From: Artificial intelligence-based evaluation of prognosis in cirrhosis
Biomarker | First author, references | Study type | Sample source | Detection method | Statistical methods | Target population |
---|---|---|---|---|---|---|
Plasma AT-III | Suda et al. [96] | A retrospective study | Blood | Blood test | ROC analysis | Cirrhosis with PVT |
Serum CysC and uNAG | Kim et al. [97] | Prospective observational studies | Blood and urine | Blood and urine tests | Multivariate analysis | Patients with AKI in decompensated cirrhosis |
Blood ammonia | Tranah et al. [98] | A retrospective study | Blood | Blood test | Random forest model | Complications of cirrhosis |
Blood chloride | Sumarsono et al. [99] | Retrospective cohort study | Blood | Blood test | Kaplan–Meier analysis and multivariate Cox proportional risk modeling | Decompensated cirrhosis |
t-Cort | Hartl et al. [101] | Prospective observational studies | Blood | Blood test | Multivariate Cox proportional risk model | Advanced CLD |
eAlb | Baldassarre et al. [100] | Observational studies | Blood | Blood test | Kaplan–Meier analysis and multivariate Cox proportional risk models | Decompensated cirrhosis |
uNGAL | Gambino et al. [102] | Prospective observational studies | Urine | ELISA | Competitive risk proportional risk Model | Patients with acute AKI in decompensated cirrhosis |
L-FABP (Urine) | Juanola et al [103] | Prospective cohort study | Blood and Urine | ELISA | Multivariate analysis | Decompensated cirrhosis |
PSP | Zanetto et al. [104] | Prospective study cohort study | Blood | ELISA | Multivariate Cox model | Acute decompensated cirrhosis |
Metabolite | Zhang et al. [105] | Prospective study cohort study | Blood | Liquid chromatography-ass spectrometry testing | Traditional statistics machine learning | ACLF |
12 proteins | Richards et al. [107] | Cohort study | Blood | High-throughput proteomics | Machine learning | Predicting response to HVPGs in cirrhotic patients with HCV |