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Table 1 Overview of papers included in our analysis

From: Machine learning in perioperative medicine: a systematic review

Author, years

Study design

Objective

Final

cohort

Outcomes

Type of ML

Prediction performance

Comparator/control

Lundberg SM, 2019

Retrospective/observational

single center

Development and testing of a ML-based system that predicts the risk of hypoxemia during general anesthesia

48,069

Hypoxemia

GB

AUC 0.92

ML-based system was compared to anesthesiologists’ predictions

Kendale S, 2018 [13]

Retrospective/observational

single center

Prediction of the risk of post-induction hypotension using ML methods

13,323

Cardiovascular complications

RF, SVM, GB, BN, LR-EN, regularization, K nearest; linear discrimination analysis; neural nets

AUC GB 0.74 (95% CI, 0.72 to 0.77). RF 0.74 (95% CI, 0.73 to 0.75)

Different ML algorithms were trained to obtain the model with the best performance

Fernandes MPB, 2021 [14]

Retrospective/observational

single center

ML models used to predict postoperative mortality rarely include intraoperative factors.

5015

Mortality

logistic regression, RF neural networks, SVM and extreme gradient boosting (XGB).

XGB predicted mortality confidence interval (CI): 0.88 (0.83–0.94)

Different ML algorithms were trained to obtain the model with the best performance

Cherifa M, 2020 [15]

Retrospective/observational

single center

Prediction of acute hypotensive episode

1151

Cardiovascular complications

Super Learner (SL) algorithm

SL AUROC 0.890

Different ML algorithms were trained to obtain the model with the best performance

Flechet M, 2019 [16]

Prospective/observational

single center

Compare diagnostic performances of ML models and physicians in predicting AKI-23 in the 7 days following ICU admission

252

Acute kidney injury

ML based AKI predictor

AUROC 0.80

Physicians’ predictions were compared against the AKI predictor model

Kang AR, 2020 [17]

Retrospective/observational

single center

Prediction of hypotension during anesthesia induction

222

Cardiovascular complications

Naïve Bayes, logistic regression, RF, ANN

RF best performance AUC 0.842

Different ML algorithms were trained to obtain the model with the best performance

Meiring C, 2018 [18]

Retrospective/observational

multicentric

Identification of risk factors for admission in ER/ICU for spine patients

11150

ER/ ICU admission

RF, SVM, GB, DECISION TREE, DEEP LEARNING, NNC, Single layer averaged neural network

RF AUC 0.859, NNC AUC0.864; SVM AUC 0.867; adaboost AUC 0.868; deep learning AUC 0.883

Logistic regression against physiological data alone outperformed APACHE-II (current risk stratification tools)

Nudel J, 2021 [17]

Retrospective/observational

multicentric

Comparison of two ML strategies with conventional statistical models in prediction of surgical complication

43,6807

Surgical complications, VTE

GB, ANN

ANN, and XGB, LR achieved similar AUCs 0.65, 0.67 and 0.64

Different ML algorithms were trained to obtain the model with the best performance

Lee Hc, 2018 [19]

Retrospective/observational

single center

Comparison of ML method with logistic regression analysis to predict AKI after cardiac surgery

2010

AKI, mortality

RF, SVM, GB, DECISION TREE, DEEP LEARNING, NNC

Best GB AUC 0.78

The performance of ML approaches was compared with that of LR analysis

Bai P, 2020 [20]

Retrospective/observational

multicentric

Identification of risk factors of early cerebral infarction and myocardial infarction after CEA with ML method

443

Cardiovascular complications

linear SVM, decision tree,RF,ANN, quadratic discriminant analysis, and XGBoost

XGBoost had the highest accuracy

Not applicable

Tan HS, 2021 [2021]

Retrospective study

single center

Use of ML to identify predictive factors for inadequate labor anesthesia

20,716

Pain prevention

RF, XGBoost and logistic regression models

All three models performed similarly, with AUC 0.763–0.772

The performance of ML was compared with regression techniques

Solomon SC, 2020 [21]

Retrospective and prognostic

single center

Prediction of intraoperative bradicardia

62,182

Cardiovascular complications

Gradient Boosting Machine (GBM)

AUC of 0.81–0,89

The performance of ML was compared with regression techniques

Ko S, 2020 [22]

Retrospective and multicentric

Prediction of postoperative AKI after total knee arthroplasty.

5757

AKI

Gradient Boosting Machine (GBM)

AUC of 0,78

Not applicable

Lu Y, 2020 [23]

Retrospective

single center

Develop ML algorithm for identification of patients requiring admission following elective anterior cruciate ligament (ACL) reconstruction.

4709

Length of stay

RF, XGBoost, LDA, AdaBoost

The ensemble model achieved the best AUC 0.76

Not applicable

Maheshwari K, 2020 [24]

Observational

single center

Using ML to predict intraoperative hypotension

305

Cardiovascular complications

Hypotension Prediction Index

95% confidence interval

Not applicable

Hill BL, 2019 [25]

Retrospective/observational

single center

Develop a model that estimates in-hospital mortality at the end of surgery to quantify the

change in risk during the perioperative period.

53,097

Mortality

Logistic regression, Elastic Net24 logistic regression, RF, GB.

Best RF 0.932

Comparison of ML methods with the perioperative score (as ASA physical status score)

Suhre W,2020 [26]

Retrospective multicentric

Correlation between chronic cannabis use and the risk of postoperative

nausea and vomiting (PONV).

16,245

PONV

Bayesian additive regression

trees (BART)

90% CI 0.98–1.33

Not applicable

Lee HC, 2018 [27]

Retrospective/observational

single center

Comparison of ML method with logistic regression analysis to predict AKI after liver transplantation

1211

AKI, mortality

RF, SVM, GB, Decision tree, Neural network Classifier, BN, LR-EN, multilayer perceptron

Best GB AUC 0.90

The performance of ML approaches was compared with that of LR analysis

Barry GS, 2021 [28]

Retrospective cohort study

Investigate the incidence and factors associated with rebound pain in patients who received a PNB for ambulatory surgery.

482

Pain control

Logistic model tree attribute-selected classifier

ROC curve of 0.609

Not applicable

Gabriel RA, 2019 [29]

Retrospective/observational

single center

Develop a predictive model for determining LOS.

1018

LOS

Ridge regression, Lasso, RF

ridge regression 0.761, Lasso 0.752, RF 0.731

Predictive models using ML techniques were compared to model performances

Li H, 2020 [30]

Retrospective/observational

single center

Development of a predictive model for LOS after total knee arthroplasty

1826

LOS

GB

AUC 0.738.

Logistic regression and ML model were compared

Jungquist CR, 2019 [31]

Retrospective/observational

single center

Early detection of respiratory depression using ML models

60

Postoperative respiratory complications

SVM

Accuracy of 80%

Not applicable

Nguyen M, 2020 [32]

Multicentric randomized

Using ML techniques and causal inference methods to detect the dynamic relationship between transfusion ratios and outcomes in trauma patients

680

Mortality and hemorrhagic complications

Statistical programming

language R

Mortality at AUC 0.89,

hemorrhagic complications 1.07

ML techniques were used to augment the intent-to-treat analysis of PROPPR

Tourani R,2019 [33]

Retrospective multicentric

In the context of perioperative decision support, understand if the use of intraoperative data improve the performance of 30-day postoperative risk models

38,045 + 9,044

Sepsis, septic shock, UTI, PNA, surgical infections

Logistic regression models.

AUC between 0.66 and 0.82

Not applicable

Cartailler J,2019 [34]

Clinical trial single center

Use of EEG-patterns to anticipate excessive deep sedation

80

Neurological complications

RF

AUC of 0.93

Not applicable

Wong WEJ, 2021 [35]

Retrospective/observational

single center

Prediction of AKi in ICU

940

ICU AKI, hospital and 1 year mortality

Chi-square test, Fisher’s exact test,Mann-Whitney test, independent t test and the Kaplan-Meier curve.

AUROCs of the auxiliary models for ICU AKI were 0.7537, 0.7589, 0.7950, 0.7333 and 0.7654.

Not applicable

Lee CK, 2021 [36]

Retrospective/observational

single center

Prediction of mortality in post-operative patients

59,985

Post-operative mortality

Generalized additive models with neural networks (GAM-NNs).

AUC 0.921

Model performance was compared to a standard LR model

Jeong YS, 2021 [37]

Retrospective/observational

single center

To make a proper model for predicting postoperative major cardiac event (MACE) in ESRD patients undergoing general anesthesia.

3220

Cardiovascular complications, mortality

SVM, decision tree, RF, Gaussian naive Bayes (GNB), ANN, LR, XGBoost

RF AUC 0.797

Different ML algorithms were trained to obtain the model with the best performance

Filiberto AC, 2021 [38]

Retrospective/observational

single center

Postoperative acute kidney injury using ML models

1531

AKI

RF

AUC 0.70

ML models using the perioperative data were compared to models using either preoperative data alone or the ASA physical status classification

Meyer A, 2018 [39]

Retrospective/observational

single center

Use machine

learning methods to predict severe complications during and after cardiothoracic surgery.

11,492

Postoperative bleeding, AKI, mortality

Deep learning model

AUC 0·09 for bleeding, of 0·18 for mortality, and of 0·25 for AKI

Deep learning methods were compare to established standard-of-care clinical reference tools

Chiew CJ, 2020 [40]

Retrospective/observational

single center

Compare the performance of ML models against

the traditionally (CARES) model and (ASA-PS) in the prediction of 30-day postsurgical mortality and ICU admission

90,785

Mortality, postoperative ICU admission

RF, GB, adaptive boosting, SVM

Best GB AUC 0.23 and for mortality and 0.38 ICU admission

The performance of ML models was compare against the traditionally Combined Assessment of Risk and Encountered in Surgery (CARES) model and the ASA physical status.

Bihorac A, 2019 [41]

Retrospective/observational

single center

To calculate the risk for postoperative complications and death after

surgery using ML

51,457

AKI, sepsis, VTE, ICU admission > 48 h,

mechanical ventilation > 48 h, wound, neurologic and cardiovascular complications

MySurgeryRisk algorithm

AUC values ranging between 0.82 and 0.94

Not applicable

Yao RQ, 2020 [42]

Retrospective/observational

single center

Develop a mathematical model for predicting the in-hospital mortality among patients

with postoperative sepsis.

3713

Postoperative sepsi, mortality

Extreme gradient boosting (XGBoost) and stepwise logistic regression

Best XGBoost AUC 0.835

ML model was compare to the stepwise LR model.

Datta S, 2020 [43]

Retrospective/observational

single center

Describe a model that predicts postoperative complications considering intraoperative events.

43,943

ICU LOS,prolonged mechanical ventilation, neurologic complications

cardiovascular complications, AKI, VTE, wound

complications, mortality

RF

AUC 0.21

ML models using preoperative and intraoperative data were compare to models using preoperative data alone

Brennan M, 2019 [44]

Prospective, non-randomized pilot study

Assess the usability and accuracy of the MySurgeryRisk algorithm for preoperative risk assessment

20

AKI, sepsis, VTE, ICU admission > 48 h,

mechanical ventilation > 48 h, wound, neurologic and cardiovascular complications

MySurgeryRisk algorithm

MySurgeryRisk algorithm ranged between 0.73 and 0.85

Compare the accuracy of perioperative risk-assessment between physicians and MySurgeryRisk.

Houthooft R,2015 [45]

Retrospective/observational

single center

develop model to determine patient survival and ICU length of stay (LOS)

based on monitored ICU patient data.

14,480

LOS

ANN, k-nearest neighbors (k-NN), SVMs, classification trees (CART), RF,

adaptive boosting (AdaBoost)

SVM AUC 0.77

Different ML algorithms were trained to obtain the model with the best performance

  1. AdaBoost = adaptive boosting algorithms, AKI = acute kidney injury; ANN = artificial neural network models, BART = Bayesian additive regression trees, BN = Bayesian network, GB = gradient boosting, ICU = intensive care unit, LDA = linear discriminant classifier, LOS = length of stay, LR-EN = logistic regression with elastic net, ML = machine learning, NNC = neural network classifier, PNA = pneumonia, PONV = postoperative nausea and vomiting, RF = Random Forest, SVM = support vector machine, UTI = urinary tract infection, VTE = venous thromboembolism, XGBoost = extreme gradient boosting, ASA = American Society of Anesthesiologist