07.030 Epub. PMID: 31627890; PMCID: PMC6883494

    Article  PubMed  PubMed Central  Google Scholar 

  26. Suhre W, O'Reilly-Shah V, Van Cleve W (2020) Cannabis use is associated with a small increase in the risk of postoperative nausea and vomiting: a retrospective machine-learning causal analysis. BMC Anesthesiol 20(1):115. https://doi.org/10.1186/s12871-020-01036-4 PMID: 32423445; PMCID: PMC7236204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lee HC, Yoon SB, Yang SM, Kim WH, Ryu HG, Jung CW, Suh KS, Lee KH (2018) Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression model. J Clin Med 7(11):428. https://doi.org/10.3390/jcm7110428 PMID: 30413107; PMCID: PMC6262324

    Article  CAS  PubMed Central  Google Scholar 

  28. Barry GS, Bailey JG, Sardinha J, Brousseau P, Uppal V (2021) Factors associated with rebound pain after peripheral nerve block for ambulatory surgery. Br J Anaesth 126(4):862–871. https://doi.org/10.1016/j.bja.2020.10.035 Epub 2020 Dec 31. PMID: 33390261

    Article  PubMed  Google Scholar 

  29. Gabriel RA, Sharma BS, Doan CN, Jiang X, Schmidt UH, Vaida F. (2019). A predictive model for determining patients not requiring prolonged hospital length of stay after elective primary total hip arthroplasty. Anesth Analg; 129(1):43-50. doi: https://doi.org/10.1213/ANE.0000000000003798. PMID: 30234533.

  30. Li H, Jiao J, Zhang S, Tang H, Qu X, Yue B (2020) Construction and comparison of predictive models for length of stay after total knee arthroplasty: regression model and machine learning analysis based on 1,826 cases in a single Singapore center. J Knee Surg. https://doi.org/10.1055/s-0040-1710573 Epub ahead of print. PMID: 32512596

  31. Jungquist CR, Chandola V, Spulecki C, Nguyen KV, Crescenzi P, Tekeste D, Sayapaneni PR (2019) Identifying patients experiencing opioid-induced respiratory depression during recovery from anesthesia: the application of electronic monitoring devices. Worldviews Evid Based Nurs 16(3):186–194. https://doi.org/10.1111/wvn.12362 Epub 2019 May 2. PMID: 31050151

    Article  PubMed  Google Scholar 

  32. Nguyen M, Pirracchio R, Kornblith LZ, Callcut R, Fox EE, Wade CE, Schreiber M, Holcomb JB, Coyle J, Cohen M, Hubbard A (2020) Dynamic impact of transfusion ratios on outcomes in severely injured patients: targeted machine learning analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios randomized clinical trial. J Trauma Acute Care Surg 89(3):505–513. https://doi.org/10.1097/TA.0000000000002819 PMID: 32520897; PMCID: PMC7830749

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tourani R, Murphree DH, Melton-Meaux G, Wick E, Kor DJ, Simon GJ (2019) The value of aggregated high-resolution intraoperative data for predicting post-surgical infectious complications at two independent sites. Stud Health Technol Inform 264:398–402. https://doi.org/10.3233/SHTI190251 PMID: 31437953; PMCID: PMC7037580

    Article  PubMed  PubMed Central  Google Scholar 

  34. Cartailler J, Parutto P, Touchard C, Vallée F, Holcman D (2019) Alpha rhythm collapse predicts iso-electric suppressions during anesthesia. Commun Biol 2:327. https://doi.org/10.1038/s42003-019-0575-3 PMID: 31508502; PMCID: PMC6718680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wong WEJ, Chan SP, Yong JK, Tham YYS, Lim JRG, Sim MA, Soh CR, Ti LK, Chew THS (2021) Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study. BMC Nephrol 22(1):63. https://doi.org/10.1186/s12882-021-02238-9 PMID: 33618695; PMCID: PMC7898752

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lee CK, Samad M, Hofer I, Cannesson M, Baldi P (2021) Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. NPJ Digit Med 4(1):8. https://doi.org/10.1038/s41746-020-00377-1 PMID: 33420341; PMCID: PMC7794438

    Article  PubMed  PubMed Central  Google Scholar 

  37. Jeong YS, Kim J, Kim D, Woo J, Kim MG, Choi HW, Kang AR, Park SY (2021) Prediction of postoperative complications for patients of end stage renal disease. Sensors (Basel) 21(2):544. https://doi.org/10.3390/s21020544 PMID: 33466610; PMCID: PMC7828737

    Article  Google Scholar 

  38. Filiberto AC, Ozrazgat-Baslanti T, Loftus TJ, Peng YC, Datta S, Efron P, Upchurch GR Jr, Bihorac A, Cooper MA (2021) Optimizing predictive strategies for acute kidney injury after major vascular surgery. Surgery 170(1):298–303. https://doi.org/10.1016/j.surg.2021.01.030 Epub 2021 Feb 27. PMID: 33648766; PMCID: PMC8276529

    Article  PubMed  Google Scholar 

  39. Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, Stamm C, Hofmann T, Falk V, Eickhoff C (2018) Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med 6(12):905–914. https://doi.org/10.1016/S2213-2600(18)30300-X Epub 2018 Sep 28. PMID: 30274956

    Article  PubMed  Google Scholar 

  40. Chiew CJ, Liu N, Wong TH, Sim YE, Abdullah HR (2020) Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission. Ann Surg 272(6):1133–1139. https://doi.org/10.1097/SLA.0000000000003297 PMID: 30973386; PMCID: PMC7668340

    Article  PubMed  Google Scholar 

  41. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, Lipori G, Hogan WR, Efron PA, Moore F, Moldawer LL, Wang DZ, Hobson CE, Rashidi P, Li X, Momcilovic P (2019) MySurgeryRisk: Development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg 269(4):652–662. https://doi.org/10.1097/SLA.0000000000002706 PMID: 29489489; PMCID: PMC6110979

    Article  PubMed  Google Scholar 

  42. Yao RQ, Jin X, Wang GW, Yu Y, Wu GS, Zhu YB, Li L, Li YX, Zhao PY, Zhu SY, Xia ZF, Ren C, Yao YM (2020) A machine learning-based prediction of hospital mortality in patients with postoperative sepsis, Front Med (Lausanne). 7:445. https://doi.org/10.3389/fmed.2020.00445 PMID: 32903618; PMCID: PMC7438711

  43. Datta S, Loftus TJ, Ruppert MM, Giordano C, Upchurch GR Jr, Rashidi P, Ozrazgat-Baslanti T, Bihorac A (2020) Added value of intraoperative data for predicting postoperative complications: the MySurgeryRisk PostOp Extension. J Surg Res 254:350–363. https://doi.org/10.1016/j.jss.2020.05.007 Epub 2020 Jun 9. PMID: 32531520; PMCID: PMC7755426

    Article  PubMed  PubMed Central  Google Scholar 

  44. Brennan M, Puri S, Ozrazgat-Baslanti T, Feng Z, Ruppert M, Hashemighouchani H, Momcilovic P, Li X, Wang DZ, Bihorac A (2019) Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: a pilot usability study. Surgery 165(5):1035–1045. https://doi.org/10.1016/j.surg.2019.01.002 Epub 2019 Feb 18. PMID: 30792011; PMCID: PMC6502657

    Article  PubMed  Google Scholar 

  45. Houthooft R, Ruyssinck J, van der Herten J, Stijven S, Couckuyt I, Gadeyne B, Ongenae F, Colpaert K, Decruyenaere J, Dhaene T, De Turck F (2015) Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med 63(3):191–207. https://doi.org/10.1016/j.artmed.2014.12.009 Epub 2014 Dec 30. PMID: 25579436

    Article  PubMed  Google Scholar 

  46. Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DK, Newman SF, Kim J, Lee SI (2018) Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2(10):749–760. https://doi.org/10.1038/s41551-018-0304-0 Epub 2018 Oct 10. PMID: 31001455; PMCID: PMC6467492

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kang AR, Lee J, Jung W, Lee M, Park SY, Woo J, Kim SH (2020) Development of a prediction model for hypotension after induction of anesthesia using machine learning. PLoS One 15(4):e0231172. https://doi.org/10.1371/journal.pone.0231172 PMID: 32298292; PMCID: PMC7162491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tan HS, Liu N, Sultana R, Han NR, Tan CW, Zhang J, Sia ATH, Sng BL (2021) Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches. Int J Obstet Anesth 45:99–110. https://doi.org/10.1016/j.ijoa.2020.08.010 Epub 2020 Aug 25. PMID: 33121883

    Article  CAS  PubMed  Google Scholar 

  49. Shillan D, Sterne JAC, Champneys A, Gibbison B (2019) Use of machine learning to analyse routinely collected intensive care unit data: a systematic review. Crit Care 23(1):284. https://doi.org/10.1186/s13054-019-2564-9 PMID: 31439010; PMCID: PMC6704673

    Article  PubMed  PubMed Central  Google Scholar 

  50. Van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J (2021) Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med 47(7):750–760. https://doi.org/10.1007/s00134-021-06446-7 Epub 2021 Jun 5. PMID: 34089064; PMCID: PMC8178026

    Article  PubMed  PubMed Central  Google Scholar 

  51. Connor CW (2019) Artificial intelligence and machine learning in anesthesiology. Anesthesiology 131(6):1346–1359. https://doi.org/10.1097/ALN.0000000000002694 PMID: 30973516; PMCID: PMC6778496

    Article  PubMed  Google Scholar 

  52. Gambus P, Shafer SL. (2018).Artificial Intelligence for Everyone. Anesthesiology;128(3):431-433. doi: https://doi.org/10.1097/ALN.0000000000001984. PMID: 29166324.

  53. Liu Q, Ma L, Fan SZ, Abbod MF, Lu CW, Lin TY, Jen KK, Wu SJ, Shieh JS. (2018). Design and evaluation of a real time physiological signals acquisition system implemented in multi-operating rooms for anesthesia. J Med Syst;42(8):148. doi: https://doi.org/10.1007/s10916-018-0999-1. PMID: 29961144.

  54. Chakravarthy M (2017) Modifying risks to improve outcome in cardiac surgery: an anesthesiologist's perspective. Ann Card Anaesth 20(2):226–233. https://doi.org/10.4103/aca.ACA_20_17 PMID: 28393785; PMCID: PMC5408530

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wang Z, Majewicz FA (2018) Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J Comput Assist Radiol Surg 13(12):1959–1970. https://doi.org/10.1007/s11548-018-1860-1 Epub 2018 Sep 25. PMID: 30255463

    Article  PubMed  Google Scholar 

  56. Rahmani, A.M.; Yousefpoor, E.; Yousefpoor, M.S.; Mehmood, Z.; Haider, A.; Hosseinzadeh, M.; Ali Naqvi, R. (2021). Machine learning (ML) in medicine: review, applications, and challenges. Mathematics; 9, 2970. https://doi.org/10.3390/math9222970

  57. Bellini V, Petroni A, Palumbo G, Bignami E (2019) Data quality and blockchain technology. Anaesth Crit Care Pain Med 38(5):521–522. https://doi.org/10.1016/j.accpm.2018.12.015 Epub 2019 Jan 8. PMID: 30633992

    Article  PubMed  Google Scholar 

  58. Collins GS, Moons KGM. (2019). Reporting of artificial intelligence prediction models. Lancet;393(10181):1577-1579. doi: https://doi.org/10.1016/S0140-6736(19)30037-6. PMID: 31007185.

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Acknowledgements

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Funding

This review was completed as a part of the research fellowship of University of Parma MADA-MED (MAchine learning and big DAta in medicina perioperatoria), co-funded with resources of FSE (Fondo Sociale Europeo, delibera di G.R. 589/2019–Rif. PA 2019-11449/RER). This research has financially been supported by the Programme “FIL-Quota Incentivante” of University of Parma and co-sponsored by Fondazione Cariparma.

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Authors and Affiliations

  1. Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy

    Valentina Bellini, Giorgia Bertorelli, Barbara Pifferi, Michelangelo Craca & Elena Bignami

  2. General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy

    Marina Valente & Paolo Del Rio

  3. Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124, Parma, Italy

    Monica Mordonini, Gianfranco Lombardo & Eleonora Bottani

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Contributions

VB, MV, PDR, and EB selected and identified the eligible studies. MM, GL, and EB analyzed and interpreted the data. VB and EB identified the aim of the review and revised the work. GB, BP, and MC elaborated, drafted, and revised the work. All authors read and approved the final manuscript. All authors have agreed to be responsible for the content of the work, and to ensure that questions related to the accuracy or integrity of any part of work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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Correspondence to Elena Bignami.

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Bellini, V., Valente, M., Bertorelli, G. et al. Machine learning in perioperative medicine: a systematic review. J Anesth Analg Crit Care 2, 2 (2022). https://doi.org/10.1186/s44158-022-00033-y

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Keywords

  • Risk prediction
  • Surgery
  • Machine learning
  • ICU
  • Anesthesia
  • Perioperative

Journal of Anesthesia, Analgesia and Critical Care

ISSN: 2731-3786

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