Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery

Table 1 Baseline demographic and clinicopathological characteristics of patients, n (%)

Variables	Training set				Testing set
	Overall, n = 471	Yes, n = 70	No, n = 401	P value	Overall, n = 203	Yes, n = 26	No, n = 177	P value
Age [median (IQR)], yr	64.00 (63.00, 66.00)	69.00 (67.00, 71.00)	64.00 (62.00, 65.00)	< 0.001	64.00 (62.00, 65.50)	68.50 (65.25, 70.75)	63.00 (62.00, 65.00)	< 0.001
BMI [median (IQR)], kg/m2	23.00 (22.00, 24.00)	24.00 (23.00, 25.00)	23.00 (22.00, 24.00)	< 0.001	23.00 (22.00, 24.00)	25.00 (23.00, 26.00)	23.00 (22.00, 24.00)	< 0.001
Catheter [median (IQR)], d	8.00 (7.00, 10.00)	13.00 (10.00, 14.00)	8.00 (7.00, 9.00)	< 0.001	8.00 (7.00, 9.00)	13.00 (11.00, 13.00)	8.00 (6.00, 9.00)	< 0.001
Catheter intubation times
≥ 3	148 (31.4)	49 (70.0)	99 (24.7)	< 0.001	59 (29.1)	23 (88.5)	36 (20.3)	< 0.001
< 3	323 (68.6)	21 (30.0)	302 (75.3)		144 (70.9)	3 (11.5)	141 (79.7)
Operation time [median (IQR)], h	3.60 (2.90, 4.40)	3.80 (3.10, 4.50)	3.60 (2.80, 4.30)	0.061	3.70 (2.90, 4.60)	3.85 (2.93, 4.60)	3.70 (2.90, 4.50)	0.373
Blood loss [median (IQR)], mL	476.00 (434.50, 515.50)	627.00 (592.75, 658.75)	465.00 (429.00, 499.00)	< 0.001	470.00 (432.00, 504.00)	646.00 (616.25, 670.00)	461.00 (426.00, 494.00)	< 0.001
Hospitalization [median (IQR)], d	11.00 (9.00, 13.00)	15.00 (14.00, 17.00)	10.00 (8.00, 12.00)	< 0.001	10.00 (8.00, 12.00)	16.00 (15.00, 17.00)	10.00 (8.00, 12.00)	< 0.001
Diabetes
Yes	162 (34.4)	57 (81.4)	105 (26.2)	< 0.001	59 (29.1)	22 (84.6)	37 (20.9)	< 0.001
No	309 (65.6)	13 (18.6)	296 (73.8)		144 (70.9)	4 (15.4)	140 (79.1)
Hypertension
Yes	283 (60.1)	45 (64.3)	238 (59.4)	0.519	132 (65.0)	19 (73.1)	113 (63.8)	0.483
No	188 (39.9)	25 (35.7)	163 (40.6)		71 (35.0)	7 (26.9)	64 (36.2)
Antibiotics
Yes	295 (62.6)	47 (67.1)	248 (61.8)	0.477	128 (63.1)	16 (61.5)	112 (63.3)	1
No	176 (37.4)	23 (32.9)	153 (38.2)		75 (36.9)	10 (38.5)	65 (36.7)
Hypoproteinaemia
Yes	122 (25.9)	53 (75.7)	69 (17.2)	< 0.001	54 (26.6)	22 (84.6)	32 (18.1)	< 0.001
No	349 (74.1)	17 (24.3)	332 (82.8)		149 (73.4)	4 (15.4)	145 (81.9)
NACT
Yes	293 (62.2)	18 (25.7)	275 (68.6)	< 0.001	133 (65.5)	12 (46.2)	121 (68.4)	0.045
No	178 (37.8)	52 (74.3)	126 (31.4)		70 (34.5)	14 (53.8)	56 (31.6)

BMI: Body mass index; IQR: Interquartile range; NACT: Neoadjuvant chemotherapy.

Table 2 Receiver operating characteristic curve analyses for predicting urinary tract infection in each machine learning-based model

Model	Training set			Testing set
	AUC mean	AUC 95%CI	Variables1	AUC mean	AUC 95%CI	Variables1
RFC	0.925	0.868-0.982	7	0.918	0.861-0.975	7
SVM	0.787	0.730-0.844	7	0.779	0.722-0.836	7
DT	0.776	0.719-0.833	7	0.769	0.712-0.826	7
ANN	0.879	0.822-0.936	6	0.854	0.797-0.911	6
XGboost	0.797	0.740-0.854	7	0.788	0.731-0.845	7

¹Variables included in the model. ANN: Artificial neutral network; AUC: Area under the curve; CI: Confidence interval; DT: Decision tree; RFC: Random forest classifier; SVM: Support vector machine; XGboost: Extreme gradient boosting.