Retrospective Study
Copyright ©The Author(s) 2022.
World J Clin Cases. Apr 26, 2022; 10(12): 3729-3738
Published online Apr 26, 2022. doi: 10.12998/wjcc.v10.i12.3729
Table 1 Patient characteristics
Variables
Training set
Test set
P value
Patient population, n473473
Age (yr)41 (13-64)43 (15-65)0.115
Male, n (%)274 (57.9)278 (58.8)0.258
BMI (kg/m2)25.3(16.9-32.8)25.9 (16.7-35.5)0.079
Systolic blood pressure119 (87-165)121(85-177)0.658
Smoking, n (%)142 (30.0)145 (30.7)0.583
Alcohol, n (%)163 (34.5)172 (36.4)0.158
Diabetes, n (%)34 (7.2)26 (5.5)0.098
Insulin, n (%)8 (1.7)4 (0.8)0.059
Hypertension, n (%)73 (15.4)80 (16.9)0.113
Preoperative chemotherapy, n (%)117 (24.7)122 (25.8)0.358
Preoperative radiotherapy, n (%)100 (21.1)82 (17.3)0.663
Obesity, n (%)112 (23.7)109 (23.0)0.487
WBC (× 103/µL)7.5 (3.2-14.3)7.2 (3.1-15.9)0.226
Hemoglobin (mg/dL)12.6 (9.8-16.6)12.9 (10.1-16.9)0.460
PLT (× 103/µL)156 (102-253)165 (113-267)0.115
Creatinine (mg/dL)0.89 (0.69-1.20)0.83 (0.65-1.15)0.328
Glucose (mg/dL)10.5(5.1-16.5)11.3 (4.4-18.8)0.085
Cholesterol (mg/dL)159.2 (137.3-195.3) 144.0 (127.4-199.8)0.075
Beta blockers, n (%)51 (10.8)55 (11.6)0.165
Aspirin, n (%)43 (9.1)47 (9.9)0.392
Flap ischemia time (min)123 (108-145)117 (101-153)0.558
Hypotensive events, n (%)11 (2.3)15 (3.2)0.663
Table 2 The model performance of the machine learning classifiers for predicting flap failure

Accuracy
Precision
Recall
F1 score
AUC
Random forest0.780.820.690.750.770
Support vector machine0.710.790.580.670.720
Gradient boosting0.680.760.530.650.707
Table 3 Multivariate logistic regression model for top 10 variables in random forest
Variables
Odds ratio (95%CI)
P value
Age 1.56 (0.57-5.87)0.04
Body mass index2.83 (0.68-5.54)0.02
Ischemia time1.98 (0.53-3.24)0.001
Smoking1.13 (0.28-2.89)0.87
Diabetes1.15 (0.53-3.28)0.06
Experience0.86 (0.18-4.87)0.79
Prior chemotherapy1.15 (0.56-2.68)0.07
Hypertension1.08 (0.25-2.64)0.28
Insulin1.27 (0.64-3.21)0.54
Obesity1.09 (0.57-2.95)0.13