Prediction models for acute kidney injury in patients with gastrointestinal cancers: a real-world study based on Bayesian networks.
BACKGROUND
This study attempts to establish a Bayesian networks (BNs) based model for inferring the risk of AKI in gastrointestinal cancer (GI) patients, and to compare its predictive capacity with other machine learning (ML) models.


METHODS
From 1 October 2014 to 30 September 2015, we recruited 6495 inpatients with GI cancers in a tertiary hospital in eastern China. Data on demographics, clinical and laboratory indicators were retrospectively extracted from the electronic medical record system. Predictors of AKI were selected in gLASSO regression, and further incorporated into BNs analysis.


RESULTS
The incidences of AKI in patients with esophagus, stomach, and intestine cancer were 20.5%, 13.9%, and 12.5%, respectively. Through gLASSO, 11 predictors were screened out, including diabetes, cancer category, anti-tumor treatment, ALT, serum creatinine, estimated glomerular filtration rate (eGFR), serum uric acid (SUA), hypoalbuminemia, anemia, abnormal sodium, and potassium. BNs model revealed that cancer category, treatment, eGFR, and hypoalbuminemia had direct connections with AKI. Diabetes and SUA were indirectly linked to AKI through eGFR, and anemia created connections with AKI through affecting album level. Compared with other ML models, BNs model maintained a higher AUC value in both the internal and external validation (AUC: 0.823/0.790).


CONCLUSION
BNs model not only delineates the qualitative and quantitative relationship between AKI and its associated factors but shows the more robust generalizability in AKI prediction.
['Li|Yang|Y|', 'Chen|Xiaohong|X|', 'Shen|Ziyan|Z|', 'Wang|Yimei|Y|', 'Hu|Jiachang|J|', 'Zhang|Yunlu|Y|', 'Xu|Jiarui|J|', 'Ding|Xiaoqiang|X|']
D058186:Acute Kidney Injury D000368:Aged D001499:Bayes Theorem D002681:China D005260:Female D005770:Gastrointestinal Neoplasms D006305:Health Status Indicators D006801:Humans D015994:Incidence D007297:Inpatients D008297:Male D008875:Middle Aged D011237:Predictive Value of Tests D012044:Regression Analysis D012189:Retrospective Studies D018570:Risk Assessment D012307:Risk Factors D062606:Tertiary Care Centers
acute kidney injury Gastrointestinal cancer Bayesian network machine learning Group LASSO disease prediction