Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study

Table 1 Baseline characteristics of patients with and without anastomotic leakage, n (%)

	NAL (n = 1732)	AL (n = 86)	P value
Sex = male	1055 (60.9)	64 (74.4)	0.016
Tobacco or alcohol			< 0.001
No	1256 (72.5)	46 (53.5)
Tobacco	258 (14.9)	18 (20.9)
Alcohol	25 (1.4)	1 (1.2)
All	193 (11.1)	21 (24.4)
T stage			< 0.001
T1	192 (11.1)	2 (2.3)
T2	453 (26.2)	17 (19.8)
T3	995 (57.4)	43 (50.0)
T4	92 (5.3)	24 (27.9)
N stage			0.025
N0	291 (16.8)	12 (14.0)
N1	1260 (72.7)	57 (66.3)
N2	181 (10.5)	17 (19.8)
Histological type			< 0.001
Adenocarcinoma	36 (2.1)	0 (0.0)
Adenosquamouscarcinoma	0 (0.0)	6 (7.0)
Others	1663 (96.0)	80 (93.0)
Multi	1 (0.1)	0 (0.0)
Neuroendocrine	17 (1.0)	0 (0.0)
Stromal	15 (0.9)	0 (0.0)
Family = yes	32 (1.8)	10 (11.6)	< 0.001
CD34 = yes	647 (37.4)	67 (77.9)	< 0.001
FOBT = M1	1276 (73.7)	64 (74.4)	0.978
Location = others	76 (4.4)	2 (2.3)	0.517
Age = high	871 (50.3)	39 (45.3)	0.433
Neoadjuvant = yes	142 (8.2)	57 (66.3)	< 0.001

NAL: Non-anastomotic leakage; AL: Anastomotic leakage.