Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study

Table 1 Baseline characteristics of the training, testing, and external validation sets, n (%)

Variables	Training cohort, n = 1163	Testing cohort, n = 291	Validation cohort, n = 197	P value
Age, yr	57.60 ± 10.83	57.56 ± 11.23	59.72 ± 9.58	0.034
Male	694 (59.67)	163 (56.01)	106 (53.81)	0.206
BMI, kg/m2	22.79 ± 2.92	22.89 ± 2.75	22.61 ± 4.02	0.382
Neoadjuvant	67 (5.76)	19 (6.53)	9 (4.57)	0.659
Hypertension	254 (21.84)	66 (22.68)	43 (21.83)	0.952
Diabetes	83 (7.14)	26 (8.93)	9 (4.57)	0.185
Previous abdominal surgery	141 (12.12)	45 (15.46)	22 (11.17)	0.250
ASA				< 0.001
1	178 (15.31)	42 (14.43)	55 (27.92)
2	893 (76.78)	218 (74.91)	89 (45.12)
3	90 (7.74)	30 (10.31)	50 (25.38)
4	2 (0.17)	1 (0.34)	3 (1.52)
Anastomotic height, cm	4.82 ± 2.37	4.57 ± 2.14	4.77 ± 2.56	0.298
Specimen length, cm	10.99 ± 3.01	10.88 ± 3.11	15.21 ± 4.49	< 0.001
Diverting ileostomy	315 (27.09)	81 (27.84)	35 (17.77)	0.017
Tumor size, cm	3.60 ± 1.29	3.53 ± 1.25	3.89 ± 1.35	0.546
Stage				< 0.001
1	354 (30.44)	91 (31.27)	31 (15.74)
2	405 (34.82)	94 (32.30)	108 (54.82)
3	404 (34.74)	106 (36.43)	58 (29.44)
LARS				0.800
Minor/no	731 (62.85)	189 (64.95)	124 (62.94)
Major	432 (37.15)	102 (35.05)	73 (37.06)

ASA: American Society of Anesthesiologists classification; BMI: Body mass index; LARS: Low anterior resection syndrome.