Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning

Table 1 Comparison of baseline characteristics in training and test cohorts, n (%)

Characteristic	All (n = 579)	Training cohort (n = 405)	Testing cohort (n = 174)	P value
Female	207 (35.7)	139 (34.3)	68 (39.1)	0.27
Age (≥ 65 years)	190 (32.8)	132 (32.6)	58 (333.3)	0.86
BMI, kg/m2	23.1 (3.0)	23.0 (3.0)	23.1 (3.0)	0.80
Smoking	141 (24.4)	102 (25.2)	39 (22.4)	0.48
Alcohol use	86 (14.9)	58 (14.3)	28 (16.1)	0.49
Hypertension	154 (26.6)	105 (25.9)	49 (28.1)	0.58
Diabetes	72 (12.4)	49 (12.1)	23 (13.2)	0.71
Previous abdominal surgery	89 (15.3)	62 (15.3)	27 (15.5)	0.95
Tumorous obstruction	27 (4.7)	19 (4.7)	8 (4.6)	0.96
Neoadjuvant therapy	89 (15.3)	65 (16.1)	24 (13.8)	0.49
Electrolyte disorders	89 (15.4)	62 (15.3)	27 (15.5)	0.95
Albumin, g/L	40.1 (4.0)	40.1 (3.9)	40.1 (4.1)	0.97
CEA (> 5 ng/mL)	143 (24.7)	95 (23.5)	48 (27.6)	0.29
Tumor size, cm	3.4 (1.3)	3.4 (1.2)	3.5 (1.3)	0.76
Tumor distance, cm	6.4 (2.4)	6.4 (2.3)	6.5 (2.5)	0.57
Laparoscopic surgery	563 (97.2)	394 (97.2)	169 (97.1)	0.96
Operative time, minutes	219.1 (57.1)	220.4 (57.4)	215.9 (56.4)	0.39
Intraoperative bleeding, ml	75.8 (122.6)	79.1 (122.5)	68.3 (123.1)	0.34
ASA				0.97
I, II	507 (91.0)	354 (91.8)	153 (91.5)
III, IV	72 (9.0)	51 (8.1)	21 (8.5)
Clinical stages				0.38
1	158 (27.2)	113 (27.9)	45 (25.9)
2	189 (32.6)	1255 (30.8)	64 (36.8)
3	232 (40.1)	167 (41.2)	65 (37.3)
PSF	31 (5.3)	21 (5.1)	10 (5.7)	0.42

ASA: American Society of Anesthesiologists; BMI: Body mass index; PSH: Parastomal hernia; CEA: Carcinoembryonic antigen.

Table 2 Performance metrics for five different models in the training cohort

Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
RF	0.909 (0.844-0.974)	0.891	0.857	0.893	0.305	0.991
LR	0.876 (0.800-0.953)	0.785	0.809	0.783	0.170	0.986
KNN	0.884 (0.825-0.954)	0.920	0.714	0.932	0.365	0.983
XGBoost	0.879 (0.812-0.945)	0.829	0.761	0.833	0.200	0.984
SVM	0.870 (0.786-0.955)	0.859	0.857	0.859	0.250	0.990

SVM: Support vector machine; LR: Logistic regression; KNN: K-nearest neighbors; RF: Random forest; XGBoost: EXtreme gradient boosting; NPV: Negative predictive value; PPV: Positive predictive value; AUC: Area under the curve.