Table 2. Area under the curve values in validation datasets for postoperative morbidity prediction.
| Surgery | Datasets | Phasea | Model typeb (clinical score) | AUCc | |||
|---|---|---|---|---|---|---|---|
| Training | Test | Category | Subtype | ||||
| Miscellaneous1 | |||||||
| Cevenini et al. (55) | CABG | 545 | 545 | Pre-, intra-, postoperative | Advanced | LR | 0.781 |
| BL | 0.778 | ||||||
| BQ | 0.785 | ||||||
| HS | 0.768 | ||||||
| DS | 0.779 | ||||||
| k-NN | 0.772 | ||||||
| ANN1 | 0.776 | ||||||
| ANN2 | 0.778 | ||||||
| Chong et al. (56) | CABG | N/A | Preoperative | Conventional | LR (QMMI score) | 0.752 | |
| 423 | 140 | Preoperative | Advanced | LR | 0.807 | ||
| ANN | 0.886 | ||||||
| Peng, Peng (14) | Mix | N/A | Preoperative | Conventional | LR (Parsonnet) | 0.727 | |
| 637 | 315 | Pre-, and postoperative | Advanced | LR | 0.789 | ||
| ANN | 0.852 | ||||||
| Secluded morbidities | |||||||
| Zhong et al. (18) | Mix | 5,475 | 1,369 | Septic shock | |||
| Pre-, intra-, postoperative | Advanced | LR | 0.93 | ||||
| RF | 0.81 | ||||||
| XGBoost | 0.96 | ||||||
| ANN | 0.88 | ||||||
| Thrombocytopenia | |||||||
| Pre-, intra-, postoperative | Advanced | LR | 0.87 | ||||
| RF | 0.89 | ||||||
| XGBoost | 0.89 | ||||||
| ANN | 0.83 | ||||||
| Liver dysfunction | |||||||
| Pre-, intra-, postoperative | Advanced | LR | 0.82 | ||||
| RF | 0.89 | ||||||
| XGBoost | 0.89 | ||||||
| ANN | 0.70 | ||||||
| Mufti et al. (57) | Mix | 4,476 | 1,117 | Agitated delirium | |||
| Pre-, intra-, postoperative | Advanced | LR | 0.814 | ||||
| RF | 0.813 | ||||||
| NB | 0.799 | ||||||
| BN | 0.774 | ||||||
| SVM | 0.811 | ||||||
| DT | 0.772 | ||||||
| ANN | 0.804 | ||||||
| Acute kidney injury | |||||||
| Lei et al. (58) | Aortic arch | 627 | 270 | Pre-, intra-, postoperative | Advanced | LR | 0.65 |
| RF | 0.71 | ||||||
| SVM | 0.64 | ||||||
| LGM | 0.80 | ||||||
| Tseng et al. (59) | Mix | 470 | 201 | Pre-, and intraoperative | Advanced | LR | 0.806 |
| RF | 0.839 | ||||||
| DT | 0.781 | ||||||
| XGboost | 0.837 | ||||||
| SVM | 0.825 | ||||||
| RF+XGBoost | 0.843 | ||||||
| Lee et al. (60) | Mix | 1,005 | 1,005 | Pre-, intra-, postoperative | Advanced | LR | 0.70 |
| RF | 0.68 | ||||||
| DT | 0.71 | ||||||
| XGBoost | 0.78 | ||||||
| SVM | 0.69 | ||||||
| NN classifier | 0.64 | ||||||
| Deep learning | 0.55 | ||||||
| Penny-Dimri et al. (61) | Mix | N/A | Preoperative | Conventional | LR (Cleveland Clinic) | 0.71 | |
| LR (Risk score) | 0.74 | ||||||
| LR (Risk score) | 0.75 | ||||||
| 77,322 | 19,331 | Preoperative | Advanced | LR | 0.76 | ||
| GBM | 0.76 | ||||||
| k-NN | 0.66 | ||||||
| ANN | 0.76 | ||||||
| Pre-, and intraoperative | Advanced | LR | 0.77 | ||||
| GBM | 0.78 | ||||||
| k-NN | 0.67 | ||||||
| ANN | 0.77 | ||||||
a, perioperative phase: pre-, intra, postoperative used variables in prediction models; b, distinction between conventional and advanced models is explained in the methods section; c, definitions of both the AUC and C-index is given in the methods section. 1, Mix of cardiovascular, respiratory, neurological, renal, infectious, and hemorrhagic complications. ANN (1, 2, etc.), artificial neural network (one-layer, two-layer, etc.). AUC, area under the receiving operating characteristics curve for the validation sets; BL, Bayes linear; BN, Bayesian network; BQ, Bayes quadratic; CABG, coronary artery bypass graft surgery; DS, direct score; DT, decision trees; GBM, gradient-boosted machine; HS, Higgins score; k-NN, k-nearest neighbor; LGM, light gradient machine; LR, logistic regression; Mix, various cardiac surgery patients with/without heart transplantation; NN, neural network; NB, Naïve Bayes; RF, random forest; SVM, support-vector machines; XGBoost, extreme gradient boosting.