Machine learning approach to predict acute kidney injury after liver surgery

doi:10.12998/wjcc.v9.i36.11255

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World Journal of Clinical Cases

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2307-8960

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Clin Cases. Dec 26, 2021; 9(36): 11255-11264
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255

Table 1 Patient characteristics

Variables	Training set	Test set	P value
Patient population, n	1715	735
Age (yr)	55 (45-65)	54 (44-66)	0.323
Male, n (%)	1390 (81.0)	602 (81.9)	0.307
BMI (kg/m²)	24.6 (17.1-29.8)	24.9 (17.3-28.9)	0.956
Tumor size (cm)	4.5 (0.9-7.8)	4.8 (0.8-8.3)	0.283
AFP	8301 (489-35203)	8842 (503-43203)	0.058
WBC (× 10³/µL)	7.3 (3.5-13.8)	7.5 (3.3-15.8)	0.128
Hemoglobin (mg/dL)	13.0 (10.8-15.6)	12.7 (10.5-16.5)	0.460
PLT (× 10³/µL)	168 (102-245)	175 (113-260)	0.156
Creatinine (mg/dL)	0.92 (0.71-1.16)	0.90 (0.70-1.15)	0.128
ALB (g/dL)	3.8 (3.3-4.4)	3.7 (3.2-4.3)	0.603
AST (IU/L)	36.1 (6.3-163.5)	42.4 (5.8-173.4)	0.096
Diabetes mellitus, n (%)	109 (6.4)	81 (11.0)	0.098
Dyslipidemia, n (%)	395 (23.0)	191 (26.0)	0.063
ALT (IU/L)	39.8 (8.3-178.5)	42.3 (6.5-169.8)	0.132
Glucose (mg/dL)	11.8 (5.8-18.3)	12.5 (6.3-19.8)	0.285
Cholesterol (mg/dL)	162.2 (135.8-198.3)	168.0 (130.0-198.3)	0.323
PRBC (units)	0.5 (0.0-3.0)	0.8 (0.0-3.0)	0.112
Crystalloid (mL)	2318.8 (1500-3500)	2218 (1500-4000)	0.994
Surgery time (min)	278 (198-363)	285 (202-387)	0.856
Beta blockers, n (%)	257 (15.0)	67 (9.1)	0.155
Aspirin, n (%)	152 (8.9)	46 (6.3)	0.183
RAAS blocker, n (%)	91 (5.3)	61 (8.3)	0.360
Insulin, n (%)	48 (2.8)	44 (6.0)	0.059
Systolic blood pressure	113 (88-154.8)	118 (95-165.5)	0.658
Diastolic blood pressure	75 (55-84)	77 (58-89)	0.537
Mean arterial pressure	93 (71-119)	108 (68-121)	0.437

PLT: Platelet; AFP: Alpha-fetoprotein; WBC: White blood cell; BMI: Body mass index; ALB: Albumin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; PRBC: Packed red blood cell; RAAS: Renin-angiotensin-aldosterone system.

Table 2 Model performance (Concordance-index, Brier score, and area under the curve)

Machine learning models	Concordance-index	Brier score	AUC
Logistic regression	0.84	0.078	0.85
Support vector machine	0.86	0.083	0.90
Random forest	0.86	0.076	0.92
Extreme gradient boosting	0.80	0.083	0.87
Decision tree	0.83	0.085	0.90

AUC: Area under the curve.

Citation: Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264
URL: https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm
DOI: https://dx.doi.org/10.12998/wjcc.v9.i36.11255