Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer

Table 1 Baseline characteristics and univariate analysis of patients (abridged version)

	All (N = 455)	No (n = 386)	Yes (n = 69)	Univariate analysis		N
				OR	P value
Age, year	61.0 (52.0-68.0)	61.0 (52.2-67.0)	62.0 (51.0-68.0)	1.0 (0.98-1.02)	0.809	455
Sex					0.045a	455
Female	113 (24.8)	103 (26.7)	10 (14.5)	Reference
Male	342 (75.2)	283 (73.3)	59 (85.5)	2.12 (1.09-4.57)
Smoking					< 0.001a	455
No	230 (50.5)	209 (54.1)	21 (30.4)	Reference
Yes	225 (49.5)	177 (45.9)	48 (69.6)	2.68 (1.56-4.75)
ASA status					< 0.001a	455
L1	54 (11.9)	48 (12.4)	6 (8.70)	Reference
L2	314 (69.0)	279 (72.3)	35 (50.7)	0.98 (0.42-2.75)
L3	87 (19.1)	59 (15.3)	28 (40.6)	3.70 (1.49-10.7)
Neoadjuvant therapy					0.008a	455
Chemotherapy	344 (75.6)	300 (77.7)	44 (63.8)	Reference
Radiochemotherapy	60 (13.2)	43 (11.1)	17 (24.6)	2.70 (1.38-5.10)
Combined immuno/targeted therapy	51 (11.2)	43 (11.1)	8 (11.6)	1.28 (0.53-2.80)
NRS-2002 ≥ 3					< 0.001a	455
No	294 (64.6)	265 (68.7)	29 (42.0)	Reference
Yes	161 (35.4)	121 (31.3)	40 (58.0)	3.01 (1.78-5.13)
Surgical approach					< 0.001a	455
Distal-Billroth 1	155 (34.1)	146 (37.8)	9 (13.0)	Reference
Distal-Billroth 2	6 (1.32)	4 (1.04)	2 (2.90)	8.11 (0.9-50.9)
Distal-Roux-en-Y	68 (14.9)	50 (13.0)	18 (26.1)	5.74 (2.46-14.3)
Proximal	8 (1.76)	7 (1.81)	1 (1.45)	2.54 (0.09-17.4)
Total-Roux-en-Y	218 (47.9)	179 (46.4)	39 (56.5)	3.48 (1.70-7.93)
Implantation of nutrition tubes					0.025a	455
No	237 (52.1)	192 (49.7)	45 (65.2)	Reference
Yes	218 (47.9)	194 (50.3)	24 (34.8)	0.53 (0.31-0.90)
Operating time > 4 h					< 0.001a	455
No	254 (55.8)	231 (59.8)	23 (33.3)	Reference
Yes	201 (44.2)	155 (40.2)	46 (66.7)	2.96 (1.74-5.17)
Blood loss > 100 mL					< 0.001a	455
No	321 (70.5)	289 (74.9)	32 (46.4)	Reference
Yes	134 (29.5)	97 (25.1)	37 (53.6)	3.43 (2.03-5.85)

^aP < 0.05.

Data are presented as n (%). The table above lists the patient demographics and all variables that were significant in the univariate analysis. The complete baseline characteristics are available in Supplementary Table 5. OR: Odds ratio; ASA: American Society of Anesthesiologists; NRS-2002: Nutritional Risk Screening-2002.

Table 2 Performance metrics of the models

Model	AUC	Accuracy	Recall	Sensitivity	Specificity
NNE	0.789 (0.739-0.840)	0.732 (0.657-0.807)	0.731 (0.610-0.851)	0.731 (0.610-0.851)	0.733 (0.625-0.841)
Logistic regression	0.786 (0.606-0.966)	0.752 (0.697-0.807)	0.763 (0.525-0.824)	0.763 (0.525-0.824)	0.756 (0.704-0.807)
GLMnet	0.776 (0.668-0.884	0.67 (0.555-0.786)	0.739 (0.570-0.908)	0.739 (0.570-0.908)	0.656 (0.508-0.803)
Random forest	0.754 (0.648-0.861)	0.666 (0.641-0.691)	0.735 (0.505-0.965)	0.735 (0.505-0.965)	0.659 (0.598-0.720)
XGBoost	0.703 (0.571-0.835)	0.679 (0.567-0.791)	0.606 (0.400-0.813)	0.606 (0.400-0.813)	0.693 (0.577-0.810)
Decision tree	0.602 (0.544-0.660)	0.756 (0.670-0.842)	0.371 (0.259-0.482)	0.371 (0.259-0.482)	0.827 (0.725-0.928)

NNE: Neural network ensemble; AUC: Area under the curve.