Machine learning approach to predict acute kidney injury after liver surgery

doi:10.12998/wjcc.v9.i36.11255

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 9, Issue 36

This Article

Academic Content and Language Evaluation of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (3703)

All Articles published online

The chart showing PDF series, WORD series, HTML series, Figures (1-4) series, Tables (1-2) series.

Item

Count

PDF

290

WORD

149

HTML

1816

Figures (1-4)

140

Tables (1-2)

169

Sum=2564

Publishing Process of This Article

The chart showing Browse series, Download series.

Item

Count

Browse

310

Download

829

Sum=1139

Dec 26, 2021 (publication date) through May 4, 2024

Times Cited of This Article

Times Cited (4)

Journal Information of This Article

Publication Name

World Journal of Clinical Cases

ISSN

2307-8960

Publisher of This Article

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

Retrospective Study

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